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DIBASHI Azuka Anthony PG/Ph.D/10/54604
CAPACITY PLANNING AND PERFORMANCE IN THE
NIGERIAN BREWING INDUSTRY IN SOUTHEASTERN
NIGERIA
FACULTY OF BUSINESS ADMINISTRATION
DEPARTMENT OF MANAGEMENT
Ebere Omeje Digitally Signed by
Name
DN
O= University of Nigeri
OU = Innovation Centre
DIBASHI Azuka Anthony PG/Ph.D/10/54604
CAPACITY PLANNING AND PERFORMANCE IN THE
NIGERIAN BREWING INDUSTRY IN SOUTHEASTERN
NIGERIA
OF BUSINESS ADMINISTRATION
DEPARTMENT OF MANAGEMENT
Digitally Signed by: Content manager’s
Name
DN : CN = Webmaster’s name
O= University of Nigeria, Nsukka
OU = Innovation Centre
2
CAPACITY PLANNING AND PERFORMANCE IN THE NIGERIAN
BREWING INDUSTRY IN SOUTHEASTERN NIGERIA
DIBASHI Azuka Anthony PG/Ph.D/10/54604
DEPARTMENT OF MANAGEMENT, FACULTY OF BUSINESS ADMINISTRATION,
UNIVERSITY OF NIGERIA, ENUGU CAMPUS
ENUGU
JULY, 2014
3
CAPACITY PLANNING AND PERFORMANCE IN THE NIGERIAN BREWING INDUSTRY IN SOUTHEASTERN NIGERIA
DIBASHI Azuka Anthony PG/Ph.D/10/54604
SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR
THE AWARD OF DOCTOR OF PHILOSOPHY (Ph.D) IN MANAGEM ENT
DEPARTMENT OF MANAGEMENT,
FACULTY OF BUSINESS ADMINISTRATION,
UNIVERSITY OF NIGERIA, ENUGU CAMPUS
ENUGU.
SUPERVISOR: PROF. U.J.F. EWURUM
JULY, 2014
4
DECLARATION
I, DIBASHI Azuka Anthony, hereby declare that I have satisfactorily completed the requirement
for Ph.D. thesis. That the work embodied in this thesis is original and has not been submitted in
part or full for any other Diploma or Degree of this University or any other University.
…………………….……. DIBASHI Azuka Anthony
(PG/Ph.D/10/54604)
5
APPROVAL
We the undersigned certified that this thesis is adequate in scope and quality for the award of
Ph.D in Management.
___________________ _____________ PROF. U.J.F. EWURUM DATE Supervisor ____________________ _______________ DR. V.A. ONODUGO DATE Head of Department
6
DEDICATION
This study is dedicated to the Almighty God, whose inspiration guided the work.
7
ACKNOWLEDGEMENTS
I remain forever grateful to the Almighty God for leading me to the successful completion of this
work. I wish to express my gratitude to Prof. U.J.F. Ewurum my supervisor for his guidance,
unrelenting advice, encouragement, constructive criticism, and valuable suggestions, without
which this work would have been an uphill and more demanding task. His combined wealth of
experience, foresightedness, magnanimity, generosity, academic prowess and moral probity,
which he always displayed, merit commendation. I also appreciate the Head of Department
(Department of Management) Dr. V.A. Onodugo for his patience, suggestions and fatherly
qualities.
I must also extend my heartfelt and warm gratitude to our dignified, amiable, and pragmatic
Dean of the Faculty, Prof G. Ugwuonah for her exemplary and charismatic leadership. I am also
grateful to Dr. E.K. Agbaeze, Dr. O.C. Ugbam, Dr. (Mrs.) Ann Ogbo (LSM), Rev. Fr. Dr. Tony
Igwe, Dr. Ben Chukwu and a host of other academic staff of the Department who has been
exceptionally wonderful; To Mrs. N. Ofodile and Mrs. Ijeomanta. I am highly indepted, may
God Almighty highly bless and reward you all.
I also remain loyal to Senator Dr. (Chief) Ify Okowa, senator representing Delta North senatorial
zone at the National Assembly Abuja for all his support, advice and encouragement. I am also
grateful to my wife Mrs. Josephine Dibashi (LSM) and my sons Azuka, Anene, Chude, and
Joseph Dibashi for their love and understanding and being there for me.
I am also grateful to Miss Augusta Adaghegbe, my driver Osakwe .O. John, Mrs. Peace Ottah
and Mrs Ngozi Ofodile for making this work a success. I am grateful to the Management and
Staff of Guinness Nigeria Plc, Benin City, Nigerian Breweries Plc, Ninth Mile Enugu,
Continental Breweries Plc, Awoomama and Premier Breweries Plc, Onitsha for their support and
Cooperation during the field work.
8
TABLE OF CONTENTS
Pages
Declaration………………………………………………………………………………… iii
Approval……………………………………………………..…….………………… iv
Dedication………………………………………………………………………………… v
Acknowledgements…………………………………………..…………………………. vi
Table of Contents………………………………………………..…………….………… vii
List of Tables………………………………………………………….….…………….. x
List of Figures…………………………………………………………………………… xi
Abstract…………………………………………………………………………………. xii
CHAPTER ONE:
1.1 Background of the Study………………………………………………………… 1
1.2 Statement of the Problem…………………………………………………… 5
1.3 Objectives of the Study……………………………………………………….. 5
1.4 Research Questions……………………………………………………………. 6
1.5 Research Hypotheses…………………………..……………………………….. 6
1.6 Significance of the Study………………………………………………………. 7
1.7 Scope of the Study…………………………………………………………….. 7
1.8 Limitations of the Study……………………………………………………… 8
1.9 Profiles of the Brewing Firms studied……………………….………...……. 8
1.10 Operational Definition of Terms………………………………………………….. 13
References……………………………………………………………………… 15
CHAPTER TWO: LITERATURE REVIEW
2.1 Literature……………………………………………………………………… 16
2.2 Conceptual Framework……………………………………………………….. 17
2.3 Theoretical Framework……………………………………………………….. 37
2.4 Empirical Review……………………………………………………………… 67
2.5 Capacity Management and Planning………………………………………….. 69
2.6 Brewing………………………………………………………………………… 74
2.7 Summary of the Review of the Related Literature………………………………. 90
References…………………………………………………………………………. 93
9
CHAPTER THREE: METHODOLOGY
3.1 Research Methods………………………………………………………………… 105
3.2 Research Design……………………………………………………………………105
3.3 Sources of Data Collection……………………………………………………. 105
3.4 Population of the Study………………………………………………………. 106
3.5 The Sample and Sampling Technique…………………………………………… 106
3.6 Description of Research Instruments……………………………………………… 107
3.7 Data Analysis Technique(s) ……………………………………………………… 107
3.8 Validity of Instrument…………………………………………………………… 109
3.9 Reliability of the Research Instrument……………………………………………. 109
References……………………………………………………………………… 110
CHAPTER FOUR: DATA PRESENTATION AND ANALYSIS
4.1 Data presentation and analysis……………………………………………. 111
4.2 Data Presentation………………………………………………………………... 111
4.3 Data Analysis………………………………………………………………....... 114
4.4 Analysis of the Relationship of the Contingency Theory and the Five Objectives 126
4.5 Discussion of Findings……………………………………………………….. 139
4.6 Discussion related to the Contingency Theory and Multi Period Capacity Problem 150
References………………………………………………………………........... 161
CHAPTER FIVE: SUMMARY OF FINDINGS, CONCLUSION,
RECOMMENDATIONS, CONTRIBUTION TO KNOWLEDGE AND
SUGGESTIONS FOR FUTURE RESEARCH
5.1 Summary of Major Findings………………………………………………….. 167
5.2 Conclusion………………………………………………………………......... 167
5.3 Recommendations………………………………………………………………. 169
5.4 Contribution to Knowledge…………………………………………………….. 169
5.5 Suggestions for Future Research……………………………………………… 170
References………………………………………………………………............. 171
Bibliography………………………………………………………………....... 172
Appendix I(Questionnaire) ……………………….…………………………… 186
Appendix II(Oral Interview Schedule) ………………..………………………… 191
10
Appendix III (Dichotomous Oral Interview Schedule on the Contingency Theory of
Capacity Planning)……………………………………………………………….. 192
Appendix IV (Dichotomous Oral Interview Schedule for Implementing the Capacity
Multi-Period Problem…………………………………………………………… 193
Appendix V (Calculation of Cronbach’s Alpha Co-efficient of Reliability)……. 194
Appendix VI (Results related to the Personal Data of the Respondents)………… 195
Appendix VII (Multi-Period Capacity Planning Problem)……………………….. 199
11
LIST OF TABLES
PAGE
Table 4.1: The Presentation of the Response rate of the questionnaires administered……. 111
Table 4.2: The Summary of the Personal Data of the 740 Respondents…………………. 112
Table 4.3: The Presentation of the Responses on their Statuses and their experiential years 113
Table 4.4: The Analysis of the Responses related to the Five Objectives………………… 114
Table 4.5: The Analysis of the 12 steps towards developing a Capacity Plan ………..….. 116
Table 4.6: The Analysis of the Responses Opposite in Meaning to the Objectives……… 117
Table 4.7: The Analysis of the other Responses related to the First Four Objectives……. 118
Table 4.8: The Computation Details of the First Hypothesis………………………..…….. 122
Table 4.9: The Computation Details of the Second Hypothesis…………………………. 123
Table 4.10: The Computation Details of the Third Hypothesis…………………………… 124
Table 4.11: The Computation Details of the Fourth Hypothesis………………………….. 125
Table 4.12: The Computation Details of the Fifth Hypothesis…………………………….. 126
Table 4.13: The Analysis of the Responses to the Dichotomous Oral Interview Questions….. 127
Table 4.14: The Analysis of the Responses on the Relationship between the Multi-Period
Capacity Problem and the Five Objectives……………………………………. 129
Table 4.15: The Analysis of the Data on how Indigenous Capacity Building Theory
relates to the Five Objectives……………………………………………….. 131
12
LIST OF FIGURE
PAGE
Figure 2.1: Systems Theory of Performance……………………………………………… 59
13
ABSTRACT
The study investigated the influence of capacity planning on the performance of brewery firms in South Eastern States of Nigeria. The specific objectives of the study were to determine the extent to which capacity planning enhanced the level of performance in the brewing industry in South Eastern Nigeria. The study examined the nature of the relationship between capacity requirement planning and materials requirements planning, to ascertain the extent to which capacity planning sustains organizations competitive advantage, thereby determining the relationship between capacity planning and capacity building, determining the steps toward developing a capacity planning that affects the profitability in the brewing firms in the area studied. The research design adopted in the study was a combination of the survey, oral interview and model modifications. Hypothesis 1,3 and 5 were tested using Z test of population proportions and 2 and 4 using Spearman’s Rank Correlations revealed that capacity planning to a large extent enhanced the performance in the brewing industry in Southeastern Nigeria, that there was a significant capacity planning to a large extent (p< 0.05) enhanced the performance in the brewery industry in the area studied. Capacity requirement had positive relationship (p< 0.05)with materials requirements planning. Capacity planning to large extent (p< 0.05) sustained the organizations competitive position. There was significant positive relationship (p< 0.05) between capacity planning and capacity building. The steps of the capacity plan positively (p< 0.05) affected profitability. Positive relationship between capacity requirements planning and materials requirements planning, that capacity planning to a large extent sustained the organizational competitive advantage, that there is a positive relationship between capacity planning and capacity building, that the 12 steps of the capacity plans were developed that positively affected the profitability in the brewing industry in the area studied. In conclusion, the finding that capacity planning enhanced the performance in the brewing industry in Southeastern Nigeria implied that it made the brewing companies studied to achieve their organizational goals and objectives. The finding that there was a significant positive relationship between capacity requirements planning and materials requirements planning implied that there was a positive correlation between them. This means that materials requirements planning which was a method of coordinating the detailed production plans could lead to an enhancement of capacity requirements planning which meant taking future decisions on the items needed for the production capability of the brewing facility.
14
CAPACITY PLANNING AND PERFORMANCE IN THE NIGERIAN
BREWING INDUSTRY IN SOUTHEASTERN NIGERIA
DIBASHI Azuka Anthony PG/Ph.D/10/54604
DEPARTMENT OF MANAGEMENT, FACULTY OF BUSINESS ADMINISTRATION,
UNIVERSITY OF NIGERIA, ENUGU CAMPUS
ENUGU
JULY, 2014
15
CHAPTER ONE
1.1 BACKGROUND TO THE STUDY
Capacity Planning has enhanced the performance of the brewing industry in Nigeria right from
1946 when Nigerian Breweries Limited set up the First Brewery in Nigeria (Nigerian Breweries
PLC, 2011). The direction has been to increase the number of breweries. Guinness Nigeria Plc
set up a Brewery in Lagos in 1963. The magnitude is now five breweries located at Ikeja, Ogba,
Benin, Jos and Aba (Guinness Nigeria Plc; 2011). Capacity has continued to be the production
capability of a facility in terms of the inputs, throughput and outputs.
In 1989, the Federal Government policy of using local inputs such as sorghum and corn instead
of malled barley negatively affected a lot of the breweries. Both Nigerian Breweries Plc and
Guinness Nigeria Plc depended on the assistance of the Parent Companies. The Brewing Industry
in Nigeria have relied on capacity planning for meeting the increased demand for beer, stout and
malt products through Demand Forecasting and Capacity Requirements Planning (Guinness
Nigeria Plc, 2011; Nigerian Breweries Plc, 2011).
Capacity building has followed capacity planning in the creation of the enabling environment
with appropriate policy and legal frameworks, institutional development including community
development (of women in particular). Human Resource Development and strengthening of
managerial systems, adding that, UNDP recognizes that capacity building is a long-term,
continuing process, in which all stakeholders participate (ministries, local authorities, non-
governmental organizations and water user groups, professional associations, academics and
other (citation: UNDP). Capacity building is very necessary for capacity planning. Planning is
deciding in advance what is to be done, when, where, how and whom it is to be done. In that it
bridges the gap from where we are to where we want to go in any business building and
performance. It is continuous, periodic managerial activities and reduces uncertainty. Capacity
is the production capability of a facility and it is measured in terms of inputs, throughput and
outputs. Manufacturing is that aspect of industry in which products, waste products and services
are produced (UNDP, 2012).
By 1992, capacity building became a central concept in Agenda 21 and in other United Nations
Conference on Environmental and Development (UNCED) Agreements. By 1998, the UN
General Assembly had commissioned and received evaluations of the impact of the UN system’s
16
support for capacity building. These evaluations were carried out by the UN Department of
Economic and Social Affairs as part of the United Nations GATT Agreement’s (UNGA’)
triennial policy review during which it looks at all UN system development activities (UN
Publications Section). Since then, the issue of capacity building has become a major priority
within the global conventions, the Global Environmental Facility (GEF) and the International
Communities.
In the year 2000, UNDP through its Strategic Partnership with the GEF Secretariat, launched the
Capacity Development Initiative (CDI), a consultative process involving extensive outreach and
dialogue to identify countries’ priorities issues in capacity development needs, and based on
these findings, to develop a strategy and action plan that addresses identified needs to meet the
challenges of global environmental action.
In 2002, the World Summit in Sustainable Development (WSSD) and the Second GEF Assembly
reaffirmed the priority of building the capacity of development countries. The WSSD
recommended that GEF resources be used to provide financial resources to developing countries
to meet their capacity needs for training, technical knowhow and strengthening national
institutions.
Capacity Building is, however, not limited to international aid work. More recently, the term is
being used by governments to transform community and industry approaches to social and
environmental problems.
According to Skinner (1985), there are five periods of industrial history that stand out in
the development of manufacturing management:
1780 – 1950 Manufacturing leaders as technology capitalists.
1850 – 1890 Manufacturing leaders as architects of mass production.
1890 – 1920 Manufacturing management as movers in the organization.
1920 – 1960 Manufacturing management refines its skills in controlling and stabilizing.
1960 – 1980 Shaking the foundations of industrial management.
During the early years of the indusial revolution, production began to shift from low volume
activity to larger-scale operations. Although the scale of these early operations was large, the
machinery was not particularly complex and production operations were rigid. The management
of these operations remained essentially in the hands of top management with the aid of
overseers. Working conditions during this period were often abysmal.
17
The major thrust of the Industrial Revolution took place in the (second 40-year) period from
1850 – 1890. During this period, the concepts of mass production and the assembly line were
born. Since coal could be efficiently transported, plants could be located in a larger variety of
locations. The plant foreman had enormous power and influence during this period.
According to Skinner(1985), the job of production manager actually came into being in the
period 1890 – 1920. Manufacturing processes became too complex to be handled by top
management personnel only. With this complexity came the need for scientific management
techniques. Frederick Taylor (often called the father of industrial engineering) is generally
credited with being the originator of the concept of scientific management. Most of the scientific
management techniques introduced around the turn of the century involved merely breaking a
task down into its various components. These techniques are probably less scientific than just
orderly. With the new levels of complexity, the single plant foreman could no longer coordinate
the demands of producing a varied product line and changing production schedules.
The enormous worldwide depression that took place n the 1930s notwithstanding, in many ways
the period 1920 – 1960 can be considered a golden age for the development of industry in the
United States. By 1960, the United States was the preeminent economic power in the world.
With the growth of the labour movement, working conditions had improved enormously. True
scientific methods started finding their way into the factory. Mathematical models for learning,
inventory control, quality control, production scheduling, and project management gained
acceptance by the user community. Top management often came through the ranks of production
professionals during this period.
Since 1960, many American companies have relinquished their domination of certain markets.
Products that were traditionally produced in the Untied States are now imported from Germany,
Japan, and the Far East. Many products are produced more cheaply and with higher quality
overseas. Furthermore, management-employee relations are often better in foreign companies.
Quality circles, introduced in Japan, allowed employees to input opinions about product
development and production procedures. Far more sophisticated scientific production methods
have been adopted in Japan than in other countries. For example, there are many more robots and
modern flexible manufacturing systems in Japan than in the United States (Skinner, 1985).
18
Over the past five years, a broad conceptual framework has emerged. This approach is
increasingly being adopted by the development cooperation community. It involves a System
Perspective that addresses various levels of environmental management capacities (i.e. capacities
of institutions, individuals, overall countries and regions) (Vallejo, 2006). This approach lays
greater emphasis on the Capacity Development Process itself, on local ownership of its process
and on equal partnership in its support (Lafontaine, 200).Capacity Building involves human
resource development, the development of organizations and promoting the emergence of an
overall policy environment, conducive to the generation of appropriate responses to emerging
needs (UNDP/UNDOALOS, 1994).
The concept of capacity building includes the following issues.
Human resource development, the process of equipping individuals with the understanding,
skills and access to information, knowledge and training that enables them to perform
effectively.Organizational development, the elaboration of management structures, processes and
procedures, not only within organizations but also the management of relationships between the
different organizations and sectors (public, private and community).Institutional and legal
framework development, making legal and regulatory changes to enable organizations,
institutions and agencies at all levels and in all sectors to enhance their capacities.The levels of
capacity building are that:The Individual: refers to the process of changing attitudes and
behaviours-imparting knowledge and developing skills while maximizing the benefits of
participation, knowledge exchange and ownership.The Institution: focuses on the overall
organizational performance and functioning capabilities, as well as the ability of an organization
to adapt to change.
The System: emphasizes the overall policy framework in which individuals and organizations
operate and interact with the external environment (Lafontaine, 2000).
1.2 STATEMENT OF THE PROBLEM
There is difficulty in determining the extent to which capacity planning enhanced performance in
the brewing firms in Southeastern Nigeria from the inception of brewing industry in Nigeria in
1946 to date. Capacity has consistently and continuously determined operational capabilities of
decision, focasting changes in demand attitudes, skill and aids proximity to market future time
services needed and work load leading to various shortage or lack of stock in production process
which involves complex measures in terms of input, through put and output. This problem leads
to other challenges in ascertaining the relationship between capacity requirementsplanning and
material requirements planning and the extent to which capacity planning sustains organizations’
competitive advantage.
19
Determining the extent of the relationship between capacity planning and capacity building and
the steps towards developing a capacity plan to improve the profitability in the brewing sector
have also produced several challenges and these lead to lack of gateways. It is these challenges
that are to be addressed in this study.
The capacity planning problem of a brewing firm would make it have less output in the form of
Lager beer, Stout and malt than is demanded by the present and potential customers. One of the
numerous ways of solving this problem is to build a new brewery firm. This is a long term
decision that will raise new issues of plant location, plant layout, selection and design of the
product, selection of equipments and processes, production design of items processed, and job
design. If these issues are not properly handled, performance will be negatively affected. This is
why the topic on capacity planning and performance in the Nigerian brewing industry in the
Southeastern States of Nigeria is apt.
1.3 OBJECTIVES OF THE STUDY
The thrust of the study is the effect of capacity planning on performance in the Nigerian brewing
industry in Southeastern Nigeria.
The specific objectives of the study are as follows:
1) To evaluate capacity planningand performance in thebrewing industry in Southeastern
Nigeria.
2) To assess capacity requirements planning and materials requirements planning.
3) To ascertain the extent to which capacity planning sustains organisations’ competitive
advantage.
4) To evaluate the relationship between capacity planning and capacity building.
5) To assess the steps toward developing a capacity plan and the profitability in the brewing
firms in the area studied.
1.4 RESEARCH QUESTIONS
This research is designed to provide answers to the following questions:
i. To what extent does capacity planning enhances performance in the brewing industry in
South Eastern Nigeria?
ii. What is the nature of the relationship between capacity requirements planning and
material requirements planning?
20
iii. What is the extent to which capacity planning sustains organisations’ competitive
advantage?
iv. What is the extent of the relationship between capacity planning and capacity building?
v. How do we assess the steps that could be used to develop capacity planning that would
affect profitability in the brewing industry in the area to be studied?
1.5 RESEARCH HYPOTHESES
Five research hypotheses have been formulated to guide the study. They are as follows:
(i): Capacity planning to a large extent does not enhances performance in the brewing
industry in South Eastern Nigeria.
(ii): There is no significant relationship between capacity requirements planning and material
requirements planning.
(iii): Capacity planning to a large extent does not sustain organizations’ competitive
advantage.
(iv): There is no positive significant relationship between capacity planning and capacity
building.
(v): The steps towards developing capacity plan that would not affect profitability in the
brewing industry in South Eastern Nigeria are of the same order of magnitude.
1.6 SIGNIFICANCE OF THE STUDY
This study will be of immence significance to the Shareholders, Board members, Manager and
Stakeholders of brewing firms. It will also benefit officers of government, the public at large and
future researchers in the following ways: Shareholders, Members, Managers, Stakeholder,
Officers of Government,The public and Researchers.
1.7 SCOPE AND DELIMITATION OF THE STUDY
The focus of the study is to determine the extent to which capacity planning enhances the
performance in the brewing industry in the South Eastern Nigeria. The brewing companies were
chosen across the major zones in south eastern Nigeria in terms of subject matter, methodology,
spatial and data that best fits the study.
The geographical scope is South Eastern Nigeria, and the time scope of the study is 2 years from
December 2010 to December 2012. The brewing firms studied are Nigeria Breweries Plc, Ninth
21
Mile Enugu, Guinness Nigeria Plc, Aba (former Dubic Breweries Plc), Premier Breweries Plc
Onitsha and Continental Breweries Plc Awoomama.
1.8 LIMITATIONS OF THE STUDY
Attitude of Respondents: Privacy of information and attitude of respondents were alo being
constraints. Some of the respondents were reluctant at releasing the required information as a
result of prejudiced opinion conceived by the study.
The Survey Research Design: It had the limitation that some respondents were not willing to
give answers to the probes. This limitation is minimized by persuading the respondents to give
answers.
The oral interview: Ithad the limitation that the interviewing situation may change from one
situation to another especially if more than one field data collector is used. This limitation is
minimized by the Researcher doing most of the field work.
The Questionnaire Research instrument: It had the limitation that its structured nature
compelled some of the respondents to give answers that they do not fully endorse. This limitation
was minimized by also asking some open-ended questions in an oral interview schedule.
The oral interviewschedule: It had the limitation that the open-ended questions asked were
difficult to analyse. This limitation was minimized by also using relative frequencies as the
numbers given over the total number of research instruments returned.
1.9 PROFILES OF THE BREWING FIRMS STUDIED
Historical Development of Nigerian Breweries
Nigerian Breweries Plc (NBPLC) is the country’s pioneer factory. Incorporated in 1946, it
commenced production in 1949. It started as a joint venture between the United African
Company (UAC) International, UK and Heineken of Holland, Thus, at inception, it was 100 per
cent foreign owned. By the early 1950s, when it began operating fully, some indigenous traders
already involved with its products were invited to become shareholders. Under the indigenization
policy of the early 1970s the foreign shareholders were forced to sell a significant proportion of
their holdings. Today, the company is 60 per cent Nigerian owned and 40 per cent foreign
owned. The 60 per cent Nigerian stake is held by company employees and members of the
public, while the 40 per cent foreign ownership is split almost equally between CWA Holdings
Limited (for Unilever) and Heineken Brouwerijen BV (Nigerian Breweries Plc, 2011).
22
The foreign partners now perform the role of technical advisers, with Unilever advising on
commercial aspects such as accounting, purchasing, marketing and personnel, while Heineken
does the same for technology. Organizationally, the company has four divisions: technical,
finance, marketing and personnel, each of which is headed by an executive director (Nigerian
Breweries Plc, 2011).
Performance and Development
At its inception in 1949, NBPLC had only Start Lager (Nigeria’s first) on the market, over the
years it has broadened its product range. Except for the period 1984-86, when sales volume
suffered an annual average decline of about 18 per cent, turnover growth in the company has
generally been accompanied by growth in profit and production volume. Thus, when normal
growth was restored in 1987, the 51 per cent and 83 per cent increases in turnover and operating
profit, respectively, for 1987 – 88 were accompanied by about 35 per cent volume growth.
Similarly, the turnover of about N1.7 billion recorded in 1991 was partially the result of 8 per
cent growth in sales volume. However, from all indications, product pricing has been the major
factor in the impressive growth in operating profits (Nigerian Breweries Plc, 2011).
The Table 1.1 presents indicators of the growth turned in the company. Apart from sales and
profit, both net total assets and the numbers of employees have enjoyed respectable growth.
23
Table 1:1 Performance Indicators for NBPLC
1971 1975 1981 1985 1991
Turnover (Nm) 40.2 75.7 241.1 179.1 1,708.6
Pretax operating profit
(Nm)
6.1 12.4 38.5 41.6 422.5
Net assets (Nm) 11.9 29.1 103.6 161.9 1,248.5
Employees 1,270.0 2,243.0 n.a 3,998.0 4,297.0
Source: Nigerian Breweries Plc (2014). Annual Report and Statement of Account. Lagos:
Nigerian Breweries Limited.
The deteriorating results recorded by the company in 1984-86 reflected the foreign exchange
rationing policy of the period, which was necessitated by the severe balance of payments crisis of
the post-oil-boom era. The import licence allocation of the company could hardly satisfy one
third of its foreign exchange requirements. The government’s mandatory backward integration
policy in the mid-1980s saw the company establishing a 5,000 – hectare far, estimated to be
worth N30 million, in Niger State. The farm is highly mechanized and produces mainly maize,
rice and sorghum, with Soya beans and cowpeas as rotational crops. The main crops are used as
input replacements for barley malt. The changeover in input mix was assisted by the company’s
N2 million R&D facility, which was commissioned in June 1987 and plant conversion costing
about N100 million (NBL,PLC, 2011).
The company works with highly structured plans, with annual budgets of intentions translated
into explicit targets. The decision board sits towards the end of the year to deliberate on the
report of each divisional head. Annual budget estimates are made in the middle of the year while
decisions on annual plans are left fill the end of the year (NBL, PLC 2011).
The company has experienced remarkable changes in its technical capability. In 1949 it used to
take between 28 and 30 days to produce a bottle of beer but with technological improvement it
now takes about two weeks. The change in input content in the late 1980s also involved changes
in processing technology (NBL, PLC, 2011).
Different measures of productivity are used for the technical division and other divisions. In the
technical section, productivity is measured in terms of the efficiency of plant operation an also in
terms of capacity utilization. In other divisions, it is in terms of the accomplishment of assigned
24
responsibility. The company is viewed as a leader in the national industry and in Africa it enjoys
a high rating, in term of both productivity and product quality (NBL, PLC, 2011).
NBPLC concentrates on the production of its beer and related products, leaving ancillary
services such as bottles, crown corks, labels, cartons and crates to be supplied by other local
manufacturers. In fact, Nigerian law precludes a brewer from producing such ancillary services.
Only the companies in the soft drinks industry appear to sponsor firms to produce such services.
Backward integration into farming was a special concession granted to the breweries in 1984
following the stringent foreign exchange control measures introduced in that year. It also uses
outside transport companies for 60 per cent of total distribution (NBL,PLC, 2011).
The company; cooperates with other producers in the industry in lending materials that are
urgently required were applicable and most needed. Under the umbrella of MAN, it cooperates
with competitors to discuss issues affecting the industry, e.g. adverse government policy. There
is no collusion with competition in marketing and no cooperation in technical services, probably
because most of the local brewers have foreign technical partners (NBL, PLC, 2011).
The prosperity of the company has been preserved by its efficient costing system, which seeks to
protect profit margins in a high-inflation setting by adjusting prices in response to changing costs
of production. Input costs rose to about 105 per cent in the period 1982 to June 1992 and selling
prices have risen to almost the same extent (NBL,PLC, 2011).
Historical Development of Guinness Nigeria Plc
In 1759, Mr. Arthur Guinness built his factory on a site whose area was four acres. The site was
not far from the western entrance to the city of Dublin in Ireland. Even though the gate is no
more, the factory which bears the name of the founder has increased in size to upwards of 66
acres and is one of the biggest breweries in the world (Guinness Nigeria Plc, 2011).
In 1936, the high demand for Guinness necessitated the establishment of a second factory. This
was located at Park Royal near London. In 1963, the third Guinness factory was opened at Ikeja,
Lagos, Nigeria. Quite unlike the situation in both Dublin and Park Royal, Guinness in Nigeria is
bottled in the factory and the Ikeja factory has the largest bottling hall of all Guinness breweries
all over the world (Guinness Nigeria Plc, 2011).
25
The world wide popularity of Guinness has made it possible to establish breweries in the
following countries:
1. Malaysia;
2. Cameroon;
3. Ghana; and
4. Jamaica (Guinness Nigeria Plc, 2011).
Guinness is also brewed under Guinness supervision in the following countries:
i. Kenya;
ii. Sierra Leone;
iii. Australia;
iv. Trinidad;
v. Canada;
vi. Mauritius;
vii. New Zealand;
viii. Seychelles;
ix. Liberia;
x. Thailand;
xi. Indonesia; and
xii. Venezuela (Guinness Nigeria Plc, 2011)
In 1959, Guinness went into the production of a larger brand of beer called Harp in Ireland and
soon expanded this market into many other countries including U.K. where it sells very widely.
Larger beer is now produced in Ireland, U.K., Malaysia, Cameroon and in the Benin Factory in
Nigeria (Guinness Nigeria Plc, 2011).
Historical Development of Premier Breweries Plc
Premier Breweries Plc was incorporated on 23rd January, 1976 with head office in Onitsha,
Anambra State. It was listed on the Nigerian Stock Exchange in 1988. Proshare reports that
Premier Breweries Plc declares 2008, 2009 and 2010 Audited Results with N42.225m million
loss in 2010. The Stock Exchange weekly reports that Premier Breweries Plc’s audited result for
the year ended 31st March 2008 shows Nil Turnover same as in 2007. Loss after tax stood at
N23.005 million compared with N224.21 million in 2007. Consequently, if previous year’s
losses are taken into account, the retained loss carried forward stood at N440.3 million compared
to N417.3 million in 2007 (Premier Breweries Plc, 2011).
26
Historical Development of Continental Breweries Plc
The company was established at Awoomama in 1980 in Imo State and very well located on the
Onitsha-Owerri Road. It is very close to such big towns like Oguta, Orlu, Owerri and Onitsha
and this gives it the advantage of proximity to a lot of traders travelling from Owerri to Onitsha.
Owerri is the State Capital of Imo State, Onitsha is the biggest commercial center in Southern
Nigeria. The location advantage of the factory is instrument to their product “33” lager beer to be
very popular and the patronage of the customers has led to the factory being a very big factory
with very modern factory facilities such as matching, cementing, match rating and bottling
facilities (Continental Breweries Plc, 2011).
1.10 OPERATIONAL DEFINITION OF TERMS
The key terms used in this study are defined as follows:
Capacity Planning is defined as the forecasting and decision making to determine the service
capability of the brewing firms and is the process of determining the production capacity
needed by an organization to meet changing demand for its product (North Caroline State
University). Capacity planning, is also the maximum amount of work an organization is
capable of completing in a given period due constraints such as quality problems, quantity,
delays, materials handling etc. Capacity planning is also used in business computing as
synonym for capacity management.
Performance is the extent to which the brewing firm achieves her organizational objectives. It
looked at operational, financial, and managerial and share angles. A performance
management system thus consists of the processes used to identify, encourage, encourage,
evaluate, improve, and reward employee performance. Armstrong & Baron (1998), define
performance as a strategic and integrated approach to delivering sustained successes to
organizations by improving the performance of the individual contributors. Since
organizations exist to achieve goals, the degree of success that individual employees have in
reaching their individual goals, therefore, becomes a critical state in the capacity planning
process.
Material requirements planning is a method for coordinating detailed production plans, It is a
multi-stage process which beings with a master schedule and works backward to determine
when and how much components will be needed. It gives the time for placing orders and
when the order is required considering the lead time.
27
Capacity requirements planning is a method that utilizes the time faced material plan
information produced by a material requirement system and includes the consideration of all
actual lot sizes as well as lead times for both open shop orders (schedule receipts) and orders
planned for future release (planned orders).
Profitability is that aspect of performance that is a measure of the difference between total
revenue and total cost, and if the difference is positive, it is said that there is profit, if
negative, it is said to be a loss.
(i) Steps are procedures for doing capacity planning.
(ii) Competitive advantage is the distinctive competence which makes a particular firm to
stand out among its competitors.
(iii) Capacity building is the creation of enabling environment with appropriate policy and
legal frameworks, institutional development, including community participation (of
women in particular), human resources development and strengthening of managerial
systems.
28
REFERENCES
Chase, R.B. and Aquilano, N.J. (2005), Production and Operations Management Homewood,
Illinois: Richard D. Irwin, Incorporated.
Continental Breweries Plc (2011). “Annual Report and Statement of Accounts”
www.continentalbreweiresplc.orgdownloaded 10th November, by 2 pm.
Guinness Nigeria Plc (2011). “Annual Report and Statement of Accounts”
www.guinnessbreweriesannualreport.orgdownloaded 10th November, by 4 pm.
Lafontaine, A. (2000). “Assessment of Capacity Development Efforts of Other Development
Cooperation Agencies.” Capacity Development Initiative, GEF-UNDP Strategic
Partnership, 1-20
Nigerian Breweries Plc (2011). “Annual Report and Statements of Accounts”
www.nigerianbreweriesplcannualreport.org downloaded by 10th November by 5 pm.
Premier Breweries Plc (2011). “Annual Report and Statement of Accounts”
www.bremierbreweriesannualreport.orgdownloaded 10th November, by 3 pm.
Skinner, W. (2004), “The Focus Factory”. Harvard Business Review, May – June. 113-121.
UNDP (2012).United Nations Development Programme. www.undp.org. downloaded 24
September by 3pm, 1-10
UNDP/UNDOALOS, (1994), “Reports on the Consultative Meeting on Training in Integrated
Management of Coastal and Marine Areas for Sustainable Development,” Sassari,
Sardinia, Italy, 21-23 June, 1993. United Nations Development Programme and Division
for Ocean Affairs, United Nations, New York, 1-20
Vallejo, S.M. (2006), Are we meeting the challenges for capacity building for managing ocean
and coasts? Balboa, Panama, November, 13-14.
29
CHAPTER TWO
LITERATURE REVIEW
Capacity Planning is the process of determining the production capacity needed by an
organization to meet changing demands for its products (North Caroline State University). In the
context of capacity planning, ‘design capacity’ is the maximum amount of work that an
organization is capable of completing in a given period, ‘effective capacity’ is the maximum
amount of work that an organization is capable of completing in a given period due to constraints
such as quality problems, delays, material handling, etc. Capacity planning is also used in
business computing as a synonym for Capacity Management.
Planning is necessary in all complex organizations. In the absence of planning, different work
units may pursue the possibly conflicting objectives of their own (Sheu and Wacker, 2001).
However, not all organizations are complex and thus heavy planning efforts are not always
necessary. In simple settings, where specialization, action variety, and task interdependence are
low, coordination can be achieved through rules and heuristics (Cyert and March, 1963).
Capacity planning in the literature has been applied to the manufacturing industry. The Research
Gap here is to determine the effect of capacity planning on the performance of the Brewing
Industry in South Eastern Nigeria. In manufacturing management, the planning-focused methods
have been developed around the concept of material requirements planning (MRP, Orlicky,
1975), while the methods that emphasize rule-based control and simplicity are founded on the
just-in-time (JIT) methodology (Ohno, 1988).
Performance factors include: efficiency, effectiveness, productivity, profitability, solvency,
leverage, activity and morale (Nwachukwu, 2004). Dictionary’s definition of efficiency as fitness
or power to accomplish or success in accomplishing the purpose intended, adequate power,
effectiveness, efficacy. Later on, it is pointed out that efficiency acquired a second meaning – the
ratio between input and output, between effort and results, expenditure and income, cost and the
resulting pleasure, this second meaning became current in Business and Economics, only since
the beginning of the 20th Century. Still later on, influenced by the scientific management
movement, efficiency was defined as the ratio of actual performance to the standard performance
(Bell, 2006).
The performance of the brewing industry was constrained by high cost of production which was
attributable mainly to substantial depreciation of the naira exchange rate. The resultant sharp rise
in cost of importation of raw materials, machinery and spare parts resulted in corresponding
30
sharp rise in the overall cost of production. Other factors that contributed to high cost of
production during the year were escalation in interest rates and sharp increases in tariffs on
public utilities, especially electricity. The sharp increase in production costs was translated into
higher product prices which tended to dampen demand for local manufactures resulting in high
inventory accumulation. Another factor that reduced the domestic demand for locally produced
goods was massive importation and smuggling of a wide range of foreign goods into the country
(CBN, 2002).
2.2 CONCEPTUAL FRAMEWORK
2.2.1 Capacity Planning
Capacity Planning is the process of determining the production capacity needed by an
organization to meet changing demands for its products (North Caroline State University, 2006).
In the context of capacity planning, ‘design capacity’ is the maximum amount of work that an
organization is capable of completing in a given period, ‘effective capacity’ is the maximum
amount of work that an organization is capable of completing in a given period due to constraints
such as quality problems, delays, material handling, etc. Capacity planning is also used in
business computing as a synonym for Capacity Management (Gunther, 2007).
Guinness Nigeria Plc (2011), defines capacity planning as the extent to which decisions are taken
and forecasting is made on the production capability of the facility for brewing stout and lager
beer and producing malt with the use of raw materials: malted barley, hops, yeast, and water.
Brewing in Guinness was done for the first time in 1759 by Mr. Arthur Guinness at St. James’s
gate in Dublin Ireland. Brewing had been done earlier outside Guinness.
A discrepancy between the capacity of an organization and the demands of its customers results
in inefficiency, either in under-utilized resources or unfulfilled customers. The goal of capacity
planning is to minimize this discrepancy. Demand for an organization’s capacity varies based on
changes in production output, such as increasing or decreasing the production quantity of an
existing product, or producing new products. Better utilization of existing capacity can be
accomplished through improvements in overall equipment effectiveness (OEE). Capacity can be
increased through introducing new techniques, equipment and materials, increasing the number
of workers or machines, increasing the number of shifts, or acquiring additional production
facilities (Gunther, 2007).
31
Capacity is calculated:
( ) ( ) ( ) ( )efficiency nutilizatioshifts ofnumber or workers machines ofnumber ×××
The broad classes of capacity planning are lead strategy, lag strategy, and match strategy.
• Lead strategy: is adding capacity in anticipation of an increase in demand. Lead strategy
is an aggressive strategy with the goal of luring customers away from the company’s
competitors. The possible disadvantage to this strategy is that it often results in excess
inventory, which is costly and often wasteful (Olhanger, 2003)
• Lag strategy: refers to adding capacity only after the organization is running at full
capacity or beyond due to increase in demand (North Olhanger, 2003). This is a more
conservative strategy. It decreases the risk of waste, but it may result in the loss of
possible customers.
• Match strategy: is adding capacity in small amounts in response to changing demand in
the market. This is a more moderate strategy.
Capacity planning is long-term decision that establishes a firms’ overall level of resources. It
extends over time horizon long enough to obtain resources. Capacity decisions affect the
production lead time, customer responsiveness, operating cost and company ability to compete.
Inadequate capacity planning can lead to the loss of the customer and business. Excess capacity
can drain the company’s resources and prevent investments into more lucrative ventures. The
question of when capacity should be increased and by how much is the critical decisions (Hill,
2006).
From a scheduling perspective, it is very easy to determine how much capacity (or time) will be
required to manufacture a quantity of parts. Simply multiply the Standard Cycle Time by the
Number of Parts and divide by the part or process.
If production is scheduled to produce 500 pieces of product A on a machine having a cycle time
of 30 seconds and the OEE for the process is 85%, then the time to produce the parts would be
calculated as follows:
(500 parts x 30 Seconds)/85% = 17647.1 seconds. The OEE index makes it easy to determine
whether we have ample capacity to run the required production. In this example, 4.2 hours at
standard versus 4.9 hours based on the OEE index (Lazowska, 1984).
32
Repeating this process for all the parts that run through a given machine, it is possible to
determine the total capacity required to run production. Considering new work for a piece of
equipment or machinery, knowing how much capacity is available to run the work will
eventually become part of the overall process. Typically, an annual forecast is used to determine
how many hours per year are required. It is also possible that seasonal influences exist within
your machine requirements, so perhaps a quarterly or even monthly capacity report is required.
The steps for capacity planning include:
1. Determine Service Level Requirements
The first step in the capacity planning process is to categorize the work done by systems
and to quantify users’ expectations for how that work gets done.
2. Analyze Current Capacity
Next, the current capacity of the system must be analyzed to determine how it is meeting
the needs of the users.
3. Planning for the Future
Finally, using forecasts of future business activity, future system requirements are
determined. Implementing the required changes in system configuration will ensure that
sufficient capacity will be available to maintain service levels, even as circumstances
change in the future (Blackstone, 2009).
Determining service levels is important in capacity planning. The overall process of establishing
service level requirements first demands an understanding of workloads.
Workloads from a capacity planning perspective, is a computer system processes workloads
(which supply the demand) and delivers services to users (Mckay and Wiers, 2004)
During the first step in the capacity planning process, these workloads must be defined and a
definition of satisfactory service must be created. A workload is a logical classification of work
performance on a computer system. For capacity planning purposes, it is useful to associate a
unit of work with a workload. This is a measurable quantity of work done, as opposed to the
amount of system resources required to accomplish that work.
To understand the difference, consider measuring the work done at a fast food restaurant. When
deciding on the unit of work, you might consider counting the number of customers served, the
weight of the food served, the number of sandwiches served, or the money taken in for the food
served. This is an opposed to the resources used to accomplish the work, i.e., the amount of
33
French fries, raw hamburgers or pickle slices used to produce the food served to customers
(Berry, Schmitt and Vollmann, 2002).
The next step is to establish a service level agreement. A service level agreement is an agreement
between the service provider and service consumer that defines acceptable service. The service
level agreements are often defined from the user’s perspective, typically in terms of response
time or throughput. Using workloads often aids in the process of developing service level
agreements, because workloads can be used to measure system performance in ways that makes
sense to clients/ushers (Berry et al, 2002).
Udochi (1999) worked on the adoption of Total Quality Management (TQM) in enhancing
capacity planning in Guinness Nigeria Plc had steps that were different from that of Blackstone
(2009). The steps of Udochi were as follows:
1. Determining the current capacity needs.
2. Determining the future capacity needs.
3. If step one is less than step two, then the company needs to take a decision on contracting out
capacity, outsourcing or use of shifts.
4. If step one is more than step two, then the company can just continue.
5. When the decisions are taken, there is need for implementation and control after involving
top management.Udochi found that total quality management was very useful by
encouraging continuous quality in enhancing capacity planning in Guinness Nigeria Plc.
Arisa (2007) worked on investment practices to enhance capacity planning in an industry: a case
study of Guinness Nigeria Plc. He observed that in 1963, the high demand for Guinness that
necessitated the establishment of a third Guinness Brewing at Ikeja was because there was the
investment practice of raising money through shares and bonds so as to go into the capacity
planning of building a third Guinness Brewing at Ikeja, Lagos. Quite unlike the situation in both
Dublin and Park Royal, Guinness in Nigeria is bottled in Ikeja Brewery and capacity is built by
having the largest bottling hall of all Guinness Breweries. After the marshing process of the raw
materials and the fermentation process the liquid that is produced is called wort which has to be
marturated. It is after the marturation of beer that it can bottled. Arisa (2007) found that the
investment practice which enabled having sufficient share capital and bond capital was necessary
for enhancing capacity planning in Guinness Nigeria Plc.
34
In the case of appointment scheduling application, service level requirement might be
established, regarding the number of requests that should be processed within a given period of
time, or it might be required that each request be processed within a certain time limit. These
possibilities are analogous to a fast food restaurant requiring that a certain number of customers
should be serviced per hour during the lunch rush, or that each customer should have to wait no
longer than three minutes to have his or her order filled (Berry et al, 2002).
Ideally, service level requirements are ultimately determined by business requirements.
Frequently, however, they are based on past experience. It is better to set service level
requirements to ensure that the business objective will be accomplished, but not surprisingly
people frequently resort to setting service level requirements like provide a response time at least
as good as is currently experienced, even after the business is ramp up.
There are several steps that should be performed during the analysis of capacity measurement
data.
a. First is comparing the measurements of any items referenced in service level agreements
with their objectives. This provides the basic indication of whether the system has
adequate capacity.
b. The next step is checking the usage of the various resources of the system (CPU,
memory, and I/O devices). This analysis identifies highly used resources that may
provide problematic now or in the future.
c. Looking at the resource utilization for each workload. Ascertain which workloads are the
major users of each resource. This helps narrow the attention to only the workloads that
are making the greatest demands on system resources.
d. Determining where each workload is spending its time by analyzing the components of
response time, allowing the determining of which system resources are responsible for
the greatest portion of the response time for each workload (Blackstone, 2009).
It is important to take a look at each resource within systems to see if any of them are saturated.
If a resource that is running is found at 100% utilization, then any workloads using that resource
are likely to have poor response time. If the goal is throughput rather than response time,
utilization is still very important. If it has two disk controllers, for example, and one is 50%
utilized and the other is swamped, then it has an opportunity to improve throughput by spreading
the work more evenly between the controllers.
35
The resources that are responsible for the greatest share of the response time are indicators for
where it should concentrate efforts to optimize performance. Using TeamQuest Model, it can be
determined the components of response time on a workload by workload basis, and it can predict
what the components will be after a ramp-up in business or a change in system configuration.
Components of response time analysis shows the average resource or component usage time for
a unit of work. It shows the contribution of each component to the total time required to
complete a unit of work
The steps to a capacity plan are:
a. First, forecasting what the organization will require of the IT systems in the future.
b. Using TeamQuest Model to determine the optimal system configuration for meeting
service levels on into the future.
Systems may be satisfying service levels now, but will they be able to do that while at the same
time meeting future organizational needs? Besides service level requirements, the other key input
into the capacity planning process is a forecast or plan for the organization’s future. Capacity
planning is really just a process for determining the optimal way to satisfy business requirements
such as forecasted increases in the amount of work to be done, while at the same time, meeting
service level requirements (Karmarker, 2009).
Future processing requirements can come from a variety of sources. Input from management
which include:
• Expected growth in the business.
• Requirements for implementing new applications.
• Planned acquisitions or divestitures.
• IT budget limitations.
• Requests for consolidation of IT resources.
Additionally, future processing requirements may be identified from trends in historical
measurements of incoming work such as orders or transactions (Karmarker, 2009).
After system capacity requirements for the future are identified, a capacity plan should be
developed to prepare for it. The first step in doing this is creating a model of the current
configuration. From this starting point, the model can be modified to reflect the future capacity
36
requirements. If the results of the model indicate that the current configuration does not provide
sufficient capacity for the future requirements, then the model can be used to evaluate
configuration alternatives to find the optimal way to provide sufficient capacity.
In summary, these basic steps towards developing a capacity plan have been shown as follow:
1. Determining service level requirements.
a. Defining workloads
b. Determining the unit of work
c. Identifying service levels for each workload
2. Analyzing current system capacity
a. Measuring service levels and compare to objectives
b. Measuring overall resource usage
c. Measuring resource usage by workload
d. Identifying components of response time
3. Planning for the future
a. Determining future processing requirements
b. Planning future system configuration.
By following these steps, it ensures that the organization will be prepared for the future, ensuring
that service level requirements will be met using an optimal configuration, and also have the
information necessary to purchase only what is needed, avoiding over-provisioning while at the
same time assuring adequate service (Karmarker, 2009).
2.2.2 Importance of Capacity Planning
Capacity planning is important because it makes the manufacturing organization to determine the
production capability of the facility. This will enable the organization to have the appropriate
through put. By having the appropriate throughput, the production process will be properly
ascertained. It will consist of the appropriate machinery, methods and maintenance (Vollmann,
Berry, Whybark and Jacobs, 2005).
Capacity planning also makes the organization to have the appropriate outputs. In a brewery, the
output will be in hectolitries of beer produced per month. The appropriate will enable the
organization to plan her sales strategies. From the sales, the total revenue will be determined as
sales revenue is price times quantity produced (Bandey, 2008). This is why capacity has a direct
37
positive relationship with the competitive advantage. This is because a company with adequate
capacity planning will know her output and sales in advance and use the knowledge to be ahead
of its competitors. It will be possible to have the correct price strategies.
Capacity planning makes the organization to know its inputs. Inputs are of the form of raw
materials, mean (human resource), money (capital) time, knowledge, energy, information and
infrastructure. It is these inputs that are processed through the production process to get the
outputs in the form of goods and services (Krajewshi, and Bitzman, 2000). The raw materials for
producing beer are malted barley, yeast, hops, additives, concentrated stout for blending and
water. Water is the largest by volume as beer is 95% water.
Capacity planning entails a knowledge of the current capacity and the average utilization rate.
The average utilization rate is the average output rate divided by capacity. It enables the
organization to determine if capacity is too much or too little. If it is too much, the company will
need to outsource, reduce capacity or sub contract some capacity. If it is too little the company
will need to run shift, or build another plant (Krajewishi and Bitzman, 2000).
2.2.3 Concept of Capacity Planning
Egujie (2001) wrote that capacity planning is the process of taking future decision today on the
inputs, throughput and outputs to meet the production requirements in a manufacturing
organization such as brewery. He pointed out that there is a hierarchy for the system linkages for
the capacity planning modules and one of the items in the hierarchy is resource planning. It is
linked directly to the sales and operations planning modules. It is the most highly aggregated and
longest range planning decision. The master production schedule is the primary information
source for rough-cut capacity planning. The rough-cut capacity planning stage is the next stage
lower than the resource planning stage. For breweries using materials requirements planning to
prepare detailed materials plans, a much more detailed capacity planning is possible with the
capacity requirements planning (CRP) is the third stage lower than rough-cut capacity planning
stage. The next stage after the capacity requirements planning is finite loading stage. Finite
loading in some ways is better seen as shop scheduling process and its therefore part of
production activity control and it is also a capacity planning procedure. The last but not the least
is the input/output stage. The inputs in Nigeria brewery include raw materials, men or human
resource which also includes women, money which is packaged through investment practices,
time which is a non-renewable resource, energy which is power x time and it is measured in
Joules when the power is in watts and time is in seconds. It also includes knowledge which is
38
specialized information gained through education, training and development and it makes the
workers in the breweries studied to reason a logical manner. It also includes information which is
processed data and it is needed for decision making, it also includes infrastructure which is the
totality of such public facilities like road network, power supply, air ways, communication
system, rail-way network, etc. Unfortunately, the breweries spent so much on electricity because
of the poor outage of electricity by the Power Holding Company of Nigeria (PHCN) and this
makes for the increase in a bottle of beer. Egujie (2001) found that quality control was a very
useful technique to improve capacity planning in a company striving for excellence such as
Bendel Brewery.
Planning is necessary in all complex organizations. In the absence of planning, different work
units may pursue the possibly conflicting objectives of their own (Sheu and Wacker, 2001).
However, not all organizations are complex and thus heavy planning efforts are not always
necessary. In simple settings, where specialization, action variety, and task interdependence are
low, coordination can be achieved through rules and heuristics. In manufacturing management,
the planning-focused methods have been developed around the concept of material requirements
planning, while the methods that emphasize rule-based control and simplicity are founded on the
just-in-time (JIT) methodology (Ohno, 1988).
A classic way to pursue simplification in brewing is to isolate operations from external
uncertainties. The extent of the isolation depends greatly on the order penetration point (Olhager,
2003:319): the earlier the order-specific requirements are taken into account, the higher is the
exposure to the environment. That is why planning methods are most important in the MTO
manufacturing and the JIT methods are at their best in the make-to-stock environments
(Karmarkar, 1989; Vollman, 2005).
Usually both approaches co-exist in assemble-to-order systems and other intermediate settings.
The postponement of the order penetration point enables the use of JIT methods in the upstream
operations of customized manufacturing (Olhager and Rudberg, 2002). However, the inherent
complexity of producing according to individual orders cannot be eliminated by forcing JIT
methods upon the MTO parts of the processes (Hopp and Spearman, 2004). Hence, the time-
phased planning has remained as a vital part of manufacturing management despite the important
contributions of JIT. Recent literature has describes several techniques for integrating the
benefits of the two paradigms. The techniques are known by many names (e.g., CONWIP,
39
POLCA, COBACABANA, etc) and they differ in details but time-phased planning methods for
the creation of production schedules (Spearman et al, 1990; Suri, 1998; Land, 2009).
Contemporary methods of time-phased production planning are based on the Manufacturing
Resource Planning (MRP) framework. It was originally developed to complement MRP with
capabilities to check material plans’ feasibility against capacity constraints. Later, more
advanced applications of MRPII have been developed so that the feasibility checks could be
extended to other factors such as delivery schedules and financial constraints (Yusuf and Little,
1998). However, the practical implementations of such solutions have remained rare (McKay
and Wiers, 2004). In fact, it has been observed that even the capacity planning features of MRPII
are far less utilized than what could be expected on the bases of the academic literature (Halsall
et al, 1994); Kemppainen, 2007). As the material-planning parts of MRPII are well-established
(Vollmann, 2005), the observation implies that companies’ production planning practices can be
measured through the methods that they use in capacity planning.
Recent developments in enterprises software deliver a promise of easily applicable capacity
planning tools. While the conventional ERP systems are well-suited for the simpler capacity
checks, the so-called advanced planning and scheduling (APS) systems promote the more
sophisticated methods (Kreipl and Pinedo, 2004; Stadtler and Kilger, 2005). However,
companies’ diligence in applying their enterprise systems’ features is known to vary
considerably (e.g. Bendoly and Cotteleer, 2008). Thus, variance may be found also in the
utilization of the capacity planning features. That variance enables testing whether complex
organizations that do not put efforts in planning suffer from the lack of coordination (e.g.,
Zwikael and Sadeh, 2007). Consequently, the following hypothesis is presented as the
underlying assumption of this study:
Advantages of Sophisticated Capacity Planning Methods
It is reasonable to assume that not only the efforts in capacity planning but also the ways of
planning matter. The practical relevance of the framework is high because dominant ERP
software providers have structured their production planning modules in the same fashion (SAP,
2009). In addition, most textbooks either refer to it directly or provide illustrations that closely
resemble it (Hill, 2005 and Stevenson, 2004).
The backbone of the capacity planning process is in the material planning activities, that is:
master production scheduling (MPS), MRP, and the input/output (I/O) control (Vollmann et al,
40
2000). The optional activities are on the side of capacity planning. They are numbered in the
order of sophistication. It shows that the amount of required data records increases as the
methods get more sophisticated. The increase is cumulative because the records do not fully
substitute each other. Brief descriptions of each method are given in the following:
Non-systematic capacity planning represents inexplicit consideration of capacity constraints.
At the level of master schedules, it means that planners use their personal experience to evaluate
the feasibility of plans (Proud, 2007). In MRP, the inexplicit capacity considerations are realized
through the lead time parameters of bills of materials. The processing lead times represent the
averages, while the variances around the averages are taken into account with safety lead times
(Vollmann et al, 2000). In the I/O, priority scheduling rules can be used to level capacity
utilization without formal planning activities (Green and Appel, 1981; Kemppainen, 2007).
Rough-cut capacity planning (RCCP) is the simplest systematic method. It can be done with
several techniques but they all share the common characteristic of aggregation. Materials are
aggregated to end products or product groups and capacities to production lines or resource
groups (Proud, 2007). RCCP simplifies planning by ignoring sub-assembly inventories,
operations’ sequences, setups, and batch sizes but still provides the planners with a systematic
means to supervise how the resource utilization accumulates during the MPS activity (Vollmann
et al, 2000). That is an advantage when master schedules are updated frequently, MPS items are
numerous, or different MPS items load the same resources. In such situations, then non-
systematic methods are prone to human errors and easily result in overloaded schedules.
Capacity requirements planning (CRP) provides a more detailed technique for checking
material plans’ feasibility. The CRP calculations are done not only for the end products but also
for the subassemblies. In addition, the routing data enable calculating loads at individual
resources and considering the effects of operations’ sequences, setups, and batch sizes. Thus,
CRP corrects for the simplifications of RCCP and helps generating more reliable schedules.
Iterating the plans to achieve feasibility in terms of resources’ capacity limits is done manually
by human planners (Burcher, 1992; McKay and Wiers, 2004).
The next step from CRP is to automate the iterations of the plans. It can be done with finite
loading methods that are usually featured in APS systems (McKay and Wiers, 2004). The
process of using them is typically the following: first, material plans are downloaded from an
ERP system. Then, the algorithms of the finite loading software are used to find a solution,
where capacity constraints are satisfied with the fewest breaches of due dates. Finally, the
41
revised plans are uploaded back to the ERP system, where they are executed. The obvious
benefit of automating the capacity leveling is that it reduces the room for human errors.
In addition to capacity leveling, the finite loading algorithms can be used to solve more
complicated scheduling problems. The finite loading tools with optimization may be used, for
example, to maximize throughput or to minimize setups or downtimes (e.g., Davis and Mabert,
2000). Such techniques require the most planning parameters and their outputs are highly
dependent on the accuracy of the parameters. Yet, the data maintenance efforts and the
investments in the software may well be justified in some manufacturing environments, for
example in capital intensive production systems (Kreipl and Pinedo, 2004).
The planning methods are by no means mutually exclusive. Instead, several methods can be used
simultaneously for different purposes. For example, plant managers can use RCCP to evaluate
sales plans, master schedulers may use CRP to supervise their processes, and production
planners can do the finite loading of critical resources. A concept that brings clarity to this
plurality is bottom-up re-planning (Fransoo and Wiers, 2008; Vollmann et al., 2000). It means
that master schedules are updated on the bases of the lower-level planning activities. In a closed-
loop planning system, the master schedules are based on the finite loading of critical resources
(Kenat and Sridharan, 1998). In an intermediate solution, the master schedules are revised on the
bases of CRP. Consequently, the main method of planning can be identified. It is the method that
determines the output to which the manufacturing function commits itself.
In handling the advantages of capacity planning in the Nigerian Brewery studied (Osaguona,
2006) wrote that the first advantage of capacity planning in the Nigeria breweries studied is that
it entailed the use of the correct quality of the input materials. If the correct inputs are not
allowed for, it will not yield the correct output. The brewing department is saved from the
problem of having to a lot of blending with good quality beer before arriving at a good quality
product. Blending entails going back a lot of times to the labouratory to run physical, chemical
and micro-biological to get the appropriate quality of the final lager beer, stout, malt products.
Another advantage of capacity planning is that it entails the appropriate throughput. By
throughput is meant the production process. The components of the process include machines,
methods, and maintenance. The brewery studied had the appropriate machines that are imported
from Dublin, Ireland. Others are placed through Oversea Buying Limited (OBL) London. Some
of the machines include marshing machine, fermenter, marturating machine and bottle pasturizer.
42
The methods include marshing, decoction, fermentation, marturation techniques, bottling and
even distribution to the customers. Maintenance is to ensure the reliability of the brewing system.
Maintenance is by a combination of planed and corrective maintenance and overhauls. Every
year, the brewery is closed for some few months, and the maintenance crew is invited from the
manufacturers of the equipments. They come with some of the knockdown parts and all the
critical parts of the equipments are changed and the equipments function as though they are new.
Osaguona (2006) found that quality control was an enabler of capacity planning in the brewing
companies studied.
As all of the advanced planning methods aim to reduce errors in planning, it can be proposed that
they should have a positive effect on operational performance. Some studies have already
implied evidence of such an effect (Sheu and Wacker, 2001). Yet, they have not included finite
loading techniques, which is a major shortcoming because substantial effort has been put into
their development (Kouvelis et al., 2005). The development of progressive algorithms and
software would be well justified if there was evidence on the relationship between the accuracy
of planning and performance. Hence, the following hypothesis is formulated:
Fit between Capacity Planning Methods and Process Types
Another perspective to different planning methods’ effectiveness is to assume that methods’
suitability would depend on the context of their usage. Preliminary support for such an argument
can be found in the surveys of Jonsson and Mattsson (2003). This shows that practitioners’
satisfaction with different planning techniques depends on the type of their production processes:
the managers of job shops are content with RCCP, the most satisfied users of CRP work in batch
process plants, and the finite loading methods are most popular in production lines. The
observations are aligned with the systematic review of and the review of Sousa and Voss, which
both indicate that the process type is a typical contingency factor for the effectiveness of various
operations management practices. In the context of planning, the influence of the process type
can be explained with two classic contingency-theoretical constructs: the repetitiveness and the
complexity of the tasks that constitute the processes.
1) RCCP fits with the job shops because in low-volume and high-variety environments, the
data records of the more detailed methods are difficult to maintain. Moreover, the more
detailed resource-specific plans are not necessary because the complexity of the system is
43
limited with general-purpose machinery and widely skilled workforce (Blackstone and
Cox, 2005; Hill, 2007).
2) CRP fits with the batch processes because the more repetitive operations make the
maintenance of the data records worthwhile. Furthermore, information about the
resource-specific workloads is necessary because the resources are more specialized, and
different products utilize them differently (Jonsson and Mattsson, 2003).
3) Finite loading methods fit with batch processes, whose complexity is reduced with
bottleneck control (Vollmann et al, 2000). Finite loading works in a batch process if a
stationary bottleneck can be identified and all other resources are subordinated to its
schedule. Otherwise, each finite loading of one resource can make another resource a
new bottleneck, and consequently the iteration of the plans may become endless.
4) In production lines, the complexity is low because all resources are subordinated to the
flow of the line. Thus, the capacity of the entire line can be planned as a single resource.
Detailed planning is desirable because untimely changeovers can be costly in larger
assembly lines (Hayes and Wheelwright, 1979) or cause congestion in smaller
manufacturing cells. In addition, the repetitiveness of operations makes it easier to
maintain the parameters of the most sophisticated methods.
The relationship between the process types and planning methods can also be explained with the
interdependence between the resources of the processes. The alternative types of
interdependence are pooled, sequential, and reciprocal. The pooled and the sequential processes
are the simplest to coordinate but they have very different implications for planning (Barki and
Pinsonneault, 2005). The processes with pooled resources are inherently flexible, and that is a
capability that should not be constrained with too stringent planning. A job shop is an archetype
of pooled interdependence (Galbraith, 1973). Meanwhile, the sequential processes are suited for
efficiency, which is a capability that can be fostered with detailed planning. In manufacturing
environments, sequential relationships exist in production lines and around the bottlenecks of
batch processes.
The most difficult processes to coordinate are those where resources are reciprocally
interdependent. That is because all actions by any resource may affect multiple other resources
(Galbraith, 1973). Some specificity in planning is necessary to prevent undesirable cascade
effects but getting into the details is difficult because the possible interactions are. Therefore, a
44
moderately sophisticated planning method such as CRP is the most suitable option for the
reciprocal processes of batch shops (Reeves and Turner, 1972).
2.2.4 The Concept of Performance
Performance factors include: efficiency, effectiveness, productivity, profitability, solvency,
leverage, activity and morale (Nwachukwu, 2004).
Dictionary’s definition of efficiency as fitness or power to accomplish or success in
accomplishing the purpose intended, adequate power, effectiveness, efficacy. Later on, it is
pointed out that efficiency acquired a second meaning – the ratio between input and output,
between effort and results, expenditure and income, cost and the resulting pleasure, this second
meaning became current in Business and Economics, only since the beginning of the 20th
Century. Still later on, influenced by the scientific management movement, efficiency was
defined as the ratio of actual performance to the standard performance (Bell, 2006).
While efficiency is concerned with measuring the ability of inputs to produce outputs, or
relationship between performance and standard efficiency is concerned wit the failure of inputs
to achieve desired outputs, the gap between actual performance and expected, and between
results and efforts (Abernathy and Townsend, 2005).
Apart from the efficiency another closely related performance variables is effectiveness. To be
literally means to have effects, when we say that something is effective we mean that it has
effects that we desired that we recognize as international in the design of the thing in question.
When we say that a television set is effective we mean that it provides clear picture and
reasonable reproduction of sounds. Such an example serves in this simple case in which the
system under study has felt outcomes and the relevant observers are decided on what is intended
to design and use. When the system under study has few outcomes and the relevant observers are
decided on what is intended in design and user. When the system is more complex like in the
case of a public enterprise, operationalisation becomes difficult. However, one public enterprise
is more effective than another if:
(a) It has more chances of survival than the other;
(b) It meets its essential function or throughput than the other;
(c) It contributes more to the suprasystem than the other; and
45
(d) It more than maximizes its benefits like profit subject to some constraints like taxes and
other obligations than the other. Apart from efficiency and effectiveness another important
performance variable is productivity (Nwachukwu, 2004).
Productivity has been defined as the measure of how well resources are brought together in
organization and utilized for accomplishing a set of results. It is reaching the highest level of
performance productivity in a public expenditure or resource. To operationalise productivity in a
public enterprise the ratio of total output to total input in very handy. Total input is the naira
value of all the factors of production for that year which include land, labour and capital. The
limitation of this method of operationalising productivity is that entrepreneurship or management
which is the factor of production is difficult to quantify in monetary terms. Another limitation is
that of public enterprises that render a service, it becomes difficult to quantify the output in
monetary terms since the outputs are not tangible (Buffa and Sarin, 2007).
This measure of productivity has the advantage that it aggregates the effectiveness of the use of
the factors of production of the public enterprise to produce goods and services. It draws
attention to the fact that a good integration of resources physical and human will yield higher
output of the public enterprises shown by the result of total output/total input being greater than 1
(Cohen and Zysman, 2007).
Higher productivity of the employees of a public enterprise has the following good effects:
iii. Higher incomes and profits;
iv. Higher earning;
v. Increased supplies of both consumer and capital goods at lows costs and lower prices;
vi. Ultimate shorter hours of work and improvements in working at living conditions;
and
vii. Strengthening the general economic foundation of workers.
Another performance variable apart from productivity is profitability or the ability of the
enterprise to make profit. Profit is the income or the difference between sales revenue and total
cost. The profitability of enterprise is summarized in the valuation of that enterprise. Indeed the
basic objective of measurement of profitability is to provide a valuation, the enterprise which
will be a critical assessment of the worth of the investment. In effect, the value of an enterprise
may be stated as being the present value of its future stream (Bell, 2006).
46
The profitability of a privatized or commercialized public enterprise can be operationalised by
using profitability ratios. Profitability ratios are classified into two categories; ratios which
express income as a percentage of sales, and, and ratios which express income as a yield
associated with the employment of resources. For the purpose of the analysis of profitability,
income is generally expressed as earnings before interest and tax (EBIT). The profitability ratios
of income as a percentage of sales include the following:
i. gross profit ratio which is the ratio of gross margin or profit to sales which is used to
check stability of market conditions;
ii. net income ratio of earnings before interest and taxes to sales. The profitability ratio of on
resources employed include the following;
iii. return on capital employed which is the ratio of the earnings before interest and taxes
over net asset value is found by adding the value of assets to that current assets and
subtracting the total assets which is the ratio of earnings before interest and taxes over
fixed plus current assets;
iv. return on total assets which is the ratio of earnings before interest and taxes plus current
assets; and
v. return on gross assts which is the ratio of earnings before interest and taxes plus
depreciation for the period over assets at costs plus current assets.
Another performance variable apart from profitability is solvency. Solvency is the ability of an
enterprise to meet its immediate financial obligations and thus avoid the possibility of
insolvency. To operationalise the solvency of the public enterprise two ratios are in common use
as follows:
i. current ratio which is the ratio of current assets to current liabilities is a measure of how
current assets could be converted into cash to meet current liabilities and if its value is
less than one it would indicate that the firm might have a potential problem in meeting
creditor’s claim;
ii. acid test ratio which is the ratio of current assets less inventory over current liabilities
which recognizes he problem of the current ratio that inventory is not easily converted to
cash (Nwachukwu, 2004).
47
Another performance variable apart from solvency is leverage which is a measure of how far the
total capital of the enterprise is borne by low term debt. In operationalising the leverage of
privatized public two ratios come in hand as follows:
i. gearing of leverage ratios which is long term debt as a fraction of share capital;
ii. gearing or leverage ratio which is longer term as a fraction of share capital.
Another performance variable apart from leverage is activity.
Activity is defined as the use made of resource by the enterprise. To operationalise activity of a
public enterprise the following ratios are useful as follows:
(i) Inventory turnover or the ratio of sales over average inventory which is the rate at
which an enterprise converts inventory into sales;
(ii) Average debt collection period which is given by debtors divided by credit sales times
365 which gives the average number of days for payment;
(iii) Ratio of sales to total assets value which is the ratio of sales to fixed assets plus current
assets and indicates the ability of the assets to generate income.
Apart from activity another performance variable is morale. It is truly as a member of an
integrated group with high morale that a worker in public enterprise can make his maximum
contribution the enterprise. Morale is the state of mind which makes men do great things
(Nwachukwu, 2006). The movement of morale includes the following:
i) Polarization;
ii) Autonomy;
iii) Flexibility;
iv) Potency;
v) Participation.
Polarization is the degree to which the group is oriented towards goals that is clear to the
members and share by them. Autonomy is the degree to which a group determines it’s own
activities and takes it’s or decision. Flexibility is the degree to which the group’s activities are
mark as informal rather than formal procedures. Potency is the degree to which the individual
needs are satisfy by membership in the group it. Participation is the degree to which members of
the groups at themselves to the assigned duties (Nwachukwu, 2004).
48
2.2.5 Concept of Manufacturing
The index of manufacturing production increased by 2.6 per cent to 182.7 (1985 =100) in 1992
compared with 9.3 per cent in 1991. The increase was reflected in all the quarterly indices which
were generally higher than those of the previous year, except the second quarter where the index
declined slightly by 0.2 per cent. The share of the subsection in the Gross Domestic Product
(GDP) rose from 8.4 per cent in 1991 to 8.6 percent in 1992 (CBN, 2002).
The performance of the brewing sub-sector was constrained by high cost of production which
was attributable mainly to substantial depreciation of the naira exchange rate. The resultant sharp
rise in cost of importation of raw materials, machinery and spare parts resulted in corresponding
sharp rise in the overall cost of production. Other factors that contributed to high cost of
production during the year were escalation in interest rates and sharp increases in tariffs on
public utilities, especially electricity. The sharp increase in production costs was translated into
higher product prices which tended to dampen demand for local manufactures resulting in high
inventory accumulation. Another factor that reduced the domestic demand for locally produced
goods was massive importation and smuggling of a wide range of foreign goods into the country
(CBN, 2002).
The observed developments were largely corroborated by the results of a country-wide survey
conducted by the Central Bank of Nigeria. The survey covered 684 manufacturing
establishments in 29 industrial groups and achieved a response rate of 56.6 per cent. The results
showed that overall manufacturing capacity utilization rate raised from 38.7 per cent in 1991 to
41.8 per cent. Eight of the twenty-seven industrial sub-groups that responded.
2.3 THEORETICAL FRAMEWORK
This study was guided bymany relevant theories and models.
2.3.1 Contingency, or Situational Management
There has been a fairly widespread tendency for certain scholars and writers in organization
theory to misunderstand the approach to management by those who emphasize the study of
management and its fundamentals (Bell, 2006). They see principles and theory as a search for the
one best way of doing things. For example, two scholars said:
49
In the past few years, there has been evidence of a new trend in the study of organizational
phenomena. Underlying this new approach is the idea that the internal functioning of
organizations must be consistent with the demands of organization task, technology, or external
environment, and the needs of its members if the organization is to be effective. Rather than
searching for the panacea of the one best way to organize under all conditions, investigators have
more and more tended to examine the functioning of organizations in relation to the needs of
their particular members and the external pressures facing them. Basically, this approach seems
to be leading to the development of a “contingency” theory of organization with the appropriate
internal states and processes of the organization contingent upon external requirements and
member needs (Bell, 2006).
In the same tone, another writer on management, one among many, appears to be concerned that
basic management theory and science attempt to prescribe a one best way of doing things and do
not take the situation into account. In an interesting book, this writer states:
Above all, the situationalist holds that there is no one best way to manage. Taylor may have been
right when he said there is one best way to perform a repetitive physical task, but that is not true
of planning, organizing, leading, controlling, or decision making. Different organizations with
different tasks and different competitive
2.3.2 Aggregate Planning Models
Aggregate planning, which might also be called macro production planning, addresses the
problem of deciding how many employees the firm should retain and, for a manufacturing firm,
the quality and the mix of products to be produced. Macro planning is not limited to
brewingfirms. Service organizations must determine employee staffing needs as well. For
example, airlines must plan staffing levels for flight attendants and pilots, and hospitals must
plan staffing levels for nurses. Macro planning strategies are a fundamental part of the firm’s
overall business strategy. Some firms operate on the philosophy that costs can be controlled only
by making frequent changes in the size and/or composition of the workforce. The aerospace
industry in California in the 1970s adopted this strategy. As government contracts shifted from
one producer to another, so did the technical workforce. Other firms have a reputation for
retaining employees, even in bad times. Until recently, IBM and AT&T were two well-know
examples (Fisher et al, 2002).
50
Whether a firm provides a service or produces a product, macro planning begins with forecast of
demand. How responsive the firm can be to anticipate changes in the demand depends on several
factors. These factors include the general strategy the firm may have regarding retaining workers
and its commitments to existing employees. Demand forecasts are generally wrong because there
is almost always a random component of the demand that cannot be predicted exactly in
advance. This assumption is made to simplify the analysis and allow us to focus on the
systematic or predictable changes in the demand pattern, rather than on the unsystematic or
random changes (Bell et al, 2003).
2.3.3 Contingency theory of capacity planning
In manufacturing organization, many important decisions are made in service activities. Capacity
planners decide when and with what resources organizations produce their outputs. The methods
that are used to create the plans are crucial to organizational performance (Kanet and Sridharan,
1998; Davis and Mabert, 2000; Zwikael and Sadeh, 2007). Poor methods yield plans that are
either too weak and result in excessive lead times or too tight and result in failures to keep
promised delivery dates. Consequently, it is not surprising that planning methods have
represented a major research area in the operations management literature. Different planning
techniques have been studied especially in analytical and simulation-based research (Kouvelis et
al, 2005). That stream of research has produced various sophisticated algorithms that enable the
leveling and optimization of capacity plans (e.g. Davis and Mabert, 2000; Yang, 2002; Deblaere
et al, 2007).
Meanwhile, however, empirical researchers have repeatedly observed that most practitioners use
considerably less sophisticated planning methods than what is discussed in the academic
literature (MacKenzie and House, 1978; McKay, 2002). Moreover, empirical evidence indicates
that those practitioners using advanced planning methods are on average less satisfied with their
plans than those who use simpler and less accurate methods (Jonsson and Mattson, 2003). This
section aims to use process complexity as a contingency factor that explains why the practices of
capacity planning often differ from the academic model of capacity planning.
The analysis of this section employs the logic of strong inference and the contingency theory of
organization to explain the determinants of different planning methods’ effectiveness. The
strong-inference logic refers to a research design, where theory building is based on tests of
competing hypotheses (Platt, 1964). The contingency-theoretical perspectives to process
51
complexity are used to propose that sometimes the most sophisticated planning methods may be
less effective than the simpler techniques. The contingency hypothesis is tested against a
hypothesis about the universal superiority of the most advanced planning methods. The statistical
results from the survey dataset are complemented by the interview dataset that sheds light on the
reasons why practitioners end up using certain planning methods.
2.3.4 Capacity Planning Supply Chain Model
Two major optimization problems in supply chain management are long term capacity planning
(static problem), and short term inventory control optimization (a dynamic problem). In capacity
planning, the entire structure of the supply chain – locations and sizes of factories, warehouses,
roads, etc is decided (within constraints). In inventory optimization, we take the structure of the
supply chain as fixed, and decide possibly in real-time who to order from, the order quantities,
etc. The challenge is to perform these optimizations under uncertainty (Berry et al, 2002).
A supply chain is a network of suppliers, production facilities, warehouses and end markets.
Capacity planning decisions involve decisions concerning the design and configuration of this
network. The decisions are made on two levels: strategic and tactical. Strategic decisions include
decisions such as where and how many facilities should be built and what their capacity should
be. Tactical decisions include where to procure the raw-materials from and in what quantity and
how to distribute finished products. These decisions are long range decisions and a static model
for the supply chain that takes into account aggregated demands, supplies, capacities and costs
over a long period of time (such as a year) will work (Berry et al, 2002)
From a theoretical viewpoint, the classical multi-commodity flow model (Buffaand Sann: 2007)
is the natural formulation for capacity planning. However, in practice, a number of non-convex
constraints like cost/price breakpoints and binary 0/1 facility location decisions change the
problem from a standard LP to an non-convex LP problem, and heuristics are necessary for
obtaining the solution even with state-of-the-art programs like CPLEX. Theoretical results on the
quality of capacity planning results do exist, and refer primarily to efficient usage of resources
relative to minimum bounds. For example, the total installed capacity can be compared with
respect to the actual usage (utilization), total cost with respect to the minimum possible to meet a
certain demand, etc.
52
The Supply Chain Model: Details
In a simple generic example, to design a supply chain network, location and capacity allocation
decisions were made. It had a fixed set of suppliers and a fixed set of market locations. There
was need to identify optimal factory and warehouse locations from a number of potential
locations. The supply chain was modeled as a graph where the nodes are the facilities and edges
are the links connecting those facilities. The model will work for linear, piece-wise linear as well
as non-linear cost functions (Ahmed, King, Parija: 2003).
In general the supply chain nodes can have complex structure. Two major classes were
distinguished: AND and OR modes, and their behaviour (Ahmed et al, 2003).
OR Nodes: At the OR nodes, the general flow equation holds. Here, the sum of inflow is equal
to the sum of outflow and there is no transformation of the inputs. The output is simply all the
inputs put together. A warehouse node is usually an OR node. For example a coal warehouse
might receive inputs from 5 different suppliers. The input is coal and the output is also coal and
even if fewer than 5 suppliers are supplying at some time, then also output from the warehouse
can be produced.
AND nodes: At the AND nodes, the total output is equal to the minimum input. A factory is
usually an AND node. It takes in a number of inputs and combines them to form some output.
For example a factory producing toothpaste might take calcium and fluoride as inputs. Output
from the factory can only be produced when both the inputs are being supplied to the factory.
Even if the amount of one input is very large, the output produced will depend on the quantity of
other input which is being supplied in smaller amounts. The flow equation for node C, if C is an
ANDnode will be as follows:
φCD = min (φAC,φBC)
The total cost of the supply chain is divided into 4 parts
1. Fixed capital expenses for the nodes: the cost of building the factory or warehouse
2. Fixed capital expenses for the edges: the cost of building the roads
3. Operational expenses for nodes
4. Transportation expenses for the edges (Ahmed et al, 2003)
The following notations are used in the model:
S = Number of supplier nodes
M = Number of market nodes
P = Number of products
53
X = Number of intermediate stages
Nx = Number of potential facility locations in stage x
E = Number of edges
PijC (Q) = Cost function for node j in stage i of the supply chain
PijC (Q) = Cost function for edge k of the supply chain
PijQ = Quantity of product p processed by node j in stage i
PijQ = Quantity of product p transported over edge k
Qij−max = Maximum capacity of node j in stage i
Qk −max = Maximum capacity of edge k
plmφ = Flow of product p between node l and node m
Fij = Fixed capital cost of building node j in stage i of the supply chain
Fk = Fixed capital cost of building edge k in the supply chain
uj = Indicator variable for entity j in the supply chain, i.e., uj = 1 if entity j is located at site j, 0
otherwise (Ahmed et al, 2003)
The goal is to identify the locations for nodes in the intermediate stages as well as quantities of
material that is to be transported between all the nodes that minimize the total fixed and variable
costs.
This minimax program is in general not a linear or integer linear optimization (weak duality can
be used to get a bound, but strong duality may not hold due to the nonconvex cost, profit
functions having breakpoints). The absolute best case (best decision, best demands and supplies)
and worst case (worst decision, worst demands and supplies) can be found using LP/ILP
techniques. It is stressed that even this information is very useful, in a complex supply chain
framework. However, note the following. The key idea in the approach was that linear
constraints were used to represent uncertainty. Sums, differences, and weighted sums of
demands, supplies, inventory variables, etc, indexed by commodity, time and location can all be
intermixed to create various types of constraints on future behaviour. Integrality constraints on
one or more uncertain variables can be imposed, but do result in computational complexities
(Ahmed, et al, 2003)
54
Given this, there are the following advantages of the approach:
(i) The formulation is quite intuitive and economically meaningful, in the supply chain
context. Many kinds of future uncertainty can be specified.
(ii) Bounds can be quickly given on any candidate solution using LP/ILP, since the
equations are then linear/quasi-linear in the demands/supplies/other params, which are
linearly constrained (or using Quadratic programming with quadratic constraints). The
best case, best decision and worst case, worst decision are clearly global bounds, solved
directly by LP/ILP.
(iii) The candidate solution is arbitrary, and can incorporate general constraints (e.g., set-
theoretic) not easily incorporated in a mathematical programming framework (formally
specifying them could make the problem intractable).
(iv) Multiple candidate solutions can be obtained in one of several ways, and the one having
the lowest worst case cost selected. These solutions can be obtained by:
(a) Randomly sampling the solution space: A feasible solution in the supply chain context
can be obtained by solving the deterministic problem for a specific instance with a
random sample of demand and other parameters. The computational complexity is that
of the deterministic problem only. A number of solutions can be sampled, and the one
having the lowest worstcase cost selected. While the convergence of this process to the
Min-max solution is still an open problem, note that our contribution is the complete
framework, and the tightest bound is not necessarily required in an uncertain setting.
(b) Successively improving the worst case bound.
1. A candidate solution is found (initially by sampling, say), and its worst case performance
is determined at a specific value of the uncertain parameters (demand, supply, …).
2. The best solution for that worst case parameter set is determined by solving a
deterministic problem. This is treated as a new candidate solution, and step 1 is repeated.
3. The process stops when new solutions do not decrease the worst case bound significantly,
or when an iteration limit has been reached.
In passing it is noted that the availability of multiple candidate solutions can be used to
determine bounds for the a-posteriori version of this optimization. How much is the worst case
cost, if an optimal decision is made after the uncertain parameters are realized? This is very
simply incorporated in our cost function CO, by using at each value of the uncertain parameters,
55
a new cost function which is the minimum of all these solutions. This retains the LP/ILP
structure of the problem of determining best/worst case bounds given candidate solutions.
C (Demands Supplies,….) =
min(C1 (Demands Supplies,…) C2 (Demands, Supplies,…)…)
These same comments apply for the inventory optimization problem also. Contrasting this with
the probabilistic approach, even if an optimal sets of decisions (candidate solution) is given, at
the minimum, the pdf's governing the uncertain parameters will in general have to be propagated
through an AND-OR tree, which can be computationally intensive.
2.3.5 Capacity Planning Model
In the previous section, the single period capacity planning problem was studied. In this section,
how to extend the single period model to a multi-period setting will be discussed. In practice, a
contract will have duration. In the existing literature that studies capacity contracts, there are two
different ways to model the duration of a contract. If the contracts require a long term
commitment, after the firm signs the contract to acquire capacity from its supplier, the firms
reserve or buy the same amount of capacity in each period until the end of the planning horizon.
On the other hand, if the contracts are short term, the firm can reserve different amounts of
capacity for different periods. For example, Ahmed et al, (2003), consider long term contracts
while Yazlali and Erhun use one-period short term contract.
In the context of the design of a new supply chain, the firm does not own the capacity itself but
reserves capacity from its suppliers. The contract does not need to be for either the short term
such as one period or the long term such as to the end of the planning horizon. The firm and its
suppliers can reach agreement on a duration that is beneficial to both parties. For instance, a
supplier might want to offer a contract with median duration and better price to encourage the
firm to commit. For the firm, signing a long term contract might be too risky; on the other hand
short term contracts might be too expensive. In this section, how the firm should plan its capacity
when it has the flexibility to choose the durations of the contracts will be examined(Ahmed et al,
2003)
2.3.6 Capacity Mathematical Model
In the single period problem, each contract can be specified with three terms: per-period unit
price of the fixed-price capacity, per-period unit price to reserve the option capacity, and per-
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period unit exercise price of the option capacity. In a multi-period setting, another specification
will be added, which is the contract duration. For example, a supplier quotes a three-month
contract with fixed-price $50, option reservation price $5, and option exercise price $50 to the
manufacturer. The manufacturer decides to reserve 100 units of fixed-price capacity and 20 units
of option capacity under this contract. It must pay the price of 100 units fixed-price capacity ($50
$100 = $5000) and 20 units option capacity ($5 $20 = $100) in each of the three consecutive
months starting with the first month of the contract. The manufacturer then has 100 units of
fixed-price capacity and 20 units of option capacity for each of the three consecutive months.
The prices of the contract can depend on the duration. To encourage a longer commitment, the
prices might decrease as the duration of the contract increases. In these situations, the multi-
period capacity planning problem involves another type of tradeoff between the flexibility (or
duration) of the contract and its price. Contracts with shorter duration have more flexibility while
contracts with longer duration offer lower prices (Ahmed et al, 2003).
Let T be the length of the planning horizon. Resource k offers contracts with durations in the set
Tk = {Tk,1, …, Tk,i, …}. To simplify the notation, we assume that for any resource all contracts
have different durations. This assumption can be relaxed and all the results still follow.
The first condition says a contract starts after the previous contract finishes. Condition 2
specifies that the manufacturer does not reserve capacity beyond the planning horizon. We call a
sequence feasible if it satisfies these two conditions. One implicit assumption here is that for
each period, we have only one contract active for each resource. In addition to deciding the
sequence of the contracts for each resource, the manufacturer needs to decide the corresponding
sizes: {ck,1, …, ck,i, …} and {gk,1, …, gk,i, …}. It is noted that it permit zero capacity contracts at
zero cost, which allows the firm to not use a resource for any subset of periods. The first contract
will cover the first two periods. Since the first two periods are cover by the same contract, the
fixed-price and total capacity reserved for each of these two periods are the same, which are c2
and g2. Similarly, a contract with duration 1 period is used to covered period 3 and a contract
with duration 3 periods is used to cover the rest of the horizon (Ahmed et al, 2003).
It was assumed that unfilled demands are lost and unused capacity cannot be saved for future
usage. It was also assumed that the manufacturer will not use any unused capacity to build and
store inventory. Even though we do not allow inventory, the multi-period capacity planning
problem is not separable since the firm can use a contract to cover multiple periods.
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It was assumed that the manufacturer needs to decide the sequence and sizes of the contracts for
each resource at the beginning of the planning horizon. To this extent, we also assume that it has
a demand forecast for each period at the beginning of the first period. In practice, capacity
decisions usually need to be made with a much longer lead time than the planning horizon. In
these situations, our two-stage decision process matches with the reality. Moreover, as was
discussed in the introduction, since the manufacturer doesn’t own the capacity, it is important for
it to secure the price and supply of the capacity by signing contracts at an early stage. However,
this is a restrictive assumption and it would be interesting to study the capacity planning problem
in a dynamic setting.
A strategy in multi-period problem contains two types of decisions: the sequence of contracts to
be used and the amount of capacity to acquire after choosing the sequence of contracts. There are
an exponential number of combinations of contracts that the manufacturer can choose from. To
evaluate one strategy, the firm needs to solve a large scale stochastic linear program, to find the
optimal contract sizes. Therefore, the multi-period problem is much more complex than the
single period problem (Ahmed et al, 2003)
In the following sections, we will develop an efficient heuristic algorithm that can find a good
capacity plan for the multi-period problem under assumption 1. The same heuristic algorithm
will also provide a good upper bound to verify the effectiveness of the capacity plan (Fisher et al,
2002).
2.3.7 Solving the General Multi-Period Problem
The main difference from the single period case is that the multi-period capacity planning
problem needs to decide the sequence of the contracts. The amount of capacity that needs to be
reserved depends on the contract sequence that the firm has chosen. If the sequence for each
process is fixed, finding the optimal contract sizes is a stochastic linear programming problem
that is very similar to the single period capacity planning problem (Fisher et al, 2002)
The difficulty of solving the multi-period problem lies in the fact that there are a large number of
combinations of contract sequences that the firm can choose from. The algorithm that we
proposed for the single period problem is effective, but it still requires a considerable amount of
computational power. Therefore, in this section an efficient heuristic algorithm for the general
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multi-period capacity planning problem will be developed under the assumption that each
process only has one dedicated resource.
The idea is to separate the decision of choosing the contract sequence from finding the optimal
contract sizes. The algorithm consists of the following steps:
1. Use the decomposition method proposed to separate the original multi-period capacity
planning problem into independent sub-problems, with one multi-period problem for
each process (Bell et al, 2003)
2. Solve each multi-period sub-problem to find a feasible contract sequence for each
process. This provides an initial feasible solution.
3. Fix the contract sequence for each process and then find the optimal contract sizes. This
provides an improvement to the initial solution.
Formalize the algorithm, and distribute the revenue of each product into each process based on
the prices of the contracts for the process. The same method will be use to separate the multi-
period problem. However, in the multi-period problem, each process has multiple sets of prices,
with one for each contract duration. Therefore, for each process, the average prices will be used
over all the contract durations in the decomposition method.
2.3.8 More Theories or Models of Capacity
Aggregate planning, which might also be called macro capacity planning, addresses the problem
of deciding how many employees the firm should retain and, for a manufacturing firm, the
quality and the mix of products to be produced. Macro capacity planning is not limited to
manufacturing firms. Service organizations must determine employee staffing needs as well. For
example, airlines must plan staffing levels for flight attendants and pilots, and hospitals must
plan staffing levels for nurses. Macro capacity planning strategies are a fundamental part of the
firm’s overall business strategy. Some firms operate on the philosophy that costs can be
controlled only by making frequent changes in the size and/or composition of the workforce. The
aerospace industry in California in the 1970s adopted this strategy. As government contracts
shifted from one producer to another, so did the technical workforce. Other firms have a
reputation for retaining employees, even in bad times. Until recently, IBM and AT&T were two
well-know examples (Fisher et al, 2002).
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Whether a firm provides a service or produces a product, macro capacity planning begins with
forecast of demand. How responsive the firm can be to anticipate changes in the demand depends
on several factors. These factors include the general strategy the firm may have regarding
retaining workers and its commitments to existing employees. Demand forecasts are generally
wrong because there is almost always a random component of the demand that cannot be
predicted exactly in advance. This assumption is made to simplify the analysis and allow us to
focus on the systematic or predictable changes in the demand pattern, rather than on the
unsystematic or random changes (Bell et al 2003).
Traditionally, most manufacturing firms have chosen to retain primary service in house. Some
components might be purchased from outside suppliers, but the primary product is traditionally
produced by the firm. Henry Ford was one of the first American producers to design a
completely vertically integrated business. Ford even owned a stand of rubber trees so it would
not have to purchase rubber for tires. That philosophy is undergoing a dramatic change, however.
In dynamic environments, firms are finding that they can be more flexible if the production is out
sourced; that is, if it is done on a subcontract basis. One example is Sun Microsystems, a
California-based producer of computer workstations. Sun, a market leader, adopted the strategy
of focusing on product innovation and design rather than on production. They have developed
close ties to contract producers such as San Jose-based Solectron Corporation, winner of the
Baldrige Award for Quality. Subcontracting is its primary producing function has allowed Sun to
be more flexible and to focus on innovation in a rapidly changing market (Hadley, 2002).
Capacity planning involves competing objectives. One objective is to react quickly to anticipated
changes in demand, which would require making frequent and potentially large changes in the
size of the labour force. Such a strategy has been called a chase strategy. This may be cost
effective, but could be a poor long-run business strategy. Workers who are laid off may not be
available when business turns around for this reason, the firm may wish to adopt the objective of
retaining a stable work-force. However, this strategy often results in large buildups of inventory
during periods of low demand. Service firms may incur substantial debt to meet payrolls in slow
periods. A third objective is to develop a production pan for the firm that maximizes profit over
the planning horizon subject to constraints on capacity. When profit maximization is the primary
objectives, explicit costs of making changes must be factored into the decision process (Hiller
and Lieberman, 2000).
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Capacity planning methodology is designed to translate demand forecasts into a blueprint for
planning staffing and production levels for the firm over a predetermined planning horizon.
Capacity planning methodology is not limited to top-level planning. Although generally
considered to be a macro planning tool for determining over all workforce and production levels,
large companies may find capacity planning useful at the plant level as well. Production capacity
planning may be viewed as a hierarchical process in which purchasing, planning, production, and
staffing decisions must be made at several levels in firm. Capacity planning methods may be
applied at almost any level, although the concept is one of managing groups of items rather than
single items. This section reviews several techniques for determining capacity plans. Some of
these are heuristic (i.e. approximate) and some are optimal. We hope to convey to the reader an
understanding of the issues involved in aggregate planning, knowledge of the basic tools
available for providing solutions, and an appreciation of the difficulties associated with
implementing aggregate plans in the real world (Buffa and Sarin, 2007).
The capacity planning approach is predicted on the existence of a capacity planning unit of
production. When the types of items produced are similar, a capacity planning production unit
can correspond to an average item, but if many different types of items are produced, it would be
more appropriate to consider capacity planning units in terms of weight (tons of steel), volume
(gallons of gasoline), amount of work required (worker-years of programming time), or dollar
value (value of inventory in dollars). What the appropriate capacity planning scheme should be is
not always obvious. It depends on the context of the particular planning problem and the level of
capacity planning required (Fisher et al, 2002).
The Aggregate Capacity Planning Problem
The goal of aggregate capacity planning is to determine aggregate production quality and the
levels of resources required to achieve these production goals. In particular translates to finding
the number of workers that should be employed and the number aggregate units to be produced
in each of the planning periods 1, 2…, T. The effective of aggregate capacity planning is to
balance the advantages of producing to meet divisions as closely as possible against the
disruptions caused by changing the levels of proportion and/or the workforce levels (Hadley,
2002). The primary issues related to the aggregate planning problem include:
1. Smoothing: smoothing refers to cost that result from changing production and workforce
levels form one period to the next. Two of the key components of smoothing costs are the
costs that result form hiring and firing workers. Aggregate capacity planning methodology
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requires the specification of these costs, which may be difficult to estimate. Firing workers
could have far reaching consequences and costs that may be difficult to evaluate. Firms that
hire and fire frequently develop a poor public image. This could adversely affect sales and
discourage potential employees from joining the company. Furthermore, workers that are laid
off might not simply wait around for business to pick up. Firing workers can have a
detrimental effect on the future size of the labor force if those workers obtain employment in
other industries. Finally, most companies are simply not at liberty to hire and fire at will.
Labor agreements restrict the freedom of management to freely alter workforce levels.
However, it is still valuable for management to be aware of the cost trade-offs associated
with varying workforce levels and the attendant savings in inventory costs (Hadley, 2002).
2. Bottleneck problems:the term bottleneck is use to refer to the inability of the system to
respond to sudden changes in demand as a result of capacity restrictions. For example, a
bottleneck could arise when the forecast for demand in one month is unusually high, and the
plant does not have sufficient capacity to meet that demand. A breakdown of a vital piece of
equipment also could result in a bottleneck (Hiller and Lieberman, 2000).
3. Planning horizon: the number of periods for which the demand is to be forecasted, and
hence the number of periods for which workforce and inventory levels are to be determined,
must be specified in advance. The choice of the value of the forecast horizon, T, can be
significant in determining the usefulness of the aggregate plan, if T is too small, then current
production levels might not be adequate for meeting the demand beyond the horizon length.
Ifit is too large, it is likely that the forecast far into the future will prove inaccurate. If future
demands turn out to be very different from the forecasts, then current decisions indicated by
the aggregate plan could be in correct. Another issue involving the planning horizon is the
end-of-horizon effect. For example, the aggregate plan might recommend that the inventory
at the end of the horizon be drawn to zero in order to minimize holding costs. This could be a
poor strategy, especially if demand increases at that time. (However, this particular problem
can be avoided by adding a constraint specifying minimum ending inventory levels) (Bell et
al, 2003).
In practice, rolling schedules are almost always used. This means that at the time of next
decision, a new forecast of demand is appended to the former forecasts and old forecasts might
be revised to reflect new information. The new aggregate plan may recommend different
production and workforce levels for the current period than were recommended one period ago.
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When only the decisions for the current planning period need to be implemented immediately,
the schedule should be viewed as dynamic rather than static (Fisher et al, 2002).
Although rolling schedules are common, it is possible that because of production lead times, the
schedule must be frozen for a certain number of planning periods. This means that decisions over
some collection of future periods cannot be altered. The most direct means of dealing with frozen
horizons is simply to label as period 1 the first period in which decisions are not frozen (Hadley,
2002).
4. Treatment of demand: as noted above, aggregate planning methodology requires the
assumption that demand is known with certainty. This is simultaneously a weakness and a
strength if the approach. It is a weakness because it ignores the possibility (and, in fact,
likelihood) of forecast errors. It is virtually a certainty that demand forecasts are wrong.
Aggregate planning does not provide any buffer against unanticipated forecast errors. However,
most inventory models that allow for random demand require that the average demand be
constant over time. Aggregate planning allows the manager to focus to the systematic changes
that are generally not present in models that assume random demand. By assuming deterministic
demand, the effects of seasonal fluctuations and business cycles can be incorporated into the
planning functions (Hiller and Lieberman, 2000).
Costs in Aggregate Capacity Planning
As with most of the optimization problems considered in production management, the goal of the
analysis is to choose the aggregate plan that minimizes cost. It is important to identify and
measure those specific costs that are affected by the planning decision (Abernathy and Wayne,
2004).
1. Smoothing Cost. Smoothing costs are those cost that accrue as a result of changing the
production levels from one period to the next. In the aggregate planning context, the most
salient smoothing cost is the cost of changing the size of the workforce. Increasing the size of
the workforce requires time and expenses to advertise positions, interview prospective
employees, and train new hires. Decreasing the size of the workforce means that workers must
be laid off. Severance pay is thus one cost of decreasing a decline I worker morale that they
result and (b) the potential for decreasing the size of the labor pool in the future, as workers who
are laid off acquire jobs with other firms or in other industries (Becker, 2006).
2. Holding cost: holding costs are the costs that accrue as a result of having capital tied up
in inventory. If the firm can decrease its inventory, the money saved could be invested
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elsewhere with a return that will vary with the industry and with the specific company. (A more
complete discussion of holding costs is deferred to the next chapter.) Hold costs are almost
always assumed to be linear in the number of units being held at a particular point in time. We
will assume for the purposes of he aggregate planning analysis that the holding cost is expressed
in terms of dollars per unit held per planning period. It will be also assumed that holding costs
are charged against the inventory remaining on hand at the end of the planning period. This
assumption is made for convenience only. Holding costs could be charged against starting
inventory or average inventory as well (Hiller and Lieberman, 2000).
3. Shortage costs. Holding costs are charged against the aggregate inventory as long as it is
positive. In some situations it may be necessary to incur shortages, which are represented by a
negative level of inventory. Shortages can occur when forecasted demand exceeds the capacity
of the production facility or when demands are higher than anticipated. For the purposes of
aggregate planning, it is generally assumed that excess demand is backlogged and filled in a
future period. In a highly competitive situation, however, it is possible that excess demand is
lost and the customer goes elsewhere. This case, which is known as lost sales, is more
appropriate in the management of single items and it’s more common in retail than in a
manufacturing context.As with holding costs, shortage costs are generally assumed to be linear.
Convex functions also can accurately describe shortage costs, but linear functions seem to be
the most common (Hadley, 2002).
4. Regular time costs. These costs involve the cost of producing one unit out-put during
regular working hours. Included in this category are the actual payroll costs of regular
employees working on regular time, the direct and indirect costs of materials, and other
manufacturing expenses. When all production is carried out on regular payroll costs become a
“sunk cost,” because the number of units produced must equal the number of units demanded
over any planning horizon of sufficient length. If there is no overtime or worker idle time,
regular payroll costs do not have to be included in the evaluation of different strategies (Hadley,
2002).
5. Overtime and subcontracting costs. Overtime and subcontracting costs are the costs of
production of units not produced on regular time. overtime refers to production by regular-time
employees beyond the normal work day, and subcontracting refers to the production of items by
an outside supplier. Again, it is generally assumed that both of these costs are linear (Fisher et
al, 2002).
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6. Idle time costs. The complete formulation of the aggregate planning problem also
includes a cost for underutilization of the workforce, or idle time. In most contexts, the idle time
cost is zero, as the direct costs of idle time would be taken into account in labor costs and lower
production levels. However, idle time could have other consequences for the firm. For example,
if the aggregate units are input to another process, idle time on the line could result in higher
costs the subsequent process. In such cases, one would explicitly include a positive idle cost
(Fisher et al, 2002).
Capacity Planning and Utilization
We focus primarily on techniques for determining the capacity requirements implied by a
production plan, master production schedule, or detailed material plans. One managerial problem
is to match the capacity with the plans: either to provide sufficient capacity to execute plans, or
to adjust plans to match capacity constraints. A second managerial problem with regard to
capacity is to consciously consider the market place implications of faster throughput times for
making products, at the expense of reduces capacity utilization. For example, JIT production
results in very fast throughout times for manufacturing products, but typically some capacities
are underutilized. Similarly, by scheduling the highest priority jobs through all work center –
taking explicit account of available capacity – it is possible to complete these jobs in much
shorter times than under more conventional MPC approaches. But this gain in speed for high
priority jobs comes at the expense of lower priority jobs throughout times and some
underutilization of capacity (Berry, Schmitt and Vollman, 2004).
This section is organized around five topics:
i) The role of capacity planning in MPC systems: how does it fit and how is capacity
managed in various manufacturing environments?
ii) Capacity planning and control techniques: How can capacity requirements be estimated
and capacity utilization controlled?
iii) Scheduling capacity and materials simultaneously: How can finite scheduling techniques
be applied, and what are the costs/benefits of these techniques?
iv) Example applications: How are techniques for capacity planning applied and what are
some best practices? (Karmaker, 2009).
Some of the techniques developed in this chapter are closely analogous to approaches for
demand management and operations planning. Finite loading techniques and the theory of
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constrains are described here as forms of capacity planning to produce detailed schedules
(Charkravarty and Jain (2000).
2.3.9 Capacity Planning Theory
There is a classic dilemma in maintenance work. If the maintenance people are busy the place is
not earning money. If they are not busy they are usually first on the redundancy list. Scheduling
of maintenance work exists against a background of unusual breakdowns, which have to be
accommodated in a hurry. The only 100% reliable way of managing this situation is to have
spare capacity either through sub-contracting or through re-deploying maintenance personnel to
other duties when not busy. This is very difficult unless routine scheduled maintenance
predominates. Another problem is the lack of outline scheduling information (standard methods
and times) for non-routine operations. A typical problem of this type of work measurement is the
establishment of loose standards, which if used to drive incentive schemes gives rise to serious
problems. As an aside: incentive schemes are no substitute for good supervision. However rule
of thumb time estimates and Rough Cut Capacity Planning is possible. Skills are the usual
resources that need to be scheduled, not plant. If Total Productive Maintenance is being utilized
scheduling becomes simpler because a higher proportion of the work is scheduled rather than
breakdown dominated (SM Thacker Associates, 2012).
Capacity Control
This again is a classic dilemma. Do we do the urgent first or the very urgent? Frequently a job
will be shelved to accommodate a more urgent one. This process can degenerate into very
cluttered workshops and high work-in-process stock holding. Running a strict good
housekeeping regime of operations control can alleviate this. The only satisfactory way to avoid
building unwanted work-in-process is to sue a simple form of input/output control i.e. do not
issue another job until the last is out of the way. One way which we have used to control work in
process is to restrict the number of work or kitting trolleys to one per individual so that they can
only be working on one job at a time. The trolley is used as a Kanban to request the next job
from stores, when the previous job has been started. Also it is common to hold some sub-
assemblies in work-in-process. Unless these require significant lead-time to assemble it is hard to
justify holding sub-assemblies and this situation often leads to cannibalizing one job to make a
more urgent job. Our advice is do not do it unless you really have to, and draw a Commonality
Tree to assess the need (SM Thacker Associates, 2012). The use of loading boards is common in
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this environment. More recently electronic loading boards with pick and place facilities are being
used. Skill management may be very important to maintain.
Other Important Aspect
Tools management is essential with Shadow Boards used to ensure tools can be located when
needed and in safety critical situations such as aircraft assembly it ensures that they are not lost.
The use of housekeeping techniques such as 5S’s is appropriate. Diagnostic skills and possibly
tools are required. These may be required to support remote diagnostic. Considerable effort may
be required to establish this infrastructure (SM Thacker Associates, 2012).
2.3.10 Theories of Performance
Performance occupies a key interface between organization behaviour, strategy and international
management. In organization behaviour the position of performance in the structural contingency
theories and research studies was marginal.
Organization behaviour is at the leading edge in developing a more substantial understanding of
performance. The structural contingency theory requires extensive revision. There are two major
areas of revision. First, to account for the hidden impacts on performance of the national context
of the firm. The hidden aspects include the roles of actor endowments (for example, raw
materials), the institutions and the market characteristics (for example, size, homogeneity and
speed of saturation). These hidden aspects impact on the performance of firms by creating a zone
of manoeuvre. Firms have to be aware of the zone, yet can enroll elements in the context which
reshape the zone. Second, it is important to be aware of the differences in approach between the
practices of auditing performance within firms from the concepts and theories used in
organization behaviour. Within firms of all kinds – public and private, commercial and custodial
– there are extensive arrays of performance data covering very diverse aspects. The financial
dimensions of the array are highly influential in constituting the recipe knowledge about strategic
directions. The influence of accountancy on the everyday understanding of performance is
significant, but should be closely scrutinized. The aim is to develop a theory which links
organizational learning to the selective usage of performance measures, in particular, to explain
the role of intangible assets but undertaking these revisions is a major challenge (Sorge, 1991).
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Key Interface
There are six reasons why performance is a key interface. First, there has been a significant shift
in the definition of best practice organization from mass standardization into mass customization
(Pennings, 1975). The practices of large US firms were regarded as the only relevant exemplars
worth emulating. The international success of firms from Japan, the Pacific Rim and Germany
demonstrates that definitions of best practice should include features which are found in those
nations. Second, political elites and corporate leaders give increasing attention to the use of
market and quasi-market mechanisms. This stimulates the auditing of performance for the parent
organizations and its sub-units. The latter are placed in quasi-market situations. This reflects the
shift from a producer-anchored capitalism to one in which consumerism is central. Third,
information technology greatly facilitates the collection of data about performance. Computer
modeling and the application of multi-dimensional frameworks display the complex way in
which various processes contribute to performance. Fourth, the professional associations
connected to accounting and to information services gain high fees and rents from developing
and diffusing measures of performance for a wide range of organizations. Fifth, firms wish to
develop their own recipe knowledge about the dynamics of performance. Finally, external
stakeholders monitor selected dimensions of performance. Ecological interests monitor the level
of pollutants and the use of scarce resources (Pennings, 1975).
Structural Contingency Theory
Organization behaviour is influenced by the forms of law-like knowledge constituted in
economics. Industrial economics concentrated upon the strategic relationship of the firm to its
economic context, especially in the choice of sector in the positioning of the firm within the
sector (Porter, 1990). Economics treated the organization structure, key processes and the
transactions with the context as a black box. Opening the black box began in the late 1950s
(Pennings, 1975; Burns and Stalker, 1994). Organization behaviour was founded on the claim
that its structural contingency theories of organization design provided a highly effective
approach to achieving high performance (Nystrom and Sharbuck, 1986).
In the late 1950s there were two contending theories to explain the differences between
successful and unsuccessful performance as assessed by survival and profitability. One theory
emphasized universal solutions to be applied everywhere. The autonomous work group was
widely promoted for every kind of organization and social movement (for example, kibbutz).
The alternative theory was derived from systems thinking. The theory of equifinality shows that
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high performance is achieved by different routes. Some firms might decentralize decisions while
also increasing the degree to which procedures were formalized. Other firms could transfer
control of professional specialists (Pennings, 1975).
Organizational behaviour established a third approach by selectively adapting systems thinking.
The contingency and congruence fit perspective specified the conditions under which given
structural solutions led to varying performance. Burns and Stalker (1994) connected the viability
of any organization to its ability to match the degree of variety in its environment with internal
mechanisms for encoding the variety and activating solutions from its repertoire. Their theory of
performance is evolutionary. Firms that chose strategies and structures without congruence to
their environment with underperform.
After the 1970s the connection between organization behaviour and strategy was established.
The linear, four-step and quasi-rational approach is an outside-in approach. From the
organization behaviour perspective the environment is analysed to ascertain the degree of
complexity and ambiguity and their stability or otherwise in the future. This information guides
the design of organization structure. The design of organization is also shaped by strategic
decisions, because those will influence features such as the economies scale and scope. In the
strategy version (version 3) the environment is analysed by searching for those sectors which are
expected to provide the most favourable sources of profits in the future. The pharmaceutical
sector was desirable in the 1980s, but less so in the 1990s. Within any sector there are wide
variations in profitability. The aim is to select a position. Key choices are whether to be a firm
which provides low-cost commodities or provides items which are differentiated from one
another. Currently the strategic approach is being challenged by organization studies (Pennings,
1975).
Six problems arise with the assumptions underpinning Figure and its usage of structural
contingency approach to performance. First, the approach assumes that firms can move from
sector to sector without friction, in a manner similar to the economists’ theory of frictionless
adjustments of the markets. In practice most firms can only alter their repertoire rather slowly:
television producers cannot easily move into tourism. Second, the approach assumes that
knowledge about the best positioning to achieve high performance will be acceptable to the
political groupings. Yet, firms are milieus of political bargaining and are set in contexts where
external stakeholders may exert influence). For example, the credibility of a firm to its financial
69
community is crucial. Third, performance is treated as end-variable with only small feedback
inputs into the state of the firm. Performance is and should be a source of scrutiny, sense-making
and the foundation for developing new strategies. Past performance is the foundation for
developing new strategies. For the four sequential steps are too rigid and do not allow for
iterations, abortions and variations in the decision process. Fifth, the treatment of the external
context is too narrow (Nohria and Eccles, 1992) and too close to the immediate context of the
firm. There is neglect of impact of symbolic forces and of the hidden influence of the national
context (Sorge, 1991). Sixth, the measurement of performance in research studies intended to
support the structural contingency perspective is mainly based upon financial data (for example,
profits) measured as a cross-sectional slice describing the past. Reviews typically conclude that
research studies relied upon inconsistent operational definitions and simplistic measurements
(Pennings, 1975).
The limits of the structural contingency theory of performance are handled in two major
revisions. First, to include the national context. Second, to develop a processual, learning theory
of performance.
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Figure 2.1: System Theory of Performance
Source: Pennings, J.M. (1975), “The relevance of the structural-contingency model for
organizational effectiveness’, Administrative Science Quarterly 20 (3)
Analysis of
environment
Strategy
formulation
Organization
design and
implementation
Desire
performance
Levels of
uncertainty,
complexity and
variability
Design of
organization
Implementation
of design
(Assumed) Desire
performance
Economic data for
national and world
Structural patterns
New technologies
Profit streams
Choice of sectors to
enter and to
position in sector
Firm as a portfolio
(Assumed that
the firm adjusts
the organisation)
Desire
performance
1
2
3
Key 1. Four steps in model
2. Version of model
found in organization
studies
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Hidden Influence Theory
Variations in the performance of firms are explained by features within the national theories. The
national context provides an envelope of opportunities and constraints. It is unlikely that Henry
Ford could have achieved such large sales for the Ford T between 1908 and 1926 if he had
started in the UK or France. It is also unlikely that the successful performance of the Italian firm
Benetton could have been achieved after 1970 if they had started up in the UK because of the
existing structure of the market for clothing. To survive and grow all firms require an envelope
of opportunity within which there are the necessary resources and markets for the outputs. These
envelopes tend to influence clusters of firms at the level of the sector. There are two theories
which reveal the hidden contextual factors in the nation of origin: the theory of societal
institutions and elective affinities (Sorge, 1991); the six-factor theory of competitive advantage
(Porter, 1990).
International comparisons reveal that some sectors do better in certain societies. For example, the
German automobile sector has performed better than the British automobile sector. How
important are the institutions within a nation through which knowledge is brought into play
within firms? The theory of elective affinities (Sorge, 1991) seeks to demonstrate that the
German success and the British failure is explained by the interaction between the institutions of
knowledge management and the strategic directions chosen by a nation’s firms. In the German
case there is a positive elective affinity because the management of knowledge creates highly
competent mechanical engineers, a connected hierarchy of skill within the firm and the strategic
choice of producing cars that are distinctive rather than produced as commodities. The theory of
elective affinities redefines the role of the structural contingency theory. The theory of elective
affinities is not deterministic in the relationship between institutions and firms, leaders
sometimes choose strategic directions which have the worst affinity with the societal institutions.
The theory of the competitive advantage of nations (Porter, 1990) locates the societal institutions
of knowledge management in the context of six interacting factors: the endowed factors, the
degree of rivalry between firms, the role of government, chance, the role of support sectors (for
example, design, and marketing) and the influence of the national market. The role of the
endowed factors deserves attention because their absence or presence exerts an influence.
Endowed factors include: raw materials, soil, rivers and climatic features. The USA has a
historical abundance of endowed factors for agriculture, but a historical shortage of labour
(compare Japan). In the USA there were many kinds of hard and soft wood which could be used
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for many purposes: making firs, building houses, fences, making equipment. One of the early
developments in the USA was the fast running saw-milling machines which wasted vast
quantities of wood and substituted technology inputs for labour inputs. These early developments
to technology became cumulative and defined a trajectory or path of development (Sorge, 1991)
The influence of the national market is worthy of close examination. National tastes differ
significantly. The Japanese prefer household consumer goods which are small, multi-functional,
portable, possess fine surfaces, are packaged delightfully and are accompanied by extensive
information. The Japanese market for consumer goods tends to be homogenous, very conscious
about newness and quickly saturated. To survive, the Japanese firms have created many variants
of products (for example, Sony Walkman) through short design cycles (Sorge, 1991)
Organizational performance is significantly shaped by the national context. Domestic firms
experience endowed factors, using their domestic market as a major context for learning, and
tend to become shaped to its characteristics. This process is referred to as ‘entertainment’ and its
influence affects the ability of firms to cross the borders from their nation of origin. There are
zones within which firms can choose to manoeuvre, and to some degree firms can reconstitute
features which might be unfavourable in the longer run. The ability of firms to manoeuvre is
influenced by how they learn from their performance.
2.3.11 Theories of Manufacturing
Manufacturing and national economies
Wealth and manufacturing theory
Wealth may be categorized into two types; namely: natural wealth and man-made wealth.
Natural wealth is derived from crude materials, that is, materials occurring in the natural state
such as mineral deposits in the earth’s crust and agricultural products. Natural wealth especially
that based on mineral deposits is delectable. Also, wealth obtained from agricultural products
without man-made inputs is unsustainable in modern times. Natural wealth is fate-dependent and
its location can hardly be influenced by man. On the other hand, man-made wealth is one derived
from refined or manufactured products, in which man exercises enormous control. This type of
wealth is usually sustainable. In this case, the wealth usually is created by manufacturing
(Ibhadode, 1993).
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Manufacturing is concerned with the production of goods, where a good is a tangible entity,
Vollmann et al, 2000). Manufacturing is undoubtedly one of the most important sectors of
national economies, as it creates wealth. The tendency is most societies are to create more
wealth. And the more volume of manufacturing activities, the more plentiful the wealth produced
(Vollmann et al, 2000).
Manufacturing is a productive system that may be defined as a process for converting resource
inputs into goods and waste products. The inputs to the system are energy, materials, equipment,
labour, information and other capital-related inputs (infrastructure). The inputs are converted to
outputs by the process technology, which is the particular method used to transform the various
inputs into outputs. Changing the process technology alters the way one input is used in relation
to the other and it may also change the outputs produced.
The outputs produced include useful products (goods), and waste products. Waste production is
inevitable as a consequence of the generalized results of the second law of thermodynamics. It is
characteristic of processes including human life that they take in suitable raw materials and
convert them into products of value. In doing so, they must produce waste materials (Ibhadode,
2006). This is inevitable even under clean technology.
The process technology in a manufacturing system is usually effected by means of machines and
equipment along with the processing methodology. The body of knowledge concerned with the
optimal combination of machinery, materials, and methods needed to achieve economical and
trouble-free production is referred to as manufacturing engineering (or manufacturing technology
when used loosely and interchangeably). It combines field experience and special engineering
research with concepts of fundamental and applied sciences to solve basic and specific
manufacturing problems. Manufacturing technology techniques are of immense importance to
modern industries where inconceivable machines are produced from elementary materials such
as blanks using basic specific manufacturing processes. In conjunction with engineering
management techniques, it ensures the most efficient use of materials and labour. It curtails
wastes and ensures the use of the right processes for each operation in the production of a
product or component. It also ensures the most effective method of handling and assembly of
components to form specific products (Ibhadode, 2006).
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Importance of Manufacturing to National Economies
Engineering manufacture is, undoubtedly, one of the most important sectors of industry. It
provides machines of different purposes to the economy. The economic and industrial growth of
a nation is largely dependent on the development of engineering industry. Food, clothing, shelter
and all the benefits of civilization determine how well a people live. How well a people live
depends on how much it produces is determined by its level of manufacturing activities
(Ibhadode, 2006).
The global economy has become knowledge and technology-driven. While innovation and rapid
technological changes are the reasons for unprecedented prosperity and growth in industrialized
countries, many developing countries and countries with economies in transition are risking
marginalization by being trapped in the technology-divide and investment gap. Research and
development (R&D) and innovation-intensive products are increasingly driving world trade.
According to a UNIDO report in 1998, high – and medium – technology products accounted for
63.6%. 67.8% and 53.8% of manufactured exports of world developed economics and
developing economies respectively. Regrettably they accounted for only 12.7% of manufactured
exports from Sub-Sahara African countries. This poses serious industrial and economic
development challenges to the Sub-Saharan region (World Bank, 2008).
To prove further that manufacturing drives the economy, despite the endowment of large
reserves of oil in the major oil producing countries, their GDP, per capita income, per capita
value added in manufacturing and longevity are 3%, 14%, 7% and 28% respectively of those for
the G7 countries. The contribution of manufacturing to the GDPs of the major oil producing
countries is at about the same level as for the world’s 20 poorest countries (World Bank, 2008).
The Manufacturing Sector in Nigeria
The Nigerian economy is in a precarious state. The manufacturing sector seems to be hardest hit.
While the mean contribution of manufacturing to GDP in the world’s 20 poorest countries was
9% in 2003, that of Nigeria was only 4%. Further, whereas the world’s 20 poorest countries had
a mean per capital value added in manufacturing in 2003 of $22, Nigeria had only $16. The
picture is even worse when Nigeria is compared with the 5 oil-producing nations.
The Manufacturers Association of Nigeria (MAN) has given the current status of the
manufacturing sector in Nigeria and is summarizes as follows:
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i. The contribution of manufacturing to the Gross Domestic Product (GDP) has been on
persistent decline over the years from 8.2% in 1990 to 4.7% in 2003.
ii. At independence in 1960, the contribution of the manufacturing sector to GDP was
3.8%. Notwithstanding the growing contribution and dominance of the oil sector
since the 1970s, manufacturing recorded impressive performance and contributed on
the average was consistently above the 70% mark. The situation has changed
dramatically. Industrial capacity utilization dropped to a paltry 48.8% in 2003.
Currently, 30% of manufacturing companies have been closed down, 60% are ailing
and only 10% are operating at sustainable level. Sectorially, the companies operating
at sustainable level are food, beverages and tobacco; leather, pharmaceuticals and
Household products. (Soaps, detergents, toothpastes, cleaning materials). Companies
in the ailing category include Textile firms, Vehicle assemblers, cable manufacturers,
paint manufacturers, steels and petrochemicals (Ibhadode, 2006).
Companies in the ‘closed down’ category cut across all industrial products but most affected are
products such as chalk, candle, dry cells and automotive batteries, shoes polish, matches, etc.
MAN gives the following as constraints to the manufacturing sector:
i. Sector is highly importing dependent.
ii. Hampered by policy inconsistencies.
iii. Besieged with multiple taxation.
iv. Burdened with weak infrastructural base and ineffective public utilities.
v. Tormented by acute funding problems, weak capital base as well as high cost of fund.
vi. Inundated with fake, counterfeit and substandard imported products.
vii. Burdened with poor sales partly as a result of low purchasing power of the citizenry.
viii. Bugged down with delay in clearing consignments due to existence of multiple
inspection agencies at the ports (World Bank, 2008).
This state of affairs is lamented! Engr. Charles Ugwuh, President of MAN, has said that the
manufacturing sector is ‘threatened with collapse due largely to deficiencies in infrastructure,
lack of appropriate funding and other policy inconsistencies and frustrated implementation of
otherwise well intentioned strategies” he added that “the sector can make enormous contribution
to the growth of Nigeria if the necessary drivers and vital investment in energy, petrochemicals
and human capacity can be made to uplift the level of value – addition’ Furthermore, the
Engineer advised that: “the federal Government must muster the courage to make the vital
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investments (in partnership with the private sector) that add value to our natural resources of
crude oil, gas and solid minerals in a way that multiplies the national wealth so all Nigerians can
share the prosperity within our reach. He summed up with the epigram:
“Poverty has no place in Nigeria”.
Re-Manufacturing
1. Materials Planning
This breaks down into 3 parts:
1) Managing the supply of units awaiting salvage
i) Sufficient stock must be maintained to support underlying demand for
reconditioned units.
2) Managing the stripped component stock to keep balanced sets of parts for rebuild
(Vollmann et al, 2000)
i) Using new items instead of salvaged items is costly. So in order to
maintain components it may be necessary to strip further units. Yields
must be used as an input to calculate material requirements. Ultimately
imbalances are bound to occur. In this case an occasional purge may be
required to restore the balance, by either throwing away surpluses or
buying new components depending on the economics of doing so
(Vollmann et al, 2000).
3) Managing the rebuild
i) Because there is a greater volume, medium to large batch rules apply with
many similarities to original production / assembly operations. Forecasting
is easier and there is more repetition. Systems may be appropriate where
demand can be forecast with some certainty. We have encountered
situations were a negative bill of material was constructed to
accommodated yields expected from salvaged units which were then
offset against the requirements for remanufacture. This method was later
abandoned in favour of change of manufacturing strategy where salvaged
units were stripped as soon as possible to determined availability of good
components.
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4) Generally Re-order Point techniques are most appropriate for forecasting demand,
with blanket orders / schedules for repetitious component requirements (SM
Thacker Associates, 2012).
2. Materials Control
Call offs are more likely to be via Kanban control because of increased repetition. It is
vital to monitor yields in this situation to ensure that the correct numbers of un-salvaged
stocks are sufficient to satisfy demand. Re-manufacturing creates a special problem for
lot traceability. If a part has been recycled and it fails, what is the cause of the failure? Is
it the original manufacture or the recycling process?
3. Capacity Planning
Because more time standards on work content are available (however informally)
estimating jobs is easier. Because processes are more predictable Routes (Routings) can
be established to use in shop loading. Because there is repetition, demand is also
smoother. The combined effect of these factors makes capacity planning easier. The use
of level scheduling is recommended (SM Thacher Assocaites, 2012).
4. Capacity Control
Because demand is smoother and more repetition is present, skills management is less
important and in fact more deskilling or automation may be possible. Switching effort to
stripping rather than rebuilding can accommodate troughs and conversely reducing
stripping to satisfy immediate demands can accommodate peaks SM Thacher Assocaites,
2012).
5. Other Important Aspects
Sometimes the organization may be slightly schizophrenic, flipping from job shop to
volume producer. At this point it is worth considering some method of segmentation
along resource utilization lines.
2.4 EMPIRICAL REVIEW
Empirical Review of the effect of Capacity Planning on Performance
Bell et al (2002) worked on how to improve the distribution of industrial gases with online
computerized Routing. He observed that distribution was very important in materials
management. Other important materials management activities included, purchasing, production
and inventory control, storage and warehousing and distribution. Distribution had to do with
transportation of raw materials, parts, sub-assemblies, semi-finished goods and finish goods from
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the point of production until they get to the final consumers. He found that the starting point in
distribution is to know the capacity or the production capability of the goods that are being
transported. Capacity planning became very important in distribution if corporate objectives had
to be achieved so capacity planning as a distribution function had a positive effect on
performance in many goods industries.
Relationship between capacity requirements planning and materials requirements
planning, using work in progress
Karmarker (2009) worked on capacity loading and Release Planning with work in progress and
lead times. The main objective of his study was to determine the nature of the relationship
between capacity requirements planning materials require planning using work in progress.
Materials requirements planning is a method of coordinating detailed production plans and it is a
multi-stage process which begins with a master’s schedule and works backwards to determine
when and how much components will be needed. It gives the time for placing orders and when
the other aids is required considering the limited time.
Capacity requirements planning give the capacity of the materials that are required both at the
ordering and receipt stages. It utilizes the time-faced materials plan information produced by a
material requirements plan system. This includes consideration of all actual lot sizes as well as
lead times for both open-shop orders (scheduled receipts) and others plan for future release
(planed orders). Karmerker found that there was a positive relationship between capacity
requirements planning and materials requirements planning in the manufacturing industry in Los
Angeles, United States of America.
The extent to which capacity planning sustains organization’s competitive advantage
Whelan (2012) did a study on who to initiate capacity planning and management process for a
rapid deployment unit of a security services company. Support Services Group Limited was
founded in 2000 to provide security and risk management services for companies within the
United Kingdom. The enterprise has encountered rapid development and growth which lead to
establishment to Rapid Deployment Service Product. The product was created to answer sudden
and volatile demand by correlating supply.
The capacity for the supply has not been planned or managed coherently creating a call for a
research on how such as process could be initiated. The purpose of the thesis is to determine the
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extent to which capacity planning sustains the organization’s competitive advantage. The
research was carried out as case study. The database of the company provided the information
which was then analyzed using a mix of both qualitative and quantitative methods. It was found
that to a large extent capacity planning sustains the organization’s distinctive competence which
is the competitive advantage which the company has over its competitors.
The relationship between capacity planning and capacity building
S.M Thacher Associates (2012) did a study on the extent of the relationship between capacity
planning and capacity building. Manufacturing Planning and Control (MPC) is often seen as
encompassing two major activities: planning/control of materials and planning/control of
capacities. The two need to be coordinated for maximum benefits, on the basis of managerial
perceptions of what is required in the marketplace. Capacity planning techniques have as their
primary objectives the estimation of capacity requirements, sufficiently far enough into the
future to be able to meet those requirements. A second objective is execution: the capacity plans
need to be executed flawlessly, with unpleasant surprises avoided. Insufficient capacity quickly
leads to deteriorating delivery performance, escalating work-in-progress inventories, and
frustrated manufacturing personnel. On the other hand, excess capacity might be a needless
expense that can be reduced. Even firms with advanced MPC systems have found times when
their inability to provide adequate work center capacities has been a significant problem. On the
other hand, there are firms that continually manage to increase output from what seems to be a
fixed set of capacities.
In the case of capacity building, the intention is to provide an enabling environment to enable
capacity planning to be properly handled. This will involve the factors and variables that will
enable the managers to be able to determine the present and future production capability of the
facility. In their study, S.M. Associates found out that to a large extent, there was a positive
relationship between capacity planning and capacity building in a case study of a manufacturing
firm in Los Angeles.
Steps towards developing capacity plan to improve the profitability in the manufacturing
sector
Farwell (2012) did a study to determine the steps of capacity planning to improve the
profitability in a manufacturing industry in a study Ohio in the United States. The steps included
determining the present capacity needs of the company, comparing the present and future
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capacity needs. If the present capacity needs are more than the future needs, then the company
could do so contracting out or outsourcing of some of its jobs. But if the future capacity needs
are more than the present capacity needs, then the company could run shifts, increase capacity or
build a new plant.
The last step is to implement its decision and Farwell (2012) found that there was a positive
relationship that the steps towards developing capacity plan if properly implemented could
improve the profitability in the manufacturing industry in areas studied.
2.5 CAPACITY MANAGEMENT AND PLANNING
The most successful events and actions in the world are primarily the outcomes of coherent and
linear planning. In order to excel, one must point out the steps towards success with awareness.
The actualization of competences, resources and capabilities will widen the viewpoint so it
comes clear where the development leads to and which tools are to be possessed and/or used at
which point of the way.
Capacity planning is the perspective of businesses to map out their capabilities. Therefore,
capacity planning is the one of key performance elements of a functional business. When
executed through careful and considerate calculations, capacity planning can be the sole
ingredient which would make the profits of an enterprise boom. However, companies which do
not focus enough on management of strategic capacity planning will encounter serious
difficulties or even disastrous problems especially during a growth or starting period.
Strategic Capacity Planning
The word capacity normally defined in Business dictionary as “specific ability of an entity
(person or organization) or resource, measured in quantity and level of quality, over an extended
period.” (Business Dictionary.com, 2011)In other words, capacity refers to the skill to hold,
receive, store, or accommodate. In general business logic, it’s often viewed as the amount of
output that a system is capable of achieving over specific period of time. (Jacobs and Chase,
2008)
Capacity also refers to the limitation which the operating element is able to process; the amount
of services executed or tangible products produced. The vital elements and considerations
needed to be taken into account before-hand are what type of capacity – whether it’s equipment,
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space or human skills – are needed, how much of it is required and the timeframe of when those
factors are to be accessible. (Beamer, 2010)
Professors F. Robert Jacobs and Richard B. Chase (2008) define strategic capacity planning as an
approach tactic which is the idea of determining the total levels of capacity for resources
mentioned above. The common feature of these resources is the scarce and the finite nature,
which is normally described as “capital-intensive” but also the availability in terms of – often
placed secondary in importance – time reflecting the reasons why planning is necessary. (Jacobs
and Chase, 2008; Hope and Muhlemann, 1997)
Purpose of Capacity Planning
The main point for an organization to perform plan capacity usage in advance is to match its
supply competence and capability levels with the predicted demand by the customer. Capacity
plan is formed to support the company’s main competitive strategy and it has to be inline and
correlate with it. The accuracy of the capacity plan is in sync with the company’s ability to
actualize their capabilities enabling them to have precise respond to the needs of the customer.
Should the situation be so that the demand is too excessive, through a detailed plan it is easy to
seek out the required steps which are to be done in order to satisfy the demand. Insufficient or
otherwise inadequate capacity may turn out to be costly for the company as unpleased customers
are lost and such a market attracts competition faster.
Capacity measurement definition
Capacity as a term is in directly aimed at the rates of output of the operations in question. The
output is normally indicated through a rate which presents the amount of deliverables completed
in a period of time. For a small pub, a fairly demonstrative measurement could be for instance
drinks sold in a day. The actual output rate gives a mere indication of the daily result. In order to
assess the rate further to determine the actual effectiveness, two capacity efficiency performance
indicators are to be used. Those indicators alongside the formula are presented in the equation
below:
capacityDesign
output Actual n Utilizatio
capacity Effective
output Actual Efficiency
=
=
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The efficiency ratio expresses the give day output of the pub in correlation to the best possible
daily rate. The effective capacity is a measure which the process was designed for, but which can
be realistically expected as a result; while taking into consideration miscellaneous factors which
keep the process reaching its peak due to their inevitability. Examples of such in the given
surrounding could be maintenance, personnel breaks, etc. Design capacity is the best possible
level for an operation, process or a facility to deliver. In brewing, the design capacity reflects the
volume of output with a minimized cost of an average unit. Ideally, the design capacity – also
known as best operating level – would be in direct link to productivity but as various
uncontrollable reasons prevent operations to reach maximum capacity, it is vital to use both
indicators as results lead to different interpretation. Both of the ratios are normally expressed in
percentages and give an idea of improvement needs. Efficiency ratio shows how effective we are
in terms of productivity whereas utilization ratio indicates the need for improvement within the
process itself. Evidently, where there are bigger the gaps – the resulting number being a lot less
than 100% - between the figures, lie a greater opportunity for an improvement. Low percentage
value in efficiency indicates insufficient variable resources such as employees who required
more training or orienting and a low value in utilization indicates the problems within the actual
process for instance the bar tap needing maintenance frequently. (Jacobs and Chase, 2008;
Beamer, 2010)
Capacity decisions
By their nature, capacity decisions are generally strategic involving investments and therefore
commitment in resources such as equipment, buildings and manpower. In light of this factor,
capacity decisions affect greatly into a myriad of organizational functionality. These decisions
have an enormous impact on the ability to meet the future demands for the goods an organization
is offering. Costs are widely influenced by capacity decision as operating costs are larger when
there are investments in resources. Additionally, the initial cost of the product is determined by
the unit cost which is normally a direct derivation from the costs of the capacity used. Other
areas which are affected are the ease of management; better capacity, easier to manage, and
competitiveness of the company. Coming to the 21st century, globalization has added its share
into the capacity decision mix by highlighting the importance as the markets and competitors are
operating in a global scale and increasing the complexity. All these reasons emphasize the need
to plan these crucial choices in advance. Capacity decisions can be divided into three categories.
Long-term, medium-term and short-term. (Hope and Muhlemann, 1997; Beamer, 2010)
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Long-term capacity decisions
Long-term capacity decisions are made in a timeframe of greater than a year. Including top
management participation, long-term plans concern productive resources which take a longer
time to acquire and/or dispose. These resources include for instance buildings and facilities.
Because of the gravity of these choices, they are to be done in the knowledge of the external
factors which could affect the decision-making. Such components are markets, major
competitors and PEST (political, economic, social and technical) environment. Long-term
capacity decisions are made to either increase or reduce capacity.
Medium-term capacity decisions
Whereas long-term capacity decisions can be considered as the macro viewpoint of capacity
planning, medium-term takes care of the micro view. The timeframe here is from 6 to 18 months.
However, depending on the organization medium-term and short-term capacity decisions do not
have a clear separation but are carried out while linked to each other. Therefore, medium-term
range can include timescale as specific as weeks. The focus point here is on “softer” resources
which include human resources and minor equipment purchasing. Decisions made within this
concept are executed in order to match the supply with demand. To do this, the company can
either adjust the supply of resources or the demand. (Jacobs and Chase, 2008; Hope and
Muhlemann, 1997)
Adjusting the demand is highly complex activity. Sudden changes in demand are somewhat
impossible to foresee. Organizations can try to manipulate demand through marketing.
Aggressive commercials entice people in to consuming more. Other attempts to affect the
demand could be two-for-one offers at restaurants during a specific day of the week, which try to
lure in customers when business is usually slow. The practice is known as yield management;
predicting and altering demand to maximize revenue. Yield management is applied in situation
which required determining the best possible price and timing of a capacity to the most suitable
customer segment. Offering a family holiday week at a resort during school summer vacation at
a price affordable by most families with children to get peak levels in sales is a fine example of
yield management (Hope and Muhlemann 1997; Jacobs & Chase, 2008).
Possibilities to adjust capacity to meet the demand are deeply associated with the flexibility of
the resources. Flexibility refers to the ability of the organization’s capacity to adapt to changes;
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multi-skilled employees, overtime, having alternatives to material resources or speeding up the
use of physical resources (libraries limiting the use of computer/internet per customer).
Concentration on one aspect of resources being flexible might not be the best choice to act by.
Most brewing companies perform a mix of human, physical and material resources in terms of
flexibility.
Short-term capacity decision
As mentioned before, short-term capacity decisions are strongly linked to medium-term
decisions. The timescale is less than one month. The decision might even be made for daily or
weekly scheduling. Short-term capacity decision could be presented as fine-tuning of medium-
term decisions. The capacity, determined and acquired through medium-term plans, is made to
match the demand by eliminating variance between planned and actual output. The flexibility of
the resources are put to test here as sudden changes in demand require rapidly executed moves
through such means as employee overtime and alternative production routines (Jacobs and
Chase, 2008; Hope and Muhlemann, 1997)
2.6 BREWING
Brewing is the production of beer through steeping a starch source (commonly cereal grains) in
water and then fermenting with yeast. It is done in a brewery by a brewer, and the brewing
industry is part of most western economies. Brewing has taken place since around the 6th
millennium BC, and archaeological evidence suggests that this technique was used in most
emerging civilizations including ancient Egypt (Arnold, 2005).
The basic ingredients of beer are water; a starch source, such as malted barley, which is able to
be fermented (converted into alcohol); a brewer’s yeast to produce the fermentation; and a
flavouring, such as hops. A secondary starch source (an adjunct) may be used, such as maize
(corn), rice or sugar. Less widely used starch sources include millet, sorghum and cassava root in
Africa, potato in Brazil, and agave in Mexico, among others (Jacson, 2008). The amount of each
starch source in a beer recipe is collectively called the grain bill.
There are several steps in the brewing process, which include malting, milling, mashing,
lautering, boiling, fermenting, conditioning, filtering, and packaging. There are three main
fermentation methods, warm, cool and wild or spontaneous. Fermentation may take place in
85
open or closed vessels. There may be a secondary fermentation that can take place in the
brewery, in the case, or in the bottle.
Brewing specifically includes the process of steeping, such as with making tea, sake, and soy
sauce. Technically, wine, cider and mead are not brewed but rather vinified as there is no
steeping process involving solids.
Malted barley before roasting
The basic ingredients of beer are water; a starch source, such as malted barley, able to be
fermented (converted into alcohol); a brewer’s yeast to produce the fermentation; a flavouring,
such as hoops, (Alabev.com, 2008) to offset the sweetness of the malt (Nachel, 2012). A mixture
of starch sources may be used, with a secondary starch source, such as maize (corn), rice, or
sugar, often being termed an adjunct, especially when used as a lower-cost substitute for malted
barley (Ted Goldammer, 2008). Less widely used starch sources include millet, sorghum, and
cassava root in Africa, potato in Brazil, and agave in Mexico, among others (Jackson, 2008). The
amount of each starch source in a beer recipe is collectively called the grain bill.
Water
Beer is composed mostly of water. Regiosn have water with different mineral components; as a
result, different regions were originally better suited to making certain types of beer, thus giving
them a regional character. For example, Dublin has hard water well suited to making stout, such
as Guinness; while Pilsen has soft water well suited to making pale lager, such as Pilsner
Urquell. The waters of Burton in England contain gypsum, which benefits making pale ale to
such a degree that brewers of pale ales will add gypsum to the local water in a process known as
Burtonisation (Jackson, 2008).
Starch Source
Main articles: Malt and Mash ingredients
The starch source in a beer provides the fermentable material and is a key determinant of the
strength and flavor of the beer. The most common starch source used in beer is malted grain.
Grain is malted by soaking it in water, allowing it to begin germination, and then drying the
partially germinated grain in a kiln. Malting grain produces enzymes that will allow conversion
from starches in the grain into fermentable sugars during the mash process (Wikisource, 2008).
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Different roasting times and temperatures are used to produce different colours of malt from the
same grain. Darker malts will produce darker beers.
Nearly all beer includes barley malt has the majority of the starch. This is because of its fibrous
husk, which is important not only in the sparging stage of brewing (in which water is washed
over the mashed barley grains to form the wort) but also as a rich source of amylase, a digestive
enzyme that facilitates conversion of starch into sugars. Other malted and unmalted grains
(including wheat, rice, oats, and rye, and less frequently, corn and sorghum) may be used. In
recent years, a few brewers have produced gluten-free beer made with sorghum with no barley
malt for people that cannot digest gluten-containing grains like wheat, barley, and rye
(Smagalski, 2006).
Hops
Hops are the female flower clusters or seed cones of the hop vine Humulus lupulus, which are
used as a flavouring and preservative agent in nearly all beer made today. Hops had been used
for medicinal and food flavouring purposes since Roman times; by the 7th century in Carolingian
monasteries in what is now Germany, beer was being made with hops, (Unger, 2007) though it is
not until the thirteenth century that widespared cultivation of hops for use in beer is recorded
(Cornell, 2003) Before the thirteenth century, beer was flavoured with plants such as yarrow,
wild rosemary, and bog myrtle, and other ingredients such as juniper berries, aniseed and ginger,
which would be combined into a mixture known as gruit and used as hops are now used;
between the thirteenth and the sixteenth century, during which hops took over as the dominant
flavouring, beer flavoured with guit was known as ale, while beer flavoured with hops was
known as beer (Nornsey, 2003). Some beers today, such as Fraoch by the Scottish Heather Ales
Company and Cervoise Lancelot by the French Brasserie-Lancelot company, use plants other
than hops for flavouring.
Hops contain several characteristics that brewers desire in beer: they contribute a bitterness that
balances the sweetness of the malt; they provide floral, citrus, and herbal aromas and flavours;
they have an antibiotic effect that favours the activity of brewer’s yeast over less desirable
microorganisms; and they aid in “head retention”, the length of time that a foamy head will last.
The acidity of hops is a preservative (Lewis, 2002). Flavouring beer is the sole major
commercial use of hops.
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Yeast
Yeast is the microorganism that is responsible for fermentation in beer. Yeast metabolises the
sugars extracted from grains, which produces alcohol and carbon dioxide, and thereby turns wort
into beer. In addition to fermenting the beer, yeast influences the character and flavor. The
dominant types of yeast used to make beer are Saccharomyces cerevisiae, known as ale yeast,
and Saccharomyces uvarum, known as lager yeast; Brettanomyces ferments lambics, and
Torulaspora delbrueckii ferments Bavarian weissbier. Before the role of yeast in fermentation
was understood, fermentation involved wild or airborne yeasts, and a few styles such as lambics
still use this method today. Emil Christian Hansen, a Danish biochemist employed by the
Carlsberg Laboratory, developed pure yeast cultures which were introduced into the Cartsberg
brewery in 1883, and pure yeast strains are now the main fermenting source used worldwide
(Burgess, 1964).
Clarifying agent
Some brewers add one or more clarifying agents to beer, which typically precipitate (Collect as a
solid) out of the beer along with protein solids and are found only in trace amounts in the
finished product. This process makes the beer appear bright and clean, rather than the cloudy
appearance of ethnic and older styles of beer such as wheat beers (Lewis and Young, 2002).
Examples of clarifying agents include isinglass, obtained from swimbladders of fish; Irish moss,
a seaweed; kappa carrageenan, from the seaweed Kappaphycus cottonii; Polyclar (artificial); and
gelatin. If a beer is marked “suitable for Vegans”, it was generally clarified either with seaweed
or with artificial agents, although the “fast Cask” method invented by Marston’s in 2009 may
provide another method (Hui, 2006).
Brewing process
There are several steps in the brewing process, which may include malting, mashing, lautering,
boiling, fermenting, conditioning, filtering, and packaging (Roger, 2010).
Malting is the process where barley grain is made ready for brewing. Malting is broken down
into three steps in order to help to release the starches in the barley. First, during steeping, the
grain is added to a vat with water and allowed to soak for approximately 40 hours. During
germination, the grain is spread out on the floor of the germination room for around 5 days. The
final part of malting is kilning. Here, the malt goes through a very high temperature drying in a
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kiln. The temperature change is gradual so as not to disturb or damage the enzymes in the grain.
When kilning is complete, the grains are now termed malt, and they will be milled or crushed to
break apart the kernels and expose the cotyledon, which contains the majority of the
carbohydrates and sugars; this makes it easier to extract the sugars during mashing (Garg, Garg
and Mukerji, 2010).
Marshing converts the starches released during the malting stage into sugars that can be
fermented. The milled grain is mixed with hot water in a large vessel known as a mash tun. In
this vessel, the grain and water are mixed together to create a cereal mash. During the mash,
naturally occurring enzymes present in the malt convert the starches 9long chain carbohydrates)
in the grain into smaller molecules or simple sugars (mono- di-, and tri-saccharides). This
“conversion” is called saccharification. The result of the mashing process is a sugar rich liquid or
“wort” (pronounced wert), which is then strained through the bottom of the mash tun in a process
known as lautering. Prior to lautering, the mash temperature may be raised to about 750C (165 –
1700F) (known as a mashout) to deactivate enzymes. Additional water may be sprinkled on the
grains to extract additional sugars (a process known as sparging) (Hall and Lindell, 2011).
The wort is moved into a large tank known as a “copper” or kettle where it is boiled with hops
and sometimes other ingredients such as herbs or sugars. This stage is where many chemical and
technical reactions take place, and where important decisions about the flavor, colour, and aroma
of the beer are made. The boiling process serves to terminate enzymatic processes, precipitate
proteins, isomerizes hop resins, and concentrates and sterilizes the wort. Hops add flavour,
aroma and bitterness to the beer. At the end of the boil, the hopped wort settles to clarify in a
vessel called a “whirlpool”, where the more solid particles in the wort are separated out
(Dasgupta, 2011).
After the whirlpool, the wort then begins the process of cooling. This is when the wort is
transferred rapidly from the whirlpool or brew kettle to a heat exchanger to be cooled. The heat
exchanger consists of tubing inside a bub of cold water. It is very important to quickly cool the
wort to a level where yeast can be added safely as yeast is unable to grow in high temperatures.
After the wort goes through the heat exchanger, the cooled wort goes into a fermentation tank. A
type of yeast is selected and added, or “pitched”, to the fermentation tank. When the yeast is
added to the wort, the fermenting process begins, where the sugars turn into alcohol, carbon
dioxide and other components. When the fermentation is complete the brewer may rack the beer
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into a new tank, called a conditioning tank. Conditioning of the beer is the process in which the
beer ages, the flavor becomes smoother, and flavours that are unwanted dissipate. After
conditioning for a week to several months, the beer may be filtered and force carbonated for
bottling, or fined in the cask (Hornsey, 2004).
Mashing
Mashing is the process of combining a mix of milled grain (typically malted barley with
supplementary grains such as corn, sorghum, rye or wheat), known as the “grain bill”, and water,
known as “liquor”, and heating this mixture in a vessel called a “mash tun”. Mashing is a form of
steeping, and defines the act of brewing, such as with making tea, sake, and soy sauce.
Technically, wine, cider and mead are not brewed but rather vinified, as there is no steeping
process involving solids. Mashing allows the enzymes in the malt to break down the starch in the
grain into sugars, typically maltose to create a malty liquid called wort. There are two main
methods – infusion mashing, in which the grains are heated in one vessel; and decoction
mashing, in which a proportion of the grains are boiled and then returned to the mash, raising the
temperature. Mashing involves pauses at certain temperatures (notably 450C, 620C and 730C),
and takes place in a “mash tun” – an insulated brewing vessel with a false bottom (Lewis and
Young, 2002; Howell and Schaefer, 2012). The end product of mashing is called a “mash”.
Marshing usually takes 1 to 2 hours, and during this time the various temperature rests activate
different enzymes depending upon the type of malt being used, its modification level, and the
intention of the brewer. The activity of these enzymes converts the starches of the grains to
dextrins and then to fermentable sugars such as maltose. A mash rest from 49-550C (120-1310F)
activates various proteases, which break down proteins that might otherwise cause the beer to be
hazy. This rest is generally used only with undermodified (i.e. undermalted) malts which are
decreasingly popular in Germany and the Czech Republic, or non-malted grains such as corn and
rice, which are widely used in North American beers. A mash rest at 600C (1400F) activates B-
glucanase, which breaks down gummy B-glucans in the mash, making the sugars flow out more
freely later in the process. In the modern mashing process, commercial fungal based B-glucanase
may be added as a supplement. Finally, a mash rest temperature of 65-710C (149-1600F) is used
to convert the starches in the malt to sugar, which is then usable by the yeast later in the brewing
process. Doing the latter rest at the lower end of the range favour B-amylase enzymes, producing
more low-order sugars like maltotriose, maltose, and glucose which are more fermentable by the
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yeast. This in turn creates a beer lower in body and higher in alcohol. A rest closer to the higher
end of the range favours α-amylase enzymes, creating more higher-order sugars and dextins
which are less fermentable by the yeast, so a fuller-bodied beer with less alcohol is the result.
Duration and PH variances also affect the sugar composition of the resulting wort (Black, 2010).
Lautering
Lautering is the separation of the wort (the liquid containing the sugar extracted during mashing)
from the grains. This is done either in a mash tun outfitted with a false bottom, in a lauter tun, or
in a mash filter. Most separation processes have two stages: first wort run-off, during which the
extract is separated in an undiluted state from the spent grains, and sparging, in which extract
which remains with the grains is rinsed off with hot water. The lauter tun is a tank with holes in
the bottom small enough to hold back the large bits of grist and hulls. The bed of grist that settles
on it is the actual filter. Some lauter tuns have provision for rotating rakes or knives to cut into
the bed of grist of maintain good flow. The knives can be turned so they push the grai, a feature
used to drive the spent grain out of the vessel. The mash filter is a plate-and-frame filter. The
empty frames contain the mash, including the spent grains, and have a capacity of around one
hectoliter. The plates contain a support structure for the filter cloth. The plates, frames, and filter
cloths are arranged in a carrier frame like so: frame, cloth, place, cloth with plates at each end of
the structure. Newer mash filters have bladders that can press the liquid out of the grains between
spargings. The grain does not act like a filtration medium in a mash filter (Unger, 2007).
Boiling
After mashing, the beer wort is boiled with hops (and other flavourings if used) in a large tank
known as a “copper” or brew kettle – though historically the mash vessel was used and is still in
some small breweries. The boiling process is where chemical and technical reactions take place,
including sterilization of the wort to remove unwanted bacteria, releasing of hop flavours,
bitterness and aroma compounds through isomerization, stopping of enzymatic processes,
precipitation of proteins, and concentration of the wort. Finally, the vapours produced during the
boil volatilize off-flavours, including dimethyl sulfide precursors. The boil is conducted so that it
is even and intense – a continuous “rolling boil”. The boil on average lasts between 45 and 90
minutes, depending on its intensity, the hop addition schedule, and volume of water the brewer
expects to evaporate (Goldhammer, 2008).
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At the end of the boil, the hopped wort settles to clarify in a vessel called a “whirlpool”, where
the more solid particles in the wort are separated out (Denny, 2012).
The simplest boil kettles are direct-fired, with a burner underneath. These can produce a vigorous
and favourable boil, but are also apt to scorch the wort where the flame touches the kettle,
causing caramelisation and making clean up difficult. Most breweries use a steam-fired kettle,
which uses steam jackets in the kettle to boil the wort (Denny, 2009). The steam is delivered
under pressure by an external boiler. State-of-the-art breweries today use many interesting
boiling methods, all of which achieve a more intense boiling and a more complete realization of
the goals of boiling.
Many breweries have a boiling unit outside of the kettle, sometimes called a calandria, through
which wort is pumped (Hough, Briggs, Stevens and Young, 1982). The unit is usually a tall, thin
cylinder, with many atubes upwards through it. These tubes provide an enormous surface area on
which vapour bubbles can nucleate, and thus provides for excellent volatilization. The total
volume of wort is circulated seven to twelve times an hour through this external boiler, ensuring
that the wort is evenly boiled by the end of the boil. The wort is then boiled in the kettle at
atmospheric pressure, and through careful control the inlets and outlets on the external boiler, an
overpressure can be achieved in the external boiler, raising the boiling point by a few Celsius
degrees. Upon return to the boil kettle, a vigorous vaporization occurs. The higher temperature
due to increased vaporization can reduce boil times up to 30%. External boilers were originally
designed to improve performance of kettle which did not provide adequate boiling effect, but
have since been adopted by the industry as a sole means of boiling wort.
Wort cooling
After the whirlpool, the wort must be brought down to fermentation temperatures (20-
260Celsius) (47) before yeast is added. In modern breweries this is achieved through a plate heat
exchanger. A plate heat exchange has many ridged plates, which form two separate paths. The
wort is pumped into the heat exchanger, and goes through every other gap between the plates.
The cooling medium, usually water, goes through the other gaps. The ridges in the plates ensure
turbulent flow. A good heat exchanger can drop 950C wort to 200C while warming the cooling
medium from about 100C to 800C. The last few plates often use a cooling medium which can be
cooled to below the freezing point, which allows a finer control over the wort-out temperature,
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and also enables cooling to around 100C. After cooling, oxygen is often dissolved into the wort
to revitalize the yeast and aid its reproduction.
While boiling, it is useful to recover some of the energy used to boil the wort. On its way out of
the brewery, the steam created during the boil is passed over coil through which unheated water
flows. By adjusting the rate of flow, the output temperature of the water can be controlled. This
is also often done using a plate heat exchanger. The water is then stored for later use in the next
mash, in equipment cleaning, or wherever necessary (Kunze, 2004).
Another common method of energy recovery takes place during the wort cooling. When cold
water is used to cool the wort in a heat exchanger, the water is significantly warmed. In an
efficient brewery, cold water is passed through the heat exchanger at a rate set to maximize the
water’s temperature upon existing. This now-hot water is then stored in a hot water tank
(Boulton, 2001).
Fermenting
Fermentation in brewing is the conversion of carbohydrates to alcohols and carbon dioxide or
organic acids using yeast, bacteria, or a combination thereof, under anaerobic conditions. A more
restricted definition of fermentation is the chemical conversion of sugars into ethanol. The
science of fermentation is known as zymurgy.
After the wort is cooled and aerated – usually with sterile air – yeast is added to it, and it begins
to ferment. It is during this stage that sugars won from the malt are metabolized into alcohol and
carbon dioxide, and the product can be called beer for the first time. Fermentation happens in
tanks which come in all sorts of forms, from enormous cylindro-conical vessles, thorugh open
stone vessels, to wooden vats.
Most breweries today use cylindro-conical vessels, or CCVs, which have a conical bottom and a
cylindrical top. The cone’s aperture is typically around 600, an angle that will allow the yeast to
flow towards the cone’s apex, but is not so steep as to take up too much vertical space. CCVs can
handle both fermenting and conditioning in the same tank. At the end of fermentation, the yeast
and other solids which have fallen to the cone’s apex can be simple flushed out of a port at the
apex.
Open fermentation vessels are also used, often for show in brewpubs, and in Europe in wheat
beer fermentation. These vessels have no tops, which makes harvesting top-fermenting yeast
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very easy. The open tops of the vessels make the risk of infection greater, but with proper
cleaning procedures and careful protocol about who enters fermentation chambers, the risk can
be well controlled.
Fermentation tanks are typically made of stainless steel. If they are simple cylindrical tanks with
beveled ends, they are arranged vertically, as opposed to conditioning tanks which are usually
laid out horizontally. Only a very few breweries still use wooden vats for fermentation as wood
is difficult to keep clean and infection-free and must be repitched more or less yearly.
Fermentation methods
There are three main fermentation methods, warm, cool and wild or spontaneous. Fermentation
may take place in open or closed vessels. There may be a secondary fermentation which can take
place in the brewery, in the cask or in the bottle.
Brewing yeast may be classed as “top-cropping” (or “top-fermenting”) and “bottom-cropping”
(or “bottom-fermenting”). This distinction was introduced by the Dane Emil Christian Hansen.
Top-cropping yeasts are so called because they form a form at the top of the wort during
fermentation. They can produce higher alcohol concentrations and in higher temperatures,
typically 16 to 240C (61 to 750F), produce fruitier, sweeter beers. An example of top-cropping
yeast is Saccharomyces cerevisiae. Bottom-cropping yeast are typically used to produce cool
fermented, lager-type beers, though they can also ferment at higher temperatures if kept under
34C. These yeast ferment more sugars, creating a dryer beer, and grow well at low temperatures
(Esslinger, 2009). An example of bottom-cropping yeast is Saccharomyces pastorianus, formerly
known as Saccharomyces carlsbergensis.
For both types, yeast is fully distributed through the beer while it is fermenting, and both equally
flocculate (clump together and precipitate to the bottom of the vessel) when fermentation is
finished by no means do all top-cropping yeasts demonstrate this behaviour, but it features
strongly in many English yeasts that may also exhibit chain forming (the failure of budded cells
to break from the mother cell), which is in the technical sense different from true flocculation.
The most common top-cropping brewer’s yeast, Saccharomyces cerevisiae, is the same species
as the common baking yeast. However, baking and brewing yeasts typically belong to different
strains, cultivated to favour different characteristics: baking yeast strains are more aggressive, in
order to carbonate dough in the shortest amount of time; brewing yeast stains act slower, but tend
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to produce fewer off-flavours and tolerate higher alcohol concentrations (with some stains, up to
22%).
To ensure purity of strain, a clean sample of brewing yeast is sometimes stored, either dried or
refrigerated in a laboratory (Goldhammer, 2008). After a certain number of fermentation cycles,
full scale propagation is produced from this laboratory sample. Typically, it is grown up in about
three or four stages using sterile brewing wort and oxygen.
Warm-fermenting
In general, yeasts such as Saccharomyces cerevisiae are fermented at warm temperatures
between 15 and 20°C (59 and 68 `F), occasionally as high as 24°C (75°F), while the yeast used
by Brasserie Dupont for saison ferments even higher at 29°C (840F) to 35°C (95°F). They
generally form foam on the surface of the fermenting beer, as during the fermentation process its
hydrophobic surface causes the floes to adhere to C02 and rise; because of this, they are often
referred to as "top-cropping" or "top-fermenting" - though this distinction is less clear in modern
brewing with the use of cylindro-conical tanks (McFarland, 2009). Generally, warm-fermented
beers are ready to drink within three weeks after the beginning of fermentation, although some
brewers will condition them for several months.
Cool fermenting
Lager is beer that has been cool fermented at around 10°C (500F), compared to typical warm
fermentation temperatures of 18°C (640F). It is then stored for 30 days or longer close to the
freezing point, and during this storage sulphur components developed during fermentation
dissipate.
Though it is the cool fermenting that defines lager, the main technical difference with lager yeast
is its ability to process raffinose (a trisaccharide composed of the sugars galactose, fructose, and
glucose), which means that all sugars are fermented, resulting in a well attenuated beer; warm
fermenting yeast only cleaves and ferments the fructose portion of raffinose, leaving melibiose,
which it cannot further cleave into two monasaccharides due to its lack of me1ibiase, so ale
remains sweeter with a lower conversion of sugar into alcohol. Raffinose is a minor dry
component of Carlsberg barley, but once malted is practically nonexistent (Denny, 2009).
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While the nature of yeast was not fully understood until Emil Hansen of the Carlsberg brewery in
Denmark isolated a single yeast cell in the 1800s, brewers in Bavaria had for centuries been
selecting cold-fermenting lager yeasts by storing (Lagern) their beers in cold alpine caves. The
process of natural selection meant that the wild yeasts that were most cold tolerant would be the
ones that would remain actively fermenting in the beer that was stored in the caves. Some of
these Bavarian yeasts were brought back to the Carlsberg brewery around the time that Hansen
did his famous work. Today, lagers represent the vast majority of beers produced. Examples
include Budweiser Budvar, Birra.
Moretti, Stella Artois, Red Stripe, and Singha. Some lager-style beers market themselves as
Pilsner, which originated in Pilsen, Czech Republic (Plzen in Czech). However, Pilsners are
brewed with 100% barley malt and aggressive hop bitterness, flavour, and aroma.
Lager yeast normally ferments at a temperature of approximately 5 °C (40 °Fahrenheit). Lager
yeast can be fermented at a higher temperature normally used for top-fermenting yeast, and this
application is often used in a beer style known as California Common or colloquially as “steam
beer”. Saccharomyces pastorianus is used in the brewing of lager.
Spontaneous fermentation
Lambic beers are brewed primarily around Brussels, Belgium. They are fermented in oak barrels
after being inoculated with wild yeast and bacteria while cooling in a Koelschip. Wild yeast and
bacteria ferment the wort in the oak barrels. The beers fermented from yeast and bacteria in the
Brussels area are called Lambic beers. These bacteria add a sour flavour to the beer. Of the many
styles of beer very few use bacteria, most are fermented with yeast alone and bacterial
contamination is avoided.
However, with the advent of yeast banks and the National Collection of Yeast Cultures, brewing
these beers - albeit not through spontaneous fermentation - is possible anywhere. Specific
bacteria cultures are also available to reproduce certain styles.
Brettanomyces is a genus of yeast important in brewing larnbic, a beer produced not by the
deliberate addition of brewer's yeasts, but by spontaneous fermentation with wild yeasts and
bacteria (Markowski, 2004).
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Taking inspiration from Belgium-style brews, American microbreweries produce beer with
microorganisms other than Saccharomyces, usually Srettanomyces. These fall in the broad
category of American wild ale. Conditioning
After an initial or primary fermentation, beer is conditioned, matured or aged, in one of several
ways, which can take from 2 to 4 weeks, several months, or several years, depending on the
brewer's intention for the beer. The beer is usually transferred into a second container, so that it
is no longer exposed to the dead yeast and other debris (also known as “trub”) that have settled to
the bottom of the primary fermenter, This prevents the formation of unwanted flavours and
harmful compounds such as acetylaldehydes (Markowski, 2004).
Krausening
Krausening is a conditioning method in which fermenting wort is added to the finished beer. The
active yeast will restart fermentation in the finished beer, and so introduce fresh carbon dioxide;
the conditioning tank will be then sealed so that the carbon dioxide is dissolved into the beer
producinq a lively “condition” or level of carbonation. The krausenlnq method may also be used
to condition bottled beer (Lea and Piggott, 2003).
Lagering
Lagers are stored at near freezing temperatures for 1- 6 months while still on the yeast. The
process of storing, or conditioning, or maturing, or aging a beer at a low temperate for a long
period is called “Iagering”, and while it is associated with lagers, the process may also be done
with ales, with the same results - that of cleaning up various chemicals, acids and compounds
(Bamforth, 2005).
Secondary fermentation
During secondary fermentation, most of the remaining yeast will settle to the bottom of the
second fermenter, yielding a less hazy product (Stevens et al, 2004).
Bottle fermentation
Some beers undergo fermentation in the bottle, giving natural carbonation. This may be a second
or third fermentation. They are bottled with a viable yeast population in suspension. If there is no
residual fermentable sugar left, sugar and or wort may be added in a process known as priming.
The resulting fermentation generates C02 that is trapped in the bottle, remaining in solution and
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providing natural carbonation. Bottle-conditioned beers may be either filled unfiltered direct
from the fermentation or conditioning tank, or filtered and then reseeded with yeast.
Cask conditioning
Cask ale or cask-conditioned beer is the term for unfiltered and unpasteurised beer that is
conditioned (including secondary fermentation) and served from a cask without additional
nitrogen or carbon dioxide pressure (Dornbusch, 2011).
Filtering
Filtering the beer stabilizes the flavour, and gives beer its polished shine and brilliance. Not all
beer is filtered. When tax determination is required by local laws, it is typically done at this stage
in a calibrated tank. Filters come in many types. Many use sheets or candles. Others use a fine
powder such as diatomaceous earth, also called kiese1guhr. The powder is added to the beer and
recirculated past screens to form a filtration bed.
Filters range from rough filters that remove much of the yeast and any solids (e.g., hops, grain
particles) left in the beer, to filters tight enough to strain colour and body from the beer.
Filtration ratings are divided into rough, fine, and sterile. Rough filtration leaves some cloudiness
in the beer, but it is noticeably clearer than unfiltered beer. Fine filtration removes almost all
cloudiness. Sterile filtration removes almost all microorganisms.
Sheet (pad) filters
These filters use sheets that allow only particles smaller than a given size to pass through. The
sheets are placed into a filtering frame, sterilized (with boiling water, for example) and then used
to filter the beer. The sheets can be flushed if the filter becomes blocked. The sheets are usually
disposable and are replaced between filtration sessions. Often the sheets contain powdered
filtration media to aid in filtration.
Pre-made filters have two sides;one with loose holes, and the other with tight holes. Flow goes
from the side with loose holes to the side with the tight holes, with the intent that large particles
get stuck in the large holes while leaving enough room around the particles and filter medium for
smaller particles to go through and get stuck in tighter holes. Sheets are sold in nominal ratings,
and typically 90% of particles larger than the nominal rating are caught by the sheet.
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Filters that use a powder medium are considerably more complicated to operate, but can filter
much more beer before regeneration. Common media include diatomaceous earth and perlite.
Packaging
Packaging is putting the beer into the containers in which it will leave the brewery. Typically,
this means putting the beer into bottles, aluminum cans and kegs/casks, but it may include
putting the beer into bulk tanks for high-volume customers.
Brewing methods
There are several additional brewing methods, such as barrel aging, double dropping, and
Yorkshire Square.
Brewing by-products are “spent grain” and the sediment (or “dregs”) from the filtration process
which may be dried and resold as “brewers dried yeast” for poultry feed, or made into yeast
extract.
Yeast extract is used in brands such as Vegemite and Marmite. The process of turning the yeast
sediment into edible yeast extract was discovered by a German scientist Justus Liebig (Bamforth,
2009).
Spent grain
Brewer's spent grain (also called spent grain, brewer's grain or draft) consists of the residue of
malt and grain which remains in the mash-kettle after the mashing and lautering process. It
consists primarily of grain husks, pericarp, and fragments of endosperm. As it mainly consists of
carbohydrates and proteins, and is readily consumed by animals, spent grain is used in animal
feed. Spent grains can also be used as fertilizer, whole grains in bread, as well as in the
production of biogas. Spent grain is also an ideal medium for growing mushrooms, such as
shiitake, and already some breweries are either growing their own mushrooms or supplying spent
grain to mushroom farms. This, in turn, makes the grain more digestible by livestock. Spent
grains can be used in the production of red bricks, to improve the open porosity and reduce
thermal conductivity of the ceramic mass (Blair, 2008).
Brewing industry
The brewing industry is a global business, consisting of several dominant multinational
companies and many thousands of smaller producers known as microbreweries or regional
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breweries depending on size and region (Blair, 2008). More than 133 billion liters (35 billion
gallons) are sold per year-producing total global revenues of $294.5 billion (£147.7 billion) as of
2006. SABMiller became the largest brewing company in the world when it acquired Royal
Grolsch, brewer of Dutch premium beer brand Grolsch. InBev was the second-largest beer-
producing company in the world and Anheuser-Busch held the third spot, but after the
acquisition of Anheuser-Busch by InBev, the new Anheuser-Busch InBev Company is currently
the largest brewer in the world (Blair, 2008).
Brewing at home is subject to regulation and prohibition in many countries. Restrictions on
homebrewing were lifted in the UK in 1963, Australia followed suit in 1972, and the" USA in
1978, though individual states were allowed to pass their own laws limiting production
(Verachert, 1995).
Research indicates that brewing has taken place since around the 6th millennium BC, and
archaeological evidence suggests that this technique was used in most emerging civilisations
including ancient Egypt. Descriptions of various beer recipes can be found in cuneiform from
Sumer, some of the oldest known writing of any sort (Forsell, 2008).
2.7 SUMMARY OF LITERATURE REVIEW
Capacity Planning is the process of determining the production capacity needed by an
organization to meet changing demands for its products (North Caroline State University). In the
context of capacity planning, ‘design capacity’ is the maximum amount of work that an
organization is capable of completing in a given period, ‘effective capacity’ is the maximum
amount of work that an organization is capable of completing in a given period due to constraints
such as quality problems, delays, material handling, etc.
Planning is necessary in all complex organizations. In the absence of planning, different work
units may pursue the possibly conflicting objectives of their own (Sheu and Wacker, 2001).
However, not all organizations are complex and thus heavy planning efforts are not always
necessary. In simple settings, where specialization, action variety, and task interdependence are
low, coordination can be achieved through rules and heuristics (Cyert and March, 1963).
Capacity planning in the literature has been applied to the brewing industry. The Research Gap
here is to determine the influence of capacity planning on the performance of the Brewing
Industry in South Eastern Nigeria. In brewing management, the planning-focused methods have
been developed around the concept of material requirements planning (MRP, Orlicky, 1975),
100
while the methods that emphasize rule-based control and simplicity are founded on the just-in-
time (JIT) methodology (Ohno, 1988).
Performance factors include: efficiency, effectiveness, productivity, profitability, solvency,
leverage, activity and morale (Nwanchukwu, 2004). Dictionary’s definition of efficiency as
fitness or power to accomplish or success in accomplishing the purpose intended, adequate
power, effectiveness, efficacy. Later on, it is pointed out that efficiency acquired a second
meaning – the ratio between input and output, between effort and results, expenditure and
income, cost and the resulting pleasure, this second meaning became current in Business and
Economics, only since the beginning of the 20th Century. Still later on, influenced by the
scientific management movement, efficiency was defined as the ratio of actual performance to
the standard performance (Bell, 2006).
The performance of the brewing industry was constrained by high cost of production which was
attributable mainly to substantial depreciation of the naira exchange rate. The resultant sharp rise
in cost of importation of raw materials, machinery and spare parts resulted in corresponding
sharp rise in the overall cost of production. Other factors that contributed to high cost of
production during the year were escalation in interest rates and sharp increases in tariffs on
public utilities, especially electricity. The sharp increase in production costs was translated into
higher product prices which tended to dampen demand for local manufactures resulting in high
inventory accumulation. Another factor that reduced the domestic demand for locally produced
goods was massive importation and smuggling of a wide range of foreign goods into the country
(CBN, 2002).
It is expected that there will be a research gap in the area of determining the extent to which
capacity planning enhanced the performance in the brewing industry in South Eastern Nigeria. It
is also expected that there will be a research gap in the area of ascertaining the nature of the
relationship between capacity requirements planning and materials requirements planning. It is
expected that there will be a research gap in the area of ascertaining the extent to which capacity
planning sustained the organizations’ competitive advantage.
Thus, there is a research gap in the area of determining the extent of the relationship between
capacity planning and capacity building. There is also a research gap in the area of accessing the
steps towards developing a capacity plan and the profitability in the brewing firms in the area
studied. The research work would attempt to fill the gaps created using data.
101
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CHAPTER THREE
METHODOLOGY
This chapter focused on the procedures, techniques and methods used for the study. It focuses on
research design, sources of data, description of research instruments, the population of the study,
sample size determination, validity of data collection and reliability of instruments and methods
of data analysis.
3.2 RESEARCH DESIGN
The research design study is the framework which specifies the type of information to be
collected, the sources of data and collection procedure. It is the basic plan for data collection and
analysis of the study. The research designs chosen for this study are the survey method, oral
interview and model modification. The survey research design chosen was considered quite
appropriate because it deals with large population of people with different characteristics and
domicile in different locations. While the oral and model adaptations, adds more credence to this
work..
The oral interview specifically, is very useful in research works because open ended questions
could be asked in a face to face interview, does clarifying some questions that otherwise would
have been difficult for the respondents to understand.
Also, model is a representation of reality and not reality itself. This is because not all factors in
the real system have to be in the model (Asika, 2006).
3.3 SOURCES OF DATA
In the conduct of this research, necessary information was obtained from two sources namely:
(a) Primary data.
(b) Secondary data.
a. Primary Data
Primary data refer to original first hand data or information collected by the researcher through
the use of structured questionnaire, personal interview and observations (Asika, 2006:17), and
this was precisely what the researcher did.
113
b. Secondary Data
Secondary data is the data that is existing and it is not the data collected by the researcher. It is
available in such sources like the Internet, Books, Journals, technical Magazines, Annual Reports
of the Brewing firms. Due to the fact that the researcher was not the original collector, all the
biases, mistakes and all the other exaggerations are inherited. However, the researcher evaluated
any secondary data that was used in this research work to make for internal consistency.
3.4 POPULATION OF THE STUDY
The population of study included all the senior and junior staff of the brewing industriesstudied.
The population size per firm is given here under:
Firms Population size per brewing company
1) Nigerian Breweries Plc 297
2) Guinness Nigeria Plc 224
3) Premier Breweries Plc 149
4) Continental Breweries Plc 75
Total 745
Source: Fieldwork, 2013
3.5 THE SAMPLE AND SAMPLING TECHNIQUE
Sampling represents the process of selecting a subset of observations from among many possible
observations for the purpose of drawing conclusions about the larger set of possible observations.
It is a strategy a researcher adopts in order to arrive at a good representation of the population.
The sampling method adopted in this study is the stratified sampling method. Sampling is a
compromise between arbitrary guess work in one extreme and perfection in the other extreme.
In calculating the sample size for this study, the researcher appllied the statistical formula for
selecting from a finite population as propounded by Taro Yamane’s
Assigning values to these symbols, the sample size was calculated thus:
2)025.0(7451
745
+=n
509=
The sample sizes were 203 for Nigerian Breweries Plc, 153 for Guinness Nigeria Plc, 102 for
Premier Breweries and 51 for Continental Breweries Plc. However, a census was done usingthe
populationof 745.
114
3.6 DESCRIPTION OF RESEARCH INSTRUMENTS
The major research instruments that were used in gathering data in this investigation were the
structured questionnaire, oral interview schedule and dichotomous (yes or no oral interview
schedule). The questionnaire was tailored in the sense that all the questions were logically
sequenced, asked to all the respondents in the same manner and no follow up questions were
allowed. The questions on the personal dataare designed to capture the demographic data of the
respondents, such that the respondents were given the answers to tick. The questions related to
the five objectives were of the Likert scale form in which there were statements with the answers
of strongly agree, agree, undecided, disagree and strongly disagree.
In the oral interview schedule, there are five open-ended questions containing the research
questions with a focuse group discussion. The answers to the questions from the two schedules
were content-analyzed.
3.7 DATA ANALYSIS TECHNIQUE(S)
The first hypothesis was tested using the Z test of population proportions. The second hypothesis
was tested using the Spearman’s rank correlation coefficient. The third hypothesis was tested
using Z test of population proportions. Test of the fourth hypothesis was using the Spearman’s
rank correlation while the fifth hypothesis was done using Z test of population proportions. So in
summary, tests of hypothesis number 1, 3 and 5 were using Z test of population proportion while
hypothesis 2 and 4 were using Spearman’s Rank Correlation.
Spearman’s Rank Correlation Coefficient
It is used in determining the relationship between independent variable and a dependent variable
and is given by the formula.
( )( )11
61
21
+−−= ∑
nnn
dr
Where:
d = is the difference in ranks for the two variables
n = is the number of years
rs = is the Spearman’s Rank Correlation Coefficient
the significance of the Spearman’s rank correlation is to be tested using the t test.
115
t – test statistic
When hypothesis is formulated using the correlation coefficient, it must be known that
population parameter is involved. Therefore, it is always advisable to transform the correlation
coefficient that would be obtained into students’ t – distribution (Agbadudu, 2004). This would
indicate whether the correlation coefficient obtained would show a real relationship or whether it
could be reasonably attributed to chance. In order to transform the r to t, the following
transformation formula would be used.
( )2
1 2
−−
=
n
r
rt
where:
t = t – statistic
r = Correlation Coefficient
n = No of Paired value
n – 2 = No Degree of freedom
Decision Rule: reject Ho if t computed is > t critical otherwise accept.
The z – test statistic
The z – test is normally used like the t – test but only when the sample size is equal or greater
than 30 (i.e. n> 30) (Agbadudu, 2004). The z – test statistic can also be computed exactly the
same way the t – test statistic is computed using the same formula. According to Ewurum (2003)
a z-test statistic can be used interchangeably with a t-test statistic when there are at least 150
degrees of freedom.
Z-test = tn –1 = ns
UX −
with n – 1 degrees of freedom.
Where:
X = Sample mean
U = Hypothesized mean of the sample
s = Sample standard deviation
n = Sample size
116
Decision Rule: Reject the Ho and uphold Hi if the Z calculated value exceeds the critical or
the Z-table values. But if the reverse is the case, do not reject the null hypotheses.
3.8 VALIDITY OF INSTRUMENT
Validity of a test reflects what a test measures and how well it measures it (Enikanselu et al,
2009). It is concerned with how a measuring instrument is actually measuring the concept in
question and also how the concept is being measured accurately. In this study, the table of
random numbers used to select the respondents and the same version of the instrument is
administered to the respondents and this gives the instrument content validity.
3.9 RELIABILITY OF THE RESEARCH INSTRUMENT
Reliability has to do with the extent to which the measure contains an error. If the research
measure does not contain an error, it is said to be reliable. There are three types of reliability
namely; split-half, equivalent forms and test-retest methods. The test-retest method is to be used.
The same version of the research instrument is to be administered to the same respondents at two
point in time and the results are correlated. A Spearman’s rank correlation coefficient of 0.9 or
less than 1 or equal to 1 makes the research measure to be reliable.
117
REFERENCES
Agbadudu, A.B. (2004), Statistics for Business and the Social Sciences, Benin City: Uri
Publishing Limited.
Asika, N. (2006), Research Methodology: A Process Approach, Lagos: Mukugamu and Brothers
Enterprises.
Banjoko, S.A. (2006), Production and Operations Management, Lagos: Saban Publishers.
Enikanselu, S.A., Ojodu, H.O. and Oyende, A.I. (2009), Management and Business Research
Seminar, Lagos: Enykon Consult.
Koontz, H., O’donnel, C. and Weihrich, H. (2000), Management, New York: McGraw-Hill.
Nwachukwu, C.C. (1988), Management Theory and Practice, Onitsha: Africana FEP Publishers
Limited.
Nwana, O.C. (2000), Introduction to Educational Research, Ibadan: Heinemann Educational
Books Nigerian Plc.
O’brien, J.A. (2008), An Introduction to Computers in Business Management. Homewood:
Illinois: Richard D. Irwin Incorporated.
Osaze, B.E. and Anao, A.R. (2000), Managerial Finance, Benin City: Uniben Press.
118
CHAPTER FOUR
DATA PRESENTATION AND ANALYSIS
4.1
In the last chapter, the Research Methodology was handled; adopting the research design was a
combination of a survey, oral interview and model modification. Primary data was collected on
the personal data of the respondents, on capacity planning and performance in the brewing
industry in South Eastern Nigeria.
The data is presented by means of tables to make it amenable for further analysis. By analysis, is
meant the act of noting relationships and aggregating data on variables with similar
characteristics (Mills and Walter, 2008). Yomere and Agbonifoh (2000) have consistently
observed that it is at the analysis stage of a research work that meaning is given to the data that is
collected. If the data is not properly analysed, it will be difficult to discuss the results or findings.
It will also be difficult to write the summary of findings, conclusion, recommendations and
contribution to knowledge and suggestions for further research in the next chapter.
Podsakoff and Dalton (1987) have observed that the factual data is going to be a basis for
reasoning, calculation and discussion. Apart from the heading above, the other major headings
are Data Presentation, Data Analysis, Discussion of the findings related to the contingency
theory and multi period capacity problem and discussion of Findings.
4.2 DATA PRESENTATION
Table 4.1 shows the presentation of the response rate of the questionnaires administered.
Table 4.1: The presentation of the response rate of the questionnaires administered
Number Rate
Total number of questionnaire administered 745 1
Copies of questionnaire returned 740 0.993
Total copies of questionnaire not returned 5 0.007
Source: From the field survey (2014).
From Table 4.1, it is shown that 740 out of 745 copies of the questionnaire administered were
returned giving a return rate of 0.993, while 5 out of the 745 copies were not returned giving a
non return rate of 0.007. The total return rate and non return rate was 1.
119
Table 4.2 shows the summary of the personal data of the 740 respondents.
Table4.2: The summary of the personal data of the 740 respondents
1 Sex Frequency
Male
Female
Total
518
222
740
2 Marital Status Frequency
Married
Single
Total
562
178
740
3 Age Frequency
Under 20 years
21 – 30 years
31 – 40 years
41 – 50 years
51 – 60 years
Above 60 years
Total
15
155
192
200
170
8
740
4 Highest Educational Qualifications Frequency
Senior School Certificate Royal Society of Arts Diploma Ordinary National Diploma Higher National Diploma First Degree Second Degree Ph.D Associate of Chartered Accountants (ACA) Total
104 44 30 141 129 211 44 1
36
740
Source: From the Questionnaire Administered (2014)
From Table 4.2, it is shown that for the sex of the 740 respondents, 518 of them were males and
222 of them were females. For the marital statuses of the 740 respondents, 562 of them were
married and 178 of them were single. For the ages of 740 respondents, they were under 20 years,
21 – 30 years, 31 – 40 years, 41 – 50 years, 51 – 60 years and above 60 years. They had
frequencies of 15, 155, 192, 200, 170 and 8 of them respectively. For the highest educational
120
qualifications of the 740 respondents, they were Senior School Certificate, Royal Society of Arts,
Diploma, Ordinary National Diploma, Higher National Diploma, First Degree, Second Degree,
Ph.D and Associate of Chartered Accountants. They had frequencies of 104, 44, 30, 141, 129,
211, 44, 1 and 36 of them respectively.
Table 4.3 shows the presentation of the responses on the statuses and durations worked of the
740 respondents.
Table 4.3: The presentation of the responses on their statuses and their experiential years
Status Frequency
Senior Staff
Junior Staff
Total
252
488
740
Durations worked (experiential
years) Frequency
0 – 10 years
11 – 20 years
20 – 30 years
Above 30 years
Total
30
342
360
8
740
Source: From the Questionnaire Administered(2014)
From Table 4.3, it is shown that for the statuses of the 740 respondents, 252 of them were Senior
Staff while 488 of them were Junior Staff. For the durations worked, they were 0-10 years, 11 –
20 years, 21 – 30 years and above 30 years. They had frequencies of 30, 342, 360 and 8 of them
respectively. This implies that majority of the respondents are quite experienced having worked
for not less than 20 years.
4.3 DATA ANALYSIS
4.3.1 Percentage Analysis
A five point Likert-scale was used with values assigned ranging from 5(SA) to 1(SD) for
positive responses and vice versa for negative responses.
Table 4.4 gives the analysis of the responses related to the five objectives.
121
Table 4.4: The analysis of the responses related to the five objectives
STATEMENTS RESPONSES
X SA A U D SD
1. Capacity planning to a large extent
enhances the performance in the
brewing sector in South Eastern Nigeria
F
%
300
40.541
367
49.595
23
3.108
25
3.378
25
3.388
2 There is a significant positive
relationship between capacity
requirements planning and materials
requirements planning
F
%
304
41.081
370
50.000
22
2.973
23
3.108
21
2.838
3 Capacity planning to a large extent
sustains the organization’s competitive
position
F
%
302
40.811
380
51.351
20
2.703
19
2.568
19
2.568
4 There is a significant positive
relationship between capacity building
and capacity planning.
F
%
308
41.622
381
51.486
17
2.297
18
2.432
16
2.162
5 The steps towards developing capacity
plan positively affected profitability in
the brewing industry in the area studied
F
%
315
42.48
381
51.486
14
1.892
15
2.027
15
2.027
Source: From the Questionnaire Administered (2014)
Table 4.4 shows the statements and the responses namely Strongly Agree (SA), Agree (A),
Undecided (U), Disagree (D) and Strongly Disagree (SD).For the statement that capacity
planning to a large extent enhances the performance in the brewing sector in South Eastern
Nigeria, the responses were Strongly Agree, Agree, Undecided, Disagree and Strongly Disagree.
They had frequencies of 300, 367, 23, 25 and 25 out of 740 respectively. These gave percentages
to 3 decimal places of 40.541, 49.595, 3.108, 3.378 and 3.378 respectively.
For the statement that there is a significant positive relationship between capacity requirements
planning and materials requirements planning, the responses were Strongly Agree, Agree,
Undecided, Disagree and Strongly Disagree. They had frequencies of 304, 370, 22, 23 and 21 out
of 740 respectively. These gave percentages of 41.081, 50.000, 2.473, 3.108 and 2.838
respectively.
122
For the statement that capacity planning to a large extent sustains the organizations competitive
position, the responses were Strongly Agree, Agree, Undecided, Disagree and Strongly Disagree.
They had frequencies of 302, 380, 20, 19 and 19 out of 740 respectively. These gave percentages
of 40.811, 51.351, 2.703, 2.568 and 2.568 respectively.
For the statement that there is a significant positive relationship between capacity building and
capacity planning, the responses were Strongly Agree, Agree, Undecided, Disagree and Strongly
Disagree. They had frequencies of 308, 381, 17, 18 and 16 out of 740 respectively. These gave
percentages of 41.622, 51.486, 2.297, 2.432 and 2.162 respectively.
For the statement that the steps towards developing a capacity plan positively affected
profitability in the brewing industry in the area studied, the responses were Strongly Agree,
Agree, Undecided, Disagree and Strongly Disagree. They had frequencies of 315, 381, 14, 15
and 15 out of 740 respectively. These gave percentages of 42.568, 51.486, 1.892, 2.027 and
2.027 respectively.
Table 4.5 gives the analysis of the 12 steps towards developing a capacity plan that have
positively affected profitability in the brewing industry in the area studied.
123
Table 4.5: The analysis of the 12 steps towards developing a capacity plan that have
positively affected profitability in the brewing industry in the area studied
s/n The 12 steps Frequency Percentages
1 To determine service level requirements 69 9.624
2 To define workloads 66 8.919
3 To determine the unit of work 65 8.784
4 To determine the service levels of each workload 64 8.649
5 To analyse the current system capacity 63 8.514
6 To measure service levels 62 8.378
7 To measure the overall resource usage 61 8.243
8 To measure the resource usage by workload 60 8.108
9 To identify the components of response time. 59 7.973
10 To plan for the future 58 7.838
11 To determine the future processing requirements 57 7.703
12 To plan the future system configuration 56 7.568
Total 740 100
Source: The 12 steps and the numbers are got from the questionnaires administered (2014)
From Table 4.5, the 12 steps were to determine service level requirements, to define workloads,
to determine the unit of work, to determine the service levels of each workload, to analyse the
current system capacity, to measure service levels, to measure the overall resource usage, to
measure the resource usage by workload, to identify the components of the response time, to
plan for the future, to determine the future processing requirements and to plan the future system
configuration. They had frequencies of 69, 66, 65, 64, 63, 62, 61, 60, 59, 58, 57 and 56 out of
740 respectively. These gave percentages of 9.324, 8.919, 8.784, 8.649, 8.514, 8.378, 8.243,
8.108, 7.973, 7.838, 7.703 and 7.568 respectively.
124
4.3.2 Relative Frequency Analysis
Table 4.6 shows the analysis of the responses opposite in meaning to the objectives.
Table 4.6: The analysis of the responses opposite in meaning to the objectives
STATEMENTS RESPONSES
X SA A U D SD
1. Capacity planning to a little extent
enhances the performance in the brewing
sector in South Eastern Nigeria
F
R.F
25
0.034
25
0.034
23
0.031
367
0.496
300
0.405
2 There is a significant negative relationship
between capacity requirements planning
and materials requirements planning
F
R.F
21
0.028
23
0.031
22
0.030
370
0.500
304
0.411
3 Capacity planning to a low extent sustains
the organization’s competitive position
F
R.F
19
0.026
19
0.026
20
0.027
380
0.514
302
0.408
4 There is a negative correlation between
capacity building and capacity planning.
F
R.F
16
0.022
18
0.024
17
0.023
381
0.575
308
0.416
5 The are no steps towards developing
capacity plan to improve the profitability in
the brewing industry in the area studied
F
R.F
15
0.020
15
0.020
14
0.019
381
0.515
215
0.426
Source: the statements, responses and the numbers are got from the questionnaire
administered (2014)
Table 4.6 shows the statements, the responses and the numbers and the relative frequencies
which summed up to 1. For the statement that capacity planning to an appreciable extent enhance
the performance in the brewing sector in South Eastern Nigeria, the responses were Strongly
Agree, Agree, Undecided, Disagree and Strongly Disagree. They had frequencies of 25, 25, 23,
367 and 300 out of 740 respectively. These gave relative frequencies of 0.034, 0.034, 0.031,
0.496 and 0.405 respectively.
For the statement that there is a significant negative relationship between capacity requirements
planning and materials requirements planning, the responses were Strongly Agree, Agree,
Undecided, Disagree and Strongly Disagree. They had frequencies of 21, 23, 22, 370 and 304 out
of 740 respectively. These gave relative frequencies of 0.028, 0.031, 0.30, 0.500 and 0.411
respectively.
125
For the statement that capacity planning to a low extent sustains the organization’s competitive
position, the responses were Strongly Agree, Agree, Undecided, Disagree and Strongly Disagree.
They had frequencies of 19, 19, 20, 380 and 302 out of 740. These gave relative frequencies of
0.026, 0.026, 0.027, 0.514 and 0.408 respectively.
For the statement that there is a negative correlation between capacity building and capacity
planning, the responses were Strongly Agree, Agree, Undecided, Disagree and Strongly
Disagree. They had frequencies of 16, 18, 17, 381 and 308 out of 740 respectively. These gave
relative frequencies of 0.022, 0.024, 0.023, 0.575 and 0.416 respectively.
For the statement that there are no steps towards developing a capacity plan to improve the
profitability in the brewing industry in the area studied, the responses were Strongly Agree,
Agree, Undecided, Disagree and Strongly Disagree. They had frequencies of 15, 15, 14, 381 and
315 out of 740. These gave relative frequencies of 0.020, 0.020, 0.019, 0.515 and 0.426
respectively.
4.3.3 Analysis using the Coefficient of Variation
Table 4.7 shows the analysis of the other responses related to the first four objectives.
Table 4.7: The analysis of the other responses related to the first four objectives
s/n Statements X SA A U D SD X S
S
X
1 Adding capacity in anticipation of an increase in demand increases the performance in the brewing sector in South Eastern Nigeria.
F 298 366 24 26 26 4.195 0.925 4.535
2 Adding capacity only after the organization is running at full capacity due to increase in demand increases the performance in the brewing sector in South Eastern Nigeria.
F 26 26 24 366 298 1.805 0.962 1.979
3 There is no relationship
between capacity
requirements planning and
F 20 22 21 371 306 1.755 0.863 2.039
126
materials requirements
planning
4 Materialsrequirements
planning has a positive
correlation with capacity
requirements planning.
F 306 371 21 22 20 4.244 0.863 4.918
5 The extent to which capacity
planning sustains the
organization’s competitive
advantage is not obvious.
F 19 20 19 381 361 1.750 0.843 2.076
6 The extent to which capacity
planning sustains the
organizations competitive
position is obvious.
F 301 381 19 20 19 4.250 0.843 5.042
7 There is a positive correlation
between capacity planning
and capacity building.
F 307 382 18 17 16 4.280 0.805 5.317
8 There is a negative
relationship between capacity
planning and capacity
building.
F 16 17 18 382 307 1.720 0.805 2.137
Source: From Questionnaire Administered (2014)
Table 4.7 given the statements, responses, sample mean, sample standard deviation and the
coefficient of determination. For the statement that adding capacity in anticipation of an increase
in demand increases the performance in the brewing sector in South Eastern Nigeria, the
responses were Strongly Agree, Agree, Undecided, Disagree and Strongly Disagree. They had
frequencies of 298, 366, 24, 26 and 26 out of 740 respectively. These gave a sample mean of
4.195, sample standard deviation of 0.925 and coefficient of variation of 4.535.
For the statement that adding capacity only after the organization is running at full capacity due
to increase in demand increases the performance in the brewing sector in South Eastern Nigeria,
the responses were Strongly Agree, Agree, Undecided, Disagree and Strongly Disagree. They
127
had frequencies of 26, 26, 24, 366 and 298 out of 740 respectively. These gave a sample mean of
1.805, a sample standard deviation of 0.962 and a coefficient of determination of 1.979.
For the statement that there is no relationship between capacity requirements planning and
materials requirements planning, the responses were Strongly Agree, Agree, Undecided,
Disagree and Strongly Disagree. They had frequencies of 20, 22, 21, 371 and 306 out of 740
respectively. These gave a sample mean of 1.755, a sample standard deviation of 0.863 and a
coefficient of variation of 2.039.
For the statement that materials requirements planning have a positive correlation with capacity
requirements planning, the responses were Strongly Agree, Agree, Undecided, Disagree and
Strongly Disagree. They had frequencies of 306, 371, 21, 22, 20. These gave a sample mean of
4.244, a sample variance of 0.863 and a coefficient of variation of 4.918.
For the statement that the extent to which capacity planning sustains the organization’s
competitive position is not obvious, the responses were Strongly Agree, Agree, Undecided,
Disagree and Strongly Disagree. They had frequencies of 19, 20, 19, 381, 301 out 740
respectively. These gave a sample mean of 1.750, a sample standard deviation of 0.843 and a
coefficient of variation of 2.076.
For the statement that the extent to which capacity planning sustains the organization’s
competitive position is obvious, the responses were Strongly Agree, Agree, Undecided, Disagree
and Strongly Disagree. They had frequencies of 301, 381, 19, 20 and 19 out of 740 respectively.
These gave a sample mean of 4.250, a sample variance of 0.843 and a coefficient of
determination of 5.042.
For the statement that there is a positive correlation between capacity planning and capacity
building the responses were Strongly Agree, Agree, Undecided, Disagree and Strongly Disagree.
They had frequencies of 307, 382, 18, 17 and 16 out of 740 respectively. These gave a sample
mean of 4.280, a sample standard deviation of 0.805 and a coefficient of determination of 5.317.
For the statement that there is a negative relationship between capacity planning and capacity
building, the responses are Strongly Agree, Agree, Undecided, Disagree and Strongly Disagree.
They had frequencies of 16, 17, 18, 382 and 307 out of 740 respectively. These gave a sample
mean of 1.720, sample standard deviation of 0.805 and a coefficient of variation of 2.137.
128
In all the positive statements had a higher sample mean, and higher coefficient of variation than
the corresponding negative statements.In all the negative statements had a lower sample mean
and lower coefficient of determination than the corresponding positive statements.Most of the
740 respondents agreed with the positive statements.
4.3.4 Hypotheses Testing
Five hypotheses are to be tested in the null that:
1. Capacity planning to a non appreciable extent enhanced the performance in the brewing
industry in South Eastern Nigeria.
2. There is no significant positive relationship between capacity requirements planning and
materials requirements planning.
3. Capacity planning to a little extent, sustains organizations’ competitive advantage.
4. There is a negative significant relationship between capacity planning and capacity
building.
5. The steps towards developing a capacity plan that would affect profitability in the
brewing industry in South Eastern Nigeria are not of the same order of magnitude.
The alternative hypotheses are that:
1. Capacity planning to a large extent enhanced the performance in the brewing industry in
South Eastern Nigeria.
2. There is a significant positive relationship between capacity requirements planning and
materials requirements planning.
3. Capacity planning to a large extent sustains organization’s competitive advantage.
4. There is a positive significant relationship between capacity planning and capacity
building.
5. The steps towards developing a capacity plan that would affect profitability in the
brewing industry in South Eastern Nigeria are of the same order of magnitude.
129
Table 4.8 shows the computational details of the first hypothesis.
Table 4.8: The computational details of the first hypothesis
Hypothesis number Calculated Z
value
Table Z value Statistical
Decision
1 6.893 1.645 Reject Ho
( )
( )
892637062.6
4.0
)101351351.0(740
740
)8.01)(8.0(
8.0740
667
)1(
=
=
−
−=
−−
=
Z
Z
Z
n
PP
PZ
OO
OnX
893.6=Z to 3 decimal places
Source: The number of respondents that strongly agree or agree with the statement x is got from
the questionnaires administered, n = 740, Po, the prescribed proportion is 0.8 and the rest are
calculated.
From Table 4.8, it is shown that the calculated Z value which is 6.893 is greater than the table Z
value which is 1.645. So the null hypothesis is rejected and the alternative hypothesis is
accepted. So capacity planning to a large extent enhanced the performance in the brewing
industry in South Eastern Nigeria.
Table 4.9 shows the computational details of the second hypothesis.
130
Table 4.9: The computational details of the second hypothesis
Year Increase in capacity
requirements planning
Rank Increase in materials
requirements planning
Rank d d2
1 5 10 4.5 9 1 1
2 4 7.5 4 7.5 0 0
3 3 5.5 3 5.5 0 0
4 2 2.5 2 2.5 0 0
5 2 2.5 2 2.5 0 0
1
95.0
20
19
20
01
20
20
20
11
)6)(4)(5(
)1)(6(1
)1)(1)((
61
2
=
=−=−=
−=
+−∑−=
s
s
s
s
r
r
r
nnn
dr
Source: The increases in the capacity requirements planning and materials requirement planning
are got from the questionnaires administered.
From Table 4.9, the Spearman’s rank correlation coefficient is 0.95 which is very close to 1, so
the null hypothesis is rejected and the alternative hypothesis is accepted. So there is a significant
positive relationship between capacity requirements planning and materials requirements
planning.
131
Table 4.10 shows the computational details of the third hypothesis.
Table 4.10: The computational details of the third hypothesis
Hypothesis number Calculated Z
value
Table Z value Statistical
Decision
3 8.271 1.645 Reject Ho
( )
271169499.8
4.0
308966909.3
4.0
)740)(8.0921621621.0(
)2.01)(8.0(
8.0740
682
)1(
=
=
−=
−
−=
−
−=
Z
Z
Z
n
Z
n
PP
PZ
OO
OnX
271.8=Z to 3 decimal places
Source: The number of respondents that either agreed or strongly agree, x is got from the
questionnaires administered, the prescribed proportion is 0.8 and the rest are calculated.
From Table 4.10, it is shown that the calculated Z value which is 8.271 is greater than the Table
Z value at 95% confidence level which is 1.645. So the null hypothesis is rejected and the
alternative hypothesis is accepted. So, capacity planning to a large extent sustains the
organization’s competitive advantage.
132
Table 4.11 shows the computational details of the Fourth hypothesis.
Table 4.11: The computational details of the Fourth hypothesis
Year Increase in capacity planning
Rank Increase in capacitybuilding
Rank d d2
1 5 9.5 5 9.5 0 0
2 4.5 8 4 7 1 1
3 3.5 6 3 5 1 1
4 2 2.5 2 2.5 0 0
5 2 2.5 2 2.5 0 0
2
9.010
9
10
1
10
10
10
11
)6)(4)(5(
)2)(6(1
)1)(1)((
61
2
=−==
−=
−=
+−∑−=
s
s
s
s
r
r
r
nnn
dr
Source: The increases in the capacity planning and capacity building over 5 years are got from
the questionnaires administered.
From Table 4.11, it is shown that the calculated Spearman’s correlation coefficient which is 0.9
is high and it is very close to 1. This shows that there is to a large extent, a positive relationship
between capacity planning and capacity building.
133
Table 4.12 shows the computational details of the fifth hypothesis.
Table 4.12: The computational details of the fifth hypothesis
Hypothesis number Calculated Z value Table Z value Statistical Decision
5 9.558 1.645 Reject Ho
( )
558.9
4.0
)740)(8.094859064.0(
740
)2.0)(8.0(
8.0740
696
)1(
=
−=
−=
−−
=
Z
Z
Z
n
PP
PZ
OO
OnX
Source: The number of respondents who either strongly agreed or agreed with the statement x is
got from the questionnaire administered, n = 740 and the prescribed proportion is 0.8 the rest are
calculated.
From Table 4.12, it is shown that the calculated Z value which is 9.558 is greater than the Table
Z value which is 1.645 at 95% confidence level. So, the null hypothesis is rejected and the
alternative hypothesis is accepted. So, the 12 steps of the capacity plan positively affected
profitability in the brewing industry in South Eastern Nigeria at 5% level of significance.
4.4 ANALYSIS OF THE RELATIONSHIP OF THE CONTINGENCY THE ORY AND
THE FIVE OBJECTIVES
Table 4.13 shows the analysis of the responses to the dichotomous oral interview questions on
the relationship between the contingency theory and the five objectives
134
Table 4.13: The analysis of the responses to the dichotomous oral interview questions on
the relationship between the contingency theory and the five objectives
s/n Question Yes in Number
% No in Number
% Total in Number
Total in %
1 Is the contingency theory related to
capacity planning to enhance the
performance in the brewing
industry?
738 99.73 2 0.27 740 100
2 Is the contingency theory related to
the nature of the positive
relationship between capacity
requirements planning and
materials requirements planning?
736 99.46 4 0.56 740 100
3 Is contingency theory related to
ascertaining the large extent of the
positive relationship between
capacity planning and capacity
building?
734 99.19 6 0.81 740 100
4 Does contingency theory relate to
the large extent to which capacity
planning sustains the organization’s
competitive advantage?
737 98.92 8 1.08 740 100
5 Is the contingency theory related to
the steps of capacity planning
which to a large extent aims to
develop the capacity plan that
positively affects the profitability of
the brewing industry?
730 98.65 10 1.35 740 100
Source: The questions and the responses were obtained from the dichotomous oral interview schedule distributed.
135
Table 4.13 shows the questions and the yes or no responses in absolute numbers and in
percentages. 740 respondents that were asked if contingency theory is related to capacity
planningto enhance the performance in the brewing industry, 738 out of 740 respondents making
99.73% of them said yes, while 2 of them making 0.27% of them said no. The 740 respondents
were asked if the contingency theory is related to the nature of the positive relationship between
capacity requirements planning and materials requirements planning, 730 out of the 740
respondents making 98.64% of them said yes, while 10 of them making 1.35% of them gave
answer to the contrary. The 740 respondents were asked if the contingency theory is related to
ascertaining to a large extent the relationship between capacity planning and capacity building.
734 out of the 740 respondents, 99.19% affirmed to it, while 6 of them had a negative view, that
is, 0.81%.
740 respondents were asked if the contingency theory is related to the view that, capacity
planning sustained the organization’s competitive advantage. 732 out of the 740 respondents,
making 98.92% affirmed to it while a handful, that is 8 (1.08%) of the respondents think
otherwise.
The 740 respondents were asked if contingency theory is related to the steps of capacity plan that
positively affects to a large extent the profitability in the brewing industry. A significant number,
730 (98.65%) of the 740 respondents gave a positive answer, while 10 (1.35%), an insignificant
number had a contrary opinion.
Table 4.14 shows the analysis of the responses on the relationship between the multi period
capacity problem and the five objectives.
136
Table 4.14: The analysis of the responses on the relationship between the multi period capacity problem and the five objectives s/n Question Yes in
Number % No in
Number % Total in
Number Total in %
1 Is the multi period capacity
problem relevant for capacity
planning to enhance the
performance in the brewing
industry?
739 99.86 1 0.14 740 100
2 Is the multi period capacity
problem relevant to the nature of
the positive relationship between
capacity requirements planning
and materials requirements
planning?
737 99.59 3 0.41 740 100
3 Is the multi period capacity
problem relevant to ascertaining
that to a large extent there is a
positive relationship between
capacity planning and capacity
building?
735 99.32 5 0.68 740 100
4 Is the multi period capacity
problem relevant to a large extent
to ascertain how capacity planning
sustains the organizations
competitive advantage?
733 99.05 7 0.95 740 100
5 Is the multi period capacity problem relevant to the development of the steps of the capacity plan that positively affect profitability in the brewing industry?
731 98.78 9 1.12 740 100
Source: The questions and responses were sourced the dichotomous oral interview schedule distributed.
137
740 respondents were asked if the multi period was relevant for capacity planning to enhance the
performance in the brewing industry. 739 out of 740 of them giving 99.86% said yes.
Incidentally, a lone voice thatis only 1 person (0.14%) had a different view.
740 respondents were asked if the multi period capacity problem was relevant to the nature of the
positive relationship between capacity requirements planning and materials requirements
planning. 737 of them making 99.59% of them said yes, while 3 of them making 0.41% of them
gave answer to the contrary. The 740 respondents were asked if the multi period capacity
problem was relevant for ascertaining the extent of the positive relationship between capacity
planning and capacity building. 735 of them making 99.32% of them said yes, while 5 of them
making 0.68% of them said no.
740 respondents were asked if the multi-period problem was relevant to a large extent to how
capacity planning sustained the organization’s competitive advantage. 733 of them making
99.05% of them said yes, while 7 of them making 0.95% gave answer to the contrary. The 740
respondents were asked if the multi period capacity problem was relevant to the development of
the steps of the capacity plan that positively affect the profitability in the brewing industry. 731
of them making 98.78% said yes, and 9 of them making 1.22% had a contrary opinion.
Table 4.15 Gives the analysis of the data on how indigenous capacity building theory relates to
the five objectives.
138
Table 4.15: The analysis of the data on how indigenous capacity building theory relates to
the five objectives
S/N STATEMENT RESPONSES X Z SA A U D SD
1 The indigenous capacity
building theory has a positive
relationship with the extent
to which capacity planning
enhances the performance in
the brewing industry in south
Eastern Nigeria
501 172 27 21 20 4.508 7.444
2 The indigenous capacity
building theory has a positive
relationship with the nature
of relationship between the
capacity requirements
planning and materials
requirements planning.
502 75 23 21 19 4.678 7.812
3 The indigenous capacity
building theory has a positive
relationship with the extent
to which capacity planning
sustains the organizational
competitive advantage
601 84 20 18 17 4.668 8.547
4 The indigenous capacity
planning theory has a
positive correlation with the
extent of the relationship
between capacity planning
and capacity building.
601 88 16 18 17 4.673 8.914
5 The indigenous capacity
planning theory has a
positive relationship with the
602 97 14 15 12 4.705 9.833
139
12 steps towards developing
a capacity plan and the
profitability in the brewing
firms in the area studied
Table 4.15 shows the statements, responses Strongly Agree (SA), Agree (A), Undecided (U),
Disagree (D) and Strongly Disagree (SD), the sample mean (X ) and the Z value. For the
statement that the indigenous capacity building theory has a positive relationship with the extent
to which capacity planning enhances the performance in the brewing industry in South Eastern
Nigeria, the sample mean is 4.508 which is greater than 3. The calculation Z value was 7.444
which is greater than the Table Z score at 95% confidence level which is 1.645. This shows that
most of the respondents strongly agree with the statement.
For the statement that the indigenous capacity building theory has a positive correlation with the
nature of the relationship between capacity requirements planning and materials requirements
planning, the sample mean is 4.648 which is more than the value of 3. The calculated Z value is
7.812 which is more than the Table Z value at 95% confidence level which is 1.645. This shows
that most of the respondents strongly agreed with the statement.
For the statement that the indigenous capacity building theory has a positive relationship with the
extent that capacity planning sustains an organization’s competitive advantage, the sample mean
is 4.668 which is greater than 3. The calculated Z value is 8.547 which is more than the table Z
value at 95% confidence level which is 1.645. This shows that most of the respondents strongly
agreed to the statement.
For the statement that the indigenous capacity building has a positive correlation with the extent
of the relationship between capacity planning and capacity building, the sample mean is 4.673
which is greater than 3. The calculated Z value is 8.914 which is more than the table Z value at
95% confidence level which is 1.645. So it shows that most of the respondents strongly agreed
with the statement.
For the statement that the indigenous capacity building theory has a positive relationship with the
12 steps towards developing a capacity plan and profitability in the brewing firms in the area
studied, the sample mean is 4.705, which is greater than 3. The calculated Z value is 9.833 which
140
is greater than the table Z value at 95% level of significance which is 1.645. This shows that
most of the respondents strongly agreed with the statement.
Discussion of findings relating the indigenous capacity building to the first objective
It was found that there is a positive relationship between the indigenous capacity theory and the
extent to which capacity planning enhances the performance in the brewing industry in South
Eastern Nigeria. The sample mean is 4.508 which is greater than three. So the sample mean lies
in the strongly agree part of the Likert scale continuum. The calculated Z value is 7.444 which is
greater than the Table Z value at 95% confidence level. This shows that most of the respondents
strongly agree with the statement.
Capacity Building
UNDP defined capacity building as the creation of an enabling environment with appropriate
policy and legal framework, institutional development, including community participation (of
women in particular), human resources development and strengthening of managerial systems,
adding that, UNDP recognizes the capacity building is a long-term, continuing process, in which
all stakeholders participate (ministries, local authorities, non-governmental organizations and
water user groups, professional associations, academics and others). Furthermore, capacity
building is the process of developing and strengthening the skills, instincts, abilities, processes
and resources that organizations and communities need to survive, adapt, and thrive in the fast-
changing world.
Capacity building is much more than training (Urban Capacity Building Network, 2010) and
includes the following:
• Human resource development, the process of equipping individuals with the
understanding, skills and access to information, knowledge and training that enables them
to perform effectively.
• Organizational development, the elaboration of management structures, processes and
procedures, not only within organizations but also the management of relationships
between the different organizations and sectors (public, private and community).
• Institutional and legal framework development, making legal and regulatory changes to
enable organizations, institutions and agencies at all levels and in all sectors to enhance
their capacities.
141
For organizations, capacity building may relate to almost any aspect of its work: improved
governance, leadership, mission and strategy, administration (including human resources,
financial management, and legal matters), program development and implementation,
fundraising and income generation, diversity, partnerships, and collaboration, evaluation,
advocacy and policy change, marketing, positioning, planning, etc. For individuals, capacity
building may relate to leadership development, advocacy skills, training/speaking abilities,
technical skills, organizing skills, and other areas of personal and professional development.
Thus, capacity building is the elements that give fluidity, flexibility and functionality of a
program/organization to adapt to changing needs of the population that is served (Linnell, 2003).
Discussion of findings of the relationship between the indigenous capacity building theory
and the second objective
It was found that the indigenous capacity building theory has a positive correlation with the
nature of the relationship between capacity requirements planning and materials requirements
planning. The sample mean is 4.668. This shows that this mean score is in the strongly agree
Likert scale continuum. The calculated Z value is 7.812 which is greater than the table Z value at
95% confidence level which is 1.645. This shows that most of respondents strongly agree with
the statement.
Materials Requirement Planning (MRP) systems do not perform capacity planning, but they can
make it easier to plan and stay within the productive capacity. Using the MRP system, a manager
can select key productive units, such as the component facility for KBC, and have the computer
print out a “load projection” for that unit. This is done by examining orders that are currently in
production or planned. The result is a summary of the future activity of the productive unit that
allows the manager to look forward in planning the capacity. (Capacity is “planned” by
scheduling overtime, extra shifts, or subcontracting, for example.) (McClain and Thomas, 2007).
Some industries plan for very long periods of time, and therefore the plan must also include
expected orders, which are ones that may materialize. These can be listed as planned orders in
developing a load projection, but expected orders should be removed from the system after the
projection is made, to maintain the system’s validity. This information can help management to
see when a capacity problem is coming. Then overtime can be scheduled, orders can be
rescheduled, capacity can be expanded (by hiring, for example), or other actions can be taken. In
142
this manner, the detailed scheduling tool (MPR) feeds into the higher-level problem of capacity
planning (Bufer, 2007).
In the intermediate run, managers face the aggregate production work-force planning problem.
The load projection feeds back to that plan to indicate how subunits are faring within the overall
aggregate plan. Prior to that, the aggregate plan was used as input to the MRP system in two
ways. First, the work-force decisions set the capacity level shown as “current capacity”. Second,
seasonal inventory plans lead to large lot sizes to build up the required seasonal inventory. The
MRP system responds to this in the same way it responds to any demand. This two-way
interaction allows the coordination of the aggregate plan and the detailed MRP (McClain and
Thomas, 2007).
The planning horizon for the MRP system is chosen considering the capacity planning problem.
The horizon should be longer than the cumulative lead time (total lead time for the product and
its predecessors) for any product, as stated before. It should also be long enough to allow
meaningful information to pass from the MRP system to the aggregate planner, with time for
appropriate action. In a company with seasonal inventory, this means that several months would
be a minimal planning horizon and a year would be better.
Finally, there is an interaction between the capacity in a unit and the lead time required. If there
is excess capacity, planned lead time can be set close to actual production time, in that waiting
time will be small. However, during peak demand periods when capacity is fully utilized, actual
lead time will frequently be much larger than production time. This makes the selection of
planned lead times difficult. A low planned lead time will occasionally be insufficient, and a
large planned lead time will cause excessive work-in-process inventories. A manager may
choose to invest in some additional capacity in the long run to avoid this problem. In addition,
capacity usage (load) projections can be used to predict and plan for production bottlenecks and
the associated increase in lead times (Unyimadu, 2007).
It is possible to use linear or integer programming methods to plan lead times and stay within
capacities, including the potential use of overtime. This approach is not in common use today.
The use of mathematical programming is explored in several problems.
143
Discussion of findings on the relationship between the indigenous capacity planning and
the third objective
It was found that there was a positive relationship between the indigenous capacity planning
theory and the extent that capacity planning sustains an organization’s competitive advantage.
The sample mean is 4.608 greater than 3. This shows that the sample mean score lies in the
strongly agree part of the Likert Scale continuum. The calculated Z value is 8.547 which is
greater than the table Z value at 95% confidence level which is 1.645. So it shows that most of
the respondents strongly agree with the statement.
Competitive advantage refers to the particular properties of individual product and markets
which will give the firm a strong competitive position. It is something that the organization does
especially well with respect to its product and market and thus gives it an advantage over its
competitors (Evbayiro-Osagie, 2008).
A case in point is the competitive advantage which Guinness has in brewing Guinness Stout
since 1759. No other competing company, not even Nigerian Breweries Plc could brew a stout
brand that is patronized by consumers like Guinness Stout. Also, multinational brewing
companies have a competitive advantage that they can get raw materials fast from the parent
company. They can also get good prices for the raw materials.
Discussion of the Findings on the relationship of the indigenous capacity building theory
and the fourth objective
It was found that the indigenous capacity building theory has a positive relationship with the
extent of the relationship between capacity planning and capacity building. The mean score is
4.673 which is in the strongly agree part of Likert Scale Continuum. The calculated Z value is
8.914 which is greater than the table Z value at 95% confidence level which is 1.645.
An indigenous capacity-building model transcends the tendencies of the Western scientific
community to adhere to a more linear, static, time-oriented format, which is likely to impede
community involvement and discourage indigenous ownership. Rather, it must establish a
participatory process where mutual learning is taking place without the potential for abuses and
exploitation and repair lines of trust between non-indigenous researchers and tribal communities.
At the same time, however, the model must incorporate strategies for non-Native partners to
144
raise their awareness of tribal sovereignty and community issues, ensure adherence to
appropriate tribal guidelines and protocols, and become effective allies of indigenous people.
An indigenous model must reflect indigenous reality. It must integrate the past, the present and
the people’s vision for the future. It must acknowledge resources and challenges and allow
communities to build a commitment to identifying and resolving business concerns and issues.
An indigenous model works from the ground up, reversing the top-down application of Western
science to classic public enterprise that too often results in programs that are outside-in and
community placed, rather than community based (Goodman, 1998).
This literature identifies varies dimensions of capacity, such as participation, leadership, social
supports, sense of community, access to resources, and skills, and their importance in developing
and empowering local coalitions. Other parallel constructs have informed the literature on
community capacity, such as empowerment, the readiness of a community to work to improve
existing conditions, and the social capital, necessary for communities to move forward and
collaborate. Although some capacity-building models recognize the importance of community
history, they have yet to consider the importance of culture, language, issues of identity and
place, and the need for tribal people to operate in both traditional and dominant cultures. There is
now increasing dialogue among indigenous researchers about indigenous approaches to
knowledge that contrast with Western ways of knowing. These concepts go beyond cultural
competence and partnerships between Western institutions and indigenous community groups to
what Labonte (2002) called the transformation of power relationships, and to creating
frameworks based on community values and indigenous perspectives not typically included in
Western Models. Cajate (2000), for example, defined models that go beyond objective measures
and honour the importance of direct research agenda based on indigenous-centered priorities,
linking self-determination with decolonization, healing, mobilization, and transformation, which
suggests that indigenous people not only take charge of their own agenda but also name the
processes and employ methodologies that fit indigenous framing of place, community, values,
and culture.
145
Discussion of the findings on the relationship of the indigenous capacity building theory
and the fifth objective
It was found that the indigenous capacity building theory has a positive relationship with the 12
steps towards developing a capacity plan and the profitability of the brewing firms in the area
studied. The sample mean is 4.705 which is in the strongly agree part of the Likert Scale
Continuum. The calculated Z score which is 8.833 is greater than the table Z value at 95%
confidence level which is 1.645. This shows that most of the respondents strongly agreed with
the statement.
Why is capacity building needed in Nigeria?
Nigeria is a nation of slow pace of progress made her leaders inspite of the abundant natural
resources. It is therefore, pertinent to the courting of implementing capacity building on the
following major development issues, prominent among them are:
i. Human resources development through reduction of abject poverty and corruption.
ii. Improvement in the provision of social services and infrastructures.
iii. Growth in employment and income generation.
iv. Increased agricultural productivity, science and technological advancement.
v. Increased emphasis on harnessing science and technology for rapid development.
vi. Environmental protection and regeneration of the natural resources bases.
vii. Good standard of education, in order to eradicate illiteracy.
viii. Better health condition for the masses.
ix. Women empowerment – this is necessary because women play an important role,
directly or indirectly in the listed activities above.
x. Provision of good governance and strategic management of resources.
xi. The imperative need for changing current practices for national development.
4.5 DISCUSSION OF FINDINGS
4.5.1 Results related to the first objective
Research Objective One: To determine the extent to which capacity planning enhances
performance in the brewing industry in South Eastern Nigeria
For the purpose of the concise and focus discussion, Table 4.4 will be relevant. 300 out of the
740 respondents making 40.501% of them strongly agreed while 367 of them making 49.595 per
cent of them agreed that capacity planning to a large extent enhances the performance in the
146
brewing industry in South Eastern Nigeria. The extent of agreement in this statement is also
shown in which the mean score is 4.239 which exceeds the cutoff point of 3.00, and this is in
agreement with the contention of Schuler and Youngblood, 2006) that says that brewing
companies in general and brewing companies in particular take the issue of capacity planning
and performance as very important. This is because without capacity planning, they will not be
able to determine the production capacity of their facilities in terms of the inputs, throughput or
produces and output and performance in terms of the extent to which they achieve or achieving
their organizational objectives
The finding that 9 out of 10 respondents said that capacity planning to a large extent enhanced
the performance in the brewing sector in South Eastern Nigeria has some implications. It meant
that the production capability of a brewingfirm could improve the ability of the brewing
organization to achieve its organizational objectives. Yomere and Osaze (2000) explained that an
objective is a short-term aim at a point in the organization’s mission.
No wonder Davis and Mabert (2000) have observed that in brewing organizations, many
important capacity planning decisions are made in production planning activities. These
decisions make the production planners to know when and with what resources organizations
produce their outputs optimally. These outputs are one of the ways of determining capacity
planning. The methods used to create the capacity plans are crucial in enhancing organization
performance. Organizational performance apart from being seen from the perspective of the
ability to achieve organizational objectives is also seen from the perspective of the ability of the
organization to fulfil the promises made to the stakeholders.
In the research question to this objective, the 740 respondents were asked whether capacity
planning to a large extent enhanced the performance in the brewing industry in South Eastern
Nigeria. 9 out of 10 of them said that to a large extent capacity planning enhanced the
performance in the brewing industry in South Eastern Nigeria and 1 out of 16 respondents had a
contrary opinion. This finding is consistent with the contention of Davis and Mabert (2000) that
in organizations, many capacity planning decisions are made in production planning which
enhance organizational performance.
In the hypothesis of this objective, it was found that capacity planning to a large extent enhanced
the performance in the brewing industry in South Eastern Nigeria. This finding was consistent to
147
that of Davis and Mabert (2000) that in many brewing organisations, many capacity planning
decisions are made in production planning that enhance organizational performance.
The alternative hypothesis was that capacity planning to a large extent enhanced the performance
in the brewing industry in South Eastern Nigeria. The hypothesis was tested in Table 4.8. The
calculated Z value was 6.893 to 3 decimal places which was greater than the Table value of
1.645. So the Null hypothesis was rejected and the alternative hypothesis was accepted. This
showed that capacity planning to a large extent enhanced the performance in the brewing
industry in South Eastern Nigeria. This means that to a large extent as capacity planning
increased, the performance of the organizations studied increased.
Item 1 of table 4.4 states that capacity planning to a large extent enhances the performance in the
brewing sector in South Eastern Nigeria, in support of this statement, 300 (40.541%) and 367
(49.595%) convincingly agreed.
Item 1 of table 4.6 states that capacity planning to a little extent enhances the performance in the
brewing sector in South Eastern Nigeria. , the responses were Strongly Agree, Agree, Undecided,
Disagree and Strongly Disagree. They had frequencies of 25, 25, 23, 367 and 300 out of 740
respectively. These gave relative frequencies of 0.034, 0.034, 0.031, 0.496 and 0.405
respectively.
From the oral interview, the 740 respondents were asked the extent to which capacity planning
enhanced the performance in the brewing sector in South Eastern Nigeria and 9 out of 10
respondents said it enhanced it to a large extent while 1 out of 10 respondents gave answers to
the contrary.
In the analysis of the responses to the dichotomous oral interview questions on the relationship
between the contingency theory and the five objectives, the 740 respondents were asked whether
contingency theory is related to capacity planning to enhance the performance in the brewing
industry, 738 out of 740 respondents making 99.73% of them said yes. 2 of them making 0.27%
of them said no.
In the analysis of the responses on the relationship between the multi period capacity problem
and the five objectives, the 740 respondents were asked whether the multi period was relevant
148
for capacity planning to enhance the performance in the brewing industry. 739 out of 740 of
them giving 99.86% of them said yes. 1 of them making 0.14% of them said no.
In the results related to the contingency theory and multi period capacity problem, it was found
that contingency theory was related to capacity planning to enhance the performance in the
brewing industry.
4.5.2 Results related to the Second Objective
Objective two: To ascertain the nature of relationship between capacity requirements
planning and materials requirements planning
The finding that 91 out of 100 respondents said that capacity requirements planning had a
positive relationship with materials requirements planning had some implications, the mean
value was got as 4.234 which is more than 3.00. This meant that as the determination of the
capacity needs of the brewing organization increases, the determination of the materials needs
increases. This is because as Berry, Schmidt and Vollmann (2004) have observed, capacity
requirements planning utilizes the time-phased material plan information produced by the
materials requirements planning system. So as the capacity needs and the time to utilize them are
determined the material’s needs and when and how they should be met are also determined.
This includes the consideration of all actual lot sizes as well as the lead times of when orders are
placed and when the materials are received. It includes balancing the open shop orders, schedules
and their receipts. If this is not done both capacity and materials may not be sufficient when they
are needed. This may lead to interruptions in production and this may lead to customer
complaints and loss of goodwill (Arnold and Chapman, 2011).
The research question to this objective, the 740 respondents were asked the nature of the
relationship between capacity requirements planning and materials requirements planning, 91 out
of 100 respondents said that capacity requirements planning had a positive relationship with
materials requirements planning. This finding is consistent with the contention of Berry et al
(2004) that capacity requirements planning utilized time phased materials plan information
produced by the materials requirements plan.
In the hypothesis of this objective, it was found that there was a significant positive relationship
between capacity requirements planning and materials requirements planning. This finding was
149
consistent with that of Berry et al (2004) that capacity requirements planning utilized time-
phased materials plan information produced by the materials requirements plan.
The alternative hypothesis was that there was a significant positive relationship between capacity
requirements planning and materials requirements planning. The hypothesis was tested in Table
4.9. The Spearman’s rank correlation between capacity requirements planning and materials
requirements planning was 0.95 which is very close to 1, so the null hypothesis was rejected and
the alternative hypothesis was accepted. This showed that there was a very high positive
correlation between capacity requirements planning and materials requirements planning. This
meant that as one increased, the other increased at the same rate.
Item 2 of table 4.4 states that there is a significant positive relationship between capacity
requirements planning and materials requirements planning. In support of this statement 304
(41.081%) and 370 (50.000%) strongly agreed and agreed.
Item 2 of table 4.6 states that there is a significant negative relationship between capacity
requirements planning and materials requirements planning, the responses were Strongly Agree,
Agree, Undecided, Disagree and Strongly Disagree. They had frequencies of 21, 23, 22, 370 and
304 out of 740 respectively. These gave relative frequencies of 0.028, 0.031, 0.30, 0.500 and
0.411 respectively.
From the oral interview, the 740 respondents were asked the nature of the relationship between
capacity requirements planning and materials requirements planning and 91 out of 100
respondents said that capacity requirements planning had a positive relationship with materials
requirements planning while 9 out of 100 respondents gave answers to the contrary.
In the analysis of the responses to the dichotomous oral interview questions on the relationship
between the contingency theory and the five objectives, the 740 respondents were asked whether
the contingency theory is related to the nature of the positive relationship between capacity
requirements planning and materials requirements planning, 730 out of the 740 respondents
making 99.46% of them said yes. 6 of them making 0.54% of them said no.
In the analysis of the responses on the relationship between the multi period capacity problem
and the five objectives, the 740 respondents were asked whether the multi period capacity
problem was relevant to the nature of the positive relationship between capacity requirements
150
planning and materials requirements planning. 737 of them making 99.59% of them said yes. 3
of them making 0.41% of them said no.
In the results related to the contingency theory and multi period capacity problem, it was found
that contingency theory related to the nature of the positive relationship between capacity
requirements planning and materials requirements planning.
4.5.3 Results related to the Third Objective
Objective three: To ascertain the extent to which capacity planning sustains organizations’
competitive advantage
The finding that 23 out of 25 respondents said that to a large extent, capacity planning sustained
the organization’s competitive position had some implications, the mean value was 4.253 which
is greater than 3.00. It meant that ensuring the adequate production capability of a
brewingfacility gives the brewing organization advantages over firms in the same line of
business. The ability to match capacity requirements planning with materials requirements
planning gives the particular brewing company a competitive edge (Chen and Paulraj, 2004).
No wonder the two brewing companies namely the Nigerian Breweries Plc and Guinness Nigeria
Plc with the highest capacities as shown by the numbers of factories they have are multinational
in their operations. The multinational nature of their operations gives them high competitive
positions because they have access to malted barley from their parent companies, they have good
financial resources and they have access to good training and development facilities for their
staff (Guinness Nigeria Plc, 2011). As Chen, Paularaj and Lado (2004) put it; capacity planning
to a large extent sustains the organization’s competitive position in such areas as strategic
purchasing and supply management giving the organization good product-market scope, goals
and objectives and distinctive competence.
The research question to this objective, the 740 respondents were asked the extent to which
capacity planning sustained the organizations’’ competitive advantage, 23 out of 25 respondents
said that to a large extent capacity planning sustained the organizations’ competitive position
while 2 out of 25 respondents gave answers to the contrary. This finding agreed with the
contention of Chen et al (2004) that capacity planning enhanced the organizations’ competitive
position in such areas as strategic purchasing and supply chain management.
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In the hypothesis of this objective, it was found that capacity planning to a large extent sustained
the organizations’ competitive position. This finding is in line with that of Chen et al (2004) that
capacity planning enhanced the organizations’ competitive position in such areas as strategic
purchasing and supply chain management.
The alternative hypothesis was that capacity planning to a large extent sustained organization’s
competitive advantage. The hypothesis was tested in Table 4.10. The calculated Z value was
8.271 which is greater than the Table value of 1.645. So the null hypothesis was rejected and the
alternative hypothesis was accepted. This shows that capacity planning to a large extent
sustained the organization’s competitive advantage. So this showed that capacity planning
increased along the same line with the competitive advantage of the organization. So it meant
that capacity planning could be used as a tool to make an organization have a better competitive
edge over its rivals.
Item 3 of table 4.4 states that capacity planning to a large extent sustains the organization’s
competitive position, in response to this statement, 302 (40.811%) and 380 (51.351%)
convincingly agreed.
Item 3 of table 4.6 states that capacity planning to a low extent sustains the organization’s
competitive position, the responses were Strongly Agree, Agree, Undecided, Disagree and
Strongly Disagree. They had frequencies of 19, 19, 20, 380 and 302 out of 740. These gave
relative frequencies of 0.026, 0.026, 0.027, 0.514 and 0.408 respectively.
From the oral interview, the 740 respondents were asked the extent to which capacity planning
sustained the organization’s competitive position and 20 out of 5 respondents said that capacity
planning to a large extent sustained the organization’s competitive position while 5 out of 25
respondents gave answers to the contrary.
In the analysis of the responses to the dichotomous oral interview questions on the relationship
between the contingency theory and the five objectives, the 740 respondents were asked whether
the contingency theory is related to ascertaining the large extent of the relationship between
capacity planning and capacity building. 734 out of the 740 respondents making 99.19% of them
said yes, while 6 of them said no making 0.81% of them.
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In the analysis of the responses on the relationship between the multi period capacity problem
and the five objectives, the 740 respondents were asked whether the multi period capacity
problem was relevant for ascertaining the extent of the positive relationship between capacity
planning and capacity building. 735 of them making 99.32% of them said yes. 5 of them making
0.68% of them said no.
In the results related to the contingency theory and multi period capacity problem, it was also
found that the contingency theory related to ascertaining the large extent of the positive
relationship between capacity planning and capacity building
4.5.4 Results related to the fourth objective
Objective four: To determine the extent of relationship between capacity planning and
capacity building
The finding that 93 out of 100 respondents said that capacity planning to a large extent had a
positive relationship with capacity building has some implications, and the mean value was
4.280 which was greater than 3.00. It meant that as the production capability of the brewing
facility increased, its creation of an enabling environment with appropriate policy and legal
framework, institutional development, including community participation of women in
particular, human resources development and strengthening of managerial systems increases. No
wonder Billey and Tesar (2008) have observed that capacity planning to a large extent has a
positive correlation with capacity building through internalization.
Internalisation which is the process of increasing the involvement in international operations has
developed overtime. A very good example of a brewing company that has been involved in
internationalization is Guinness Overseas Limited. From 1759 when the first Guinness beer was
brewed in Saint James’s gate in Dublin Ireland to date, the company has grown tremendously.
Guinness is now brewed in over 20 countries in the world. In Nigeria there are Guinness
Breweries at Ogba, Ikeja and Benin. Guinness is also brewed at Aba at the former Dublic
Factory Plant. Guinness is also brewed in Jos in the Jos metropolitan Factory Plant (Guinness
Nigeria Plc, 2011).
In the research question to this objective, the 740 respondents were asked the extent of the
relationship between capacity planning and capacity building and 93 out of 100 of them said that
to a large extent there was a positive relationship between capacity planning and capacity
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building and 7 out of 100 respondents gave answers to the contrary. This finding is consistent
with the contention of Billey and Tesar (2008) that capacity planning to a large extent has a
positive correlation with capacity building through internationalization.
In the hypothesis of this objective, it was found that there was a positive significant relationship
between capacity planning and capacity building. This finding is consistent with that of Billey
and Tesar (2008) that capacity planning to a large extent has a positive correlation with capacity
building.
The alternative hypothesis was that there was a positive significant relationship between capacity
planning and capacity building. The hypothesis was tested in Table 4.11. It was found that the
Spearmans’ rank correlation between capacity planning and capacity building was 0.9 which was
very close to 1. The null hypothesis was rejected and the alternative hypothesis was accepted at
5% level of significant. This showed that there was a significant positive relationship between
capacity planning and capacity building. This showed that as one increased, the other also
increases at the same rate and in the same direction.
Item 4 of table 4.4 states that there is a significant positive relationship between capacity
building and capacity planning, in response to this statement, 308 (41.622%) and 381 (51.486%)
convincingly agreed.
Item 4 of table 4.6 states that there is a negative correlation between capacity building and
capacity planning, the responses were Strongly Agree, Agree, Undecided, Disagree and Strongly
Disagree. They had frequencies of 16, 18, 17, 381 and 308 out of 740 respectively. These gave
relative frequencies of 0.022, 0.024, 0.023, 0.575 and 0.416 respectively.
From the oral interview, the 740 respondents were asked the extent of the relationship between
capacity planning and capacity building and 93 out of 100 respondents said that to a large extent
there was a positive relationship between capacity planning and capacity building, 7 out of 100
respondents gave answers to the contrary.
In the analysis of the responses to the dichotomous oral interview questions on the relationship
between the contingency theory and the five objectives, the 740 respondents were asked whether
the contingency theory related to the large extent to which capacity planning sustained the
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organization’s competitive advantage. 734 out of the 740 respondents making 98.92% of them
said yes. 8 of them making 1.08% of them said no.
In the analysis of the responses on the relationship between the multi period capacity problem
and the five objectives, the 740 respondents were asked whether the multi-period problem
relevant to a large extent to which capacity planning sustained the organization’s competitive
advantage. 733 of them making 99.05% of them said yes. 7 of them making 0.95% of them said
no.
In the results related to the contingency theory and multi period capacity problem, it was found
that the contingency theory was related to the large extent to which capacity planning sustained
the organization’s competitive position.
4.5.5 Results related to the fifth objective
Objective five: To access the steps toward developing a capacity plan and the profitability
in the brewing firms in the area studied
The finding that there are 12 steps of the capacity plan to improve the profitability of the brewing
industry in South Eastern Nigeria has some implications, the mean value was 4.305 which was
greater than 3.00. The steps could be summarized as determining the future capacity needs,
ascertaining the present capacity needs, deciding what to do if the future capacity needs exceed
or are lower than the present capacity needs; and executing the capacity decision and
implementing the capacity decision. Guinness Nigeria Plc has taken capacity decisions which
involved brewing Guinness, Harp and Malt in former Dubic Factory Plant at Aba and Jos
Metropolitan Factory Plant at Jos (Guinness Nigeria Plc, 2011).
This was when the future capacity needs were more than the present capacity needs. Other
capacity decisions they could have taken included outsourcing, or building new brewing plants.
To build a new factory plant is very costly. The brewing firm has to do a plan to determine the
project concept, facility location and layout, demand, financial feasibility, legal feasibility etc
which is not easy to do (Unyimadu, 2008).
In the research question to this objective, the 740 respondents were asked the steps that would be
used to develop capacity planning that would affect profitability in the brewing industry in South
Eastern Nigeria, it was found that there were 12 steps in a descending order starting from the
determinants of service requirements and ending at planning the future system configuration.
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In the hypothesis of this objective, it was found that the steps towards developing a capacity plan
that would affect profitability in the brewing industry in South Eastern Nigeria were of the same
order of magnitude.
The alternative hypothesis was that the steps towards developing a capacity plan that would
affect profitability in the brewing industry in South Eastern Nigeria were of the same order of
magnitude. The hypothesis was tested in Table 4.12 and the calculated Z value was 9.558 while
the Table Z value was 1.645, so the null hypothesis was rejected and the alternative hypothesis
was accepted at 5% level of significance. This showed that the steps towards developing a
capacity plan that would affect profitability in the brewing industry were of the same order of
magnitude. This meant that each of the 12 steps even though they were in a hierarchy, each had
equal weight though the observed frequencies differed.
Item 5 of table 4.4 states that the steps towards developing capacity plan positively affected
profitability in the brewing industry in the area studied, in response to this statement, 315
(42.48%) and 381 (51.486%) convincingly agreed.
Item 5 of table 4.6 states that there are no steps towards developing capacity plan to improve the
profitability in the brewing industry in the area studied. the responses were Strongly Agree,
Agree, Undecided, Disagree and Strongly Disagree. They had frequencies of 15, 15, 14, 381 and
315 out of 740. These gave relative frequencies of 0.020, 0.020, 0.019, 0.515 and 0.426
respectively.
From the oral interview, the 740 respondents were asked the steps that could be used to develop
a capacity plan to improve the profitability in the brewing industry in South Eastern Nigeria. The
12 steps found were to determine service level requirements, to define work loads, to determine
the unit of work, to determine the service levels of each work load, to analyse current system
capacity, to measure service levels, to measure the overall resource usage, to measure resource
usage by workload, to identify the components of response time, to plan for the future, to
determine the future processing requirements and to plan the future system configuration in a
descending order of magnitude.
In the analysis of the responses to the dichotomous oral interview questions on the relationship
between the contingency theory and the five objectives, the 740 respondents were asked whether
156
contingency theory related to the steps of capacity plan that positively affects to a large extent to
which capacity planning positively affected the profitability in the brewing industry. 730 out of
the 740 respondents making 98.65% of them said yes. 10 of them making 1.35% of them said no.
In the analysis of the responses on the relationship between the multi period capacity problem
and the five objectives, the 740 respondents were asked whether the multi period capacity
problem was relevant to the development of the steps of the capacity plan that positively affect
the profitability in the brewing industry. 731 of them making 98.78% said yes. 9 of them making
1.22% of them said no.
In the results related to the contingency theory and multi period capacity problem, it was found
that the steps of capacity planning which to a large extent aimed to develop a capacity plan that
positively affected the profitability of the brewing industry.
4.6 DISCUSSION RELATED TO THE CONTINGENCY THEORY AN D MULTI
PERIOD CAPACITY PROBLEM
The contingency theory assumes that the situation dictates the extent to which capacity planning
enhances the performance in the brewing industry in terms of profitability, effectiveness and
competitive advantage.
In brewing organizations, many important decisions are made in production planning activities.
Production planners decide when and with what resources organizations produce their outputs.
The methods that are used to create the plans are crucial to organizational performance (Kanet
and Sridharan, 1998; Davis and Mabert, 2000; Zwikael and Sadeh, 2007). Poor methods yield
plans that are either too loose and result in excessive lead times or too tight and result in failures
to keep promised delivery dates. Consequently, it is not surprising that planning methods have
represented a major research area in the operations management literature. Different planning
techniques have been studied especially in analytical and simulation-based research (Kouvelis et
al, 2005). That stream of research has produced various sophisticated algorithms that enable the
leveling and optimization of production plans (e.g., Davis and Mabert, 2000; Yang et al, 2002;
Deblaere et al, 2007).
Meanwhile, however, empirical researchers have repeatedly observed that most practitioners use
considerably less sophisticated planning methods than what is discussed in the academic
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literature. Moreover, empirical evidence indicates that those practitioners using advanced
planning methods are on average less satisfied with their plans then those who use simpler and
less accurate methods (Johnson and Mattsson, 2003). This section aims to use process
complexity as a contingency factor that explains why the practices of production planning often
differ from the academic model of production planning.
The analysis of this section employs the logic of strong inference and the contingency theory of
organizations to explain the determinants of different planning methods’ effectiveness. The
strong-inference logic refers to a research design, where theory building is based on tests of
competing hypotheses (Platt, 1964). The contingency-theoretical perspectives to process
complexity (e.g., Thompson, 1967) are used to propose that sometimes the most sophisticated
planning methods may be less effective than the simpler techniques. The contingency hypothesis
is tested against a hypothesis about the universal superiority of the most advanced planning
methods. The statistical results from the survey dataset are complemented by the interview
dataset that sheds light on the reasons why practitioners end up using certain planning methods.
Planning is necessary in all complex organizations. In the absence of planning, different work
units may pursue the possibly conflicting objectives of their own (March and Simon, 1958).
However, not all organizations are complex and thus heavy planning efforts are not always
necessary. In simple settings, where specialization, action variety, and task interdependence are
low, coordination can be achieved through rules and heuristics (Cyert and March, 1963). In
manufacturing management, the planning-focused methods have been developed around the
concept of material requirements planning (MRP, Orlicky, 1975), while the methods that
emphasize rule-based control and simplicity are founded on the just-in-time (JIT) methodology
(Ohno, 1988).
A classic way to pursue simplification in brewing is to isolate operations from external
uncertainties (Thompson, 1967). The extent of the isolation depends greatly on the order
penetration point (Olhager, 2003): the earlier the order-specific requirements are taken into
account, the higher is the exposure to the environment. That is why planning methods are most
important in the MTO manufacturing and the JIT methods are at their best in the make-to-stock
environments (Karmarkar, 1989; Vollmann et al, 2005). Usually both approaches coexist in
assemble-to-order systems and other intermediate settings. The postponement of the order
penetration point enables the use of JIT methods in the upstream operations of customized
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manufacturing (Olhager and Rudberg, 2002). However, the inherent complexity of producing
according to individual orders cannot be eliminated by forcing JIT methods upon the MTO parts
of the processes (Hopp and Spearman, 2004). Hence, the time-phased planning has remained as a
vital part of manufacturing management despite the important contributions of JIT. Recent
literature has described several techniques for integrating the benefits of the two paradigms. The
techniques are known by many names (e.g., CONWIP, POLCA, COBACABANA, etc.) and they
differ in details but they share the main idea of using the pull logic of JIT for the purposes of
shop floor control and time-phased planning methods for the creation of production schedules
(Spearman et al, 1990; Suri, 1998; Land, 2009).
Contemporary methods of time-phased production planning are based on the manufacturing
resource planning (MRPII) framework. It was originally developed to complement MRP with
capabilities to check material plans’ feasibility against capacity constraints (Landvater and Gray,
1989). Later, more advanced applications of MRPII have been developed so that the feasibility
checks could be extended to other factors such as delivery schedules and financial constraints
(Yusuf and Little, 1998). However, the practical implementations of such solutions have
remained rare (McKay and Wiers, 2004). In fact, it has been observed that even the capacity
planning features of MRPII are far less utilized than what could be expected on the bases of the
academic literature (Halsall et al, 1994; Kemppainen, 2007). As the material-planning parts of
MRPII are well-established (Vollmann et al, 2005), the observation implies that companies’
production planning practices can be measured through the methods that they use in capacity
planning.
Recent developments in enterprise software deliver a promise of easily applicable capacity
planning tools. While the conventional ERP systems are well-suited for the simpler capacity
checks (Wortmann et al., 1996), the so-called advanced planning and scheduling (APS) systems
promote the more sophisticated methods (Kreipl and Pinedo, 2004; Stadtler and Kilger, 2005).
However, companies’ diligence in applying their enterprise systems’ features is known to vary
considerably (e.g.., Bendoly and Cotteleer, 2008). Thus, variance may be found also in the
utilization of the capacity planning features. That variance enables testing whether complex
organizations that do not put efforts in planning suffer from the lack of coordination (e.g., March
and Simon, 1958; Zwikael and Sadeh, 2007). Consequently, the following hypothesis is
presented as the underlying assumption of the study:
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It is reasonable to assume that not only the efforts in planning but also the ways of planning
matters. The main method of time-phased production planning according to the framework of
Vollmann et al (2005) is described below:
1. Non-Systematic capacity planning
- Materials (Mater Production Scheduling) (Material Requirements Planning)
- Bills of materials (Material Requirements Planning)
- Priority scheduling rules (Input/output Control)
2. Rough-Cut Capacity Planning (RCCP)
- Rough-cut profiles
3. Capacity Requirements Planning
- Capacities (labour and machines)
- Work enters
- Routings
4. Finite Loading with Capacity Leveling
- Shift schedules
- Move time matrices
- Setup time matrices
- Capacities (tool, jigs, etc.)
5. Finite Loading with Optimization
- Objective functions
- Parameters (processing/setup/delay/costs, products/customer priorities, etc.)
Alternative methods in capacity planning.
The practical relevance of the framework is high because dominant ERP software providers have
structured their production planning modules in the same fashion (e.g., SAP, 2009). In addition,
most textbooks either refer to it directly or provide illustrations that closely resemble it (e.g.,
Hill, 2005; Slack et al., 2007; Stevenson, 2004).
The backbone of the planning process is in the material planning activities, that is: master
production scheduling (MPS), MRP, and the input/output (I/O) control (Vollmann et al., 2005).
The optional activities are on the side of capacity planning. In the illustration, they are numbered
in the order of sophistication. The illustration shows that the amount of required data records
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increases as the methods get more sophisticated. The increase is cumulative because the records
do not fully substitute each other. Brief descriptions of each method are given in the following:
Non-systematic capacity planning represents inexplicit consideration of capacity constraints. At
the level of mater schedules, it means that planners use their personal experience to evaluate the
feasibility of plans (Proud, 2007). In MRP, the inexplicit capacity considerations are realized
through the lead time parameters of bills of materials. The processing lead times represent the
averages, while the variances around the averages are taken into account with safety lead times
(Vollmann et al, 2005). In the I/O control, priority scheduling rules can be used to level capacity
utilization without formal planning activities (Green and Appel, 1981; Kemppainen, 2007).
Rough-cut capacity planning (RCCP) is the simplest systematic method. It can be done with
several techniques but they all share the common characteristic of aggregation (Wortmann et al.,
1996). Materials are aggregated to end products or product groups and capacities to production
lines or resource groups (Proud, 2007). RCCP simplifies planning by ignoring subassembly
inventories, operations’ sequences, setups, and batch sizes but still provides the planners with a
systematic means to supervise how the resource utilization accumulates during the MPS activity
(Vollmann et al, 2005). That is an advantage if mater schedules are updated frequently, MPS
items are numerous, or different MPS items load the same resources. In such situations, the non-
systematic methods are prone to human errors and easily result in overloaded schedules.
Capacity requirements planning (CRP) provides a more detailed technique for checking material
plans’ feasibility. The CRP calculations are done not only for the end products but also for the
subassemblies. In addition, the routing data enable calculation loads at individual resources and
considering the effects of operations’ sequences, setups, and batch sizes. Thus, CRP corrects for
the simplifications of RCCP and helps generating more reliable schedules. Iterating the plans to
achieve feasibility in terms of resources’ capacity limits is done manually by human planners
(Burcher, 1992; McKay and Wiers, 2004).
The next step from CRP is to automate the iterations of the plans. It can be done with finite
loading methods that are usually featured in APS systems (McKay and Wiers, 2004). The
process of using them is typically the following: first, material plans are downloaded from an
ERP system. Then, the algorithms of the finite leading software are used to find a solution,
where capacity constraints are satisfied with the fewest breaches of due dates. Finally, the
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revised plans are uploaded back to the ERP system, where they are executed (Stadtler and
Kilger, 2005). The obvious benefit of automating the capacity leveling is that it reduces the room
for human errors.
In addition to capacity leveling, the finite loading algorithms can be used to solve more
complicated scheduling problems. The finite leading tools with optimization may be used, for
example, to maximize throughput or to minimize setups or downtimes (e.g., Davis and Mabert,
2000). Such techniques require the most planning parameters and their outputs are highly
dependent on the accuracy of the parameters. Yet, the data maintenance efforts and the
investments in the software may well be justified in some manufacturing environments, for
example in capital intensive production systems (Kreipl and Pinedo, 2004; Stadtler and Kilger,
2005).
The planning methods are by no means mutually exclusive. Instead, several methods can be used
simultaneously for different purposes (Meal, 1984). For example, plant managers can use RCCP
to evaluate sales plans, master schedulers may use CRP to supervise their processes, and
production planners can do the finite loading of critical resources. A concept that brings clarity to
this plurality is bottom-up re-planning (Fransoo and Wiers, 2008; Vollmann et al., 2005). It
means that master schedules are updated on the bases of the lower-level planning activities. In a
closed-loop planning system, themaster schedules are based on the finite loading of critical
resources (Kenat and Sridharan, 1998). In an intermediate solution, the master schedules are
revised on the bases of CRP. Consequently, the main method of planning can be identified. It is
the method that determines the output to which the manufacturing function commits itself.
As all of the advanced planning methods aim to reduce errors in planning, it can be proposed that
they should have a positive effect on operational performance. Some studies have already
implied evidence of such as effect (Sheu and Wacker, 2001; Wacker and Sheu, 2006). Yet, they
have not included finite loading techniques, which is a major shortcoming because substantial
effort has been put into their development (Kouvelis et al, 2005). The development of
progressive algorithms and software would be well justified if there was evidence on the
relationship between the accuracy of planning and performance.
Another perspective to different planning methods’ effectiveness is to assume that methods’
suitability would depend on the context of their usage. Preliminary support for such an argument
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can be found in the surveys of Jonsson and Mattsson (2002:2003). They show that practitioners’
satisfaction with different planning techniques depends on the type of their production processes:
the managers of job shops are content with RCCP, the most satisfied users of CRP work in
batch-process plants, and the finite loading methods are most popular in production lines.
The observations are aligned with the systematic review of Sousa and Voss (2008) which
indicate that the process type is a typical contingency factor for the effectiveness of various
operations management practices. In the context of planning, the influence of the process type
can be explained with two classic contingency-theoretical constructs: the repetitiveness and the
complexity of the tasks that constitute the processes (Perrow, 1967; Woodward, 1965).
- RCCP fits with the job shops because in low-volume and high-variety
environments, the data records of the more detailed methods are difficult to
maintain. Moreover, the more detailed resource-specific plans are not necessary
because the complexity of the system is limited with general-purpose machinery
and widely skilled workforce (Blackstone and Cox, 2005; Hill, 2007).
- CRP fits with the batch processes because the more repetitive operations make the
maintenance of the data records worthwhile. Furthermore, information about the
resource-specific workloads is necessary because the resources are more
specialized, and different products utilize them differently (Jonsson and Mattsson,
2003; Wortmann et al., 1996).
- Finite loading methods fit with batch processes, whose complexity is reduced
with bottleneck control (Goldratt and Cox, 1984; Vollmann, 1986). Finite loading
works in a batch process if a stationary bottleneck can be identified and all other
resources are subordinated to its schedule. Otherwise, each finite loading of one
resource can make another resource a new bottleneck, and consequently the
iteration of the plans may become endless.
- In production lines, the complexity is low because all resources are subordinated
to the flow of the line. Thus, the capacity of the entire line can be planned as a
single resource. Detailed planning is desirable because untimely changeovers can
be costly in larger assembly lines (Hayes and Wheelwright, 1979; Kreipl and
Pinedo, 2004) or cause congestion in smaller manufacturing cells (Venkatesan,
1990; Vandaele et al, 2008). In addition, the repetitiveness of operations makes it
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easier to maintain the parameters of the most sophisticated methods (Safizadeh
and Ritzman, 1997; Stadtler and Kilger, 2005).
The relationship between the process types and planning methods can also be explained with the
interdependence between the resources of the processes. As discussed earlier, the alternative
types of interdependence are pooled, sequential, and reciprocal (Thompson, 1967; Donaldson,
2001). The pooled and the sequential processes are the simplest to coordinate but they have very
different implications for planning (Barki and Pinsonneault, 2005). The processes with pooled
resources are inherently flexible, and that is a capability that should not be constrained with too
stringent planning. A job shop is an archetype of pooled interdependence (Galbraith, 1973).
Meanwhile, the sequential processes are suited for efficiency, while is a capability that can be
fostered with detailed planning. In manufacturing environments, sequential relationships exist in
production lines and around the bottlenecks of batch processes (Thompson, 1967; Woodward,
1965).
The most difficult processes to coordinate are those where resources are reciprocally
interdependent. That is because all actions by any resource may affect multiple other resources
(Galbraith, 1973; Monahan and Smunt, 1999). Some specificity in planning is necessary to
prevent undesirable cascade effects but getting into the details is difficult because the possible
interactions are numerous (Tushman and Nadler, 1978). Therefore, a moderately sophisticated
planning method such as CRP is the most suitable option for the reciprocal processes of batch
shops (Reeves and Turner, 1972).
In summary, classic contingency-theoretical concepts produce a meaningful fit proposition that
challenges the hypothesis on the universal superiority of the most sophisticated planning
methods.
The existence of two competing hypotheses calls for a strong inference research design. It is an
inductive approach, where theory building is based on tests of mutually excluding hypotheses
(Platt, 1964). Strong inference studies must be carefully designed so that the research settings do
not favour any of the rival hypotheses (MacKenzie and House, 1978). Multiple data sources are
also necessary: quantitative data enable the testing of the hypotheses while qualitative data
provide the understanding that is needed in the development of theory (Jick, 1979; Gupta et al,
2006). Although the strong inference research design was originally developed for experimental
164
studies (e.g., Nadler et al., 2003), it has been employed successfully in non-experimental
empirical research as well (e.g., Shaw et al., 2005).
The efforts in capacity planning were operationalized with two formative indicators. They
represent the main aspects of efforts in formal routines: the organizational deployment of the
routine (i.e., the structuration aspect), and individuals’ efforts to follow the routine in their work
(i.e., appropriation, DeSanctis and Poole, 1994). The formative operationalization is suitable
because the latter aspect does not always follow from the former and because studies have shown
that both aspects are necessary for the routines to be effective (Devaraj and Kohli, 2003). This
simple operationalization was used because the more sophisticated measures of planning efforts
are typically tied to certain planning methods (e.g., Zwikael and Sadeh, 2007).
A strategy in multi-period problem contains two types of decisions: the sequence of contracts to
be used and the amount of capacity to acquire after choosing the sequence of contracts. There are
an exponential number of combinations of contracts that the manufacturer can choose from. To
evaluate one strategy, the firm needs to solve a large scale stochastic linear problem, e.g.
Problem (11), to find the optimal contract sizes. Therefore, the multi-period problem is much
more complex than the single period problem.
In the following sections, we will develop an efficient heuristic algorithm that can find a good
capacity plan for the multi-period problem under Assumption 1. The same heuristic algorithm
will also provide a good upper bound to verify the effectiveness of the capacity plan.
The expectations of the demands during the planning horizon are given. Product 1 is introduced
to the market at the beginning of the first month. Its demand grows with time and reaches its
peak at the fifth month. After that, the market is saturated and the demand starts to drop. Product
2, on the other hand, is a mature product at the beginning and as time passes by, it phases out. At
the seventh month, the manufacturer introduces a new version of product 2 and it starts to gain
more demand from then on. The standard deviations of the demands of both products at each
period are 10.
Both products are sold at N9,750. All processes have the same price structure. Each process
offers contracts in four different durations: 1 month, 3 months, 6 months and 12 months. The
corresponding prices of the fixed-price and option contracts are also given. The contracts with
longer duration have lower per-period prices.
165
Given the supply chain structure, demand information and contract information, the
manufacturer needs to make the following decisions:
1. What sequence of contracts that it should use for each process,
2. What types of contract (fixed-price and option) that it should use, and
3. How much capacity it should reserve or buy for each type of contract (Barahona et al,
2005).
Decision 2 and 3 are the same as in the single period case while decision 1 is unique to the multi-
period problem. Since the example only contains dedicated resources, the manufacturer does not
need to choose suppliers. However, similar to the single period problem, the firm still faces the
other trade-offs that involve demand uncertainty, common process, coordination among the
processes of the same product, and option capacity. Moreover, the manufacturer needs to
consider the trade-off between contract flexibility and prices. Should it use shorter contracts to
match the demand or should it take advantage of lower prices by using longer contracts?(Simchi-
Levi, 2005).
For this example, the sequences of the contracts for the processes suggested by the algorithm are
given.
1. For process 1, the manufacturer should use two 1-month contracts to cover the first two
periods. It can then obtain a 6-month contract to cover month 3 to month 8. Following
another 1-month contract in month 9, it should get a 3-month contract to cover the rest of
the planning horizon.
2. For process 2, the manufacturer should take full advantage of the low price from a longer
contract and secure the capacity for 12 months with the 12-month contract.
3. For process 3, the manufacturer should use a 3-month contract to cover month 5, 6, and 7.
For the other months, it should use 1-month contracts (Devaraj and Kohli, 2003).
The contracts reserved for process 3 vary to match the demands. On the other hand, the contract
reserved for process 2 is fixed over the horizon and does not fluctuate with the demand. We also
notice that for the contracts with a long duration, the option capacity component is
significant.Finding the right level of flexibility in terms of shorter contracts and/or in the use of
option contracts, is a complex problem that needs to consider demand variability, product profits,
contract durations, and contract.
166
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172
CHAPTER FIVE
SUMMARY OF FINDINGS, CONCLUSION, RECOMMENDATIONS,
CONTRIBUTION TO KNOWLEDGE AND SUGGESTIONS FOR
FUTURE RESEARCH
5.1 SUMMARY OF FINDINGS
The specific objectives of the study were:
To determine the extent to which capacity planning enhanced the performance in the brewing
industry in South Eastern Nigeria.
To ascertain the nature of relationship between capacity requirement planning and material
requirements planning.
To ascertain the extent to which capacity planning sustains organisations’ competitive
advantage.
To determine the extent of relationship between capacity planning and capacity building.
To determine the steps toward developing a capacity plan and the profitability in the brewing
firms in the area studied.
It was found that:
Capacity planning to a large extent enhanced the performance in the brewing industry in
South Eastern Nigeria. There was significant positive relationship between capacity
requirements planning and materials requirements planning.Capacity planning to a large
extent sustained the organizational competitive advantage.There was positive relationship
between capacity planning and capacity building.The 12 steps starting from to determine
service level requirements and ending in to plan the future system configuration were in a
descending order of magnitude and of the same order of magnitude.
5.2 CONCLUSION
The finding that capacity planning enhanced the performance in the brewing industry in South
Eastern Nigeria implied that it made the brewing companies studied to achieve their
organizational goals and objectives. It also made them to fulfil the promises the companies made
to their numerous stakeholders. It positively affected the behaviour of the factory senior and
junior staff towards striving to achieve the organizational goals and objectives.
173
The finding that there was a significant positive relationship between capacity requirements
planning and materials requirements planning implied that there was a positive correlation
between them. This meant that materials requirements planning which was a method of
coordinating the detailed production plans could lead to an enhancement of capacity
requirements planning which meant taking future decisions on the items needed for the
production capability of the brewing facility. Both processes were multi stage ones which began
with a master capacity schedule and master materials schedule. Both of them worked backwards
to determine when and how the component would be needed.
The finding that capacity planning to a large extent sustained the organizations’ competitive
position implied that capacity decision making, forecasting and simulating the capacity objective
sustained the organizations’ standing when compared with the firms in the same line of brewing
business. So to get a distinctive competence, a brewing company needed capacity planning. So
capacity planning was needed by a brewing company to retain its customers and present and get
potential customers in the future.
The finding that there was a positive relationship between capacity planning and capacity
building implied that as one increased the other increased. So planning for the production
capability of a brewing facility could enhance the capacity environment especially in such areas
as human resource development and planning. So capacity planning could enhance capacity
building through internationalization. This is pertinent in the petroleum industry where even
though the Nigerian people do not have the technology by joint venture relationship between
N.N.P.C and the crude oil producing and servicing companies, the Nigerian economy is
dependent on crude oil and associated gas exports.
The finding that there were 12 steps towards developing a capacity plan that positively affected
profitability in the brewing industry in South Eastern Nigeria, starting from determining the
service levels and ending in planning the future system and they were in a descending order of
importance but of the same order of magnitude had some implications. It meant that the 12 steps
could be arranged in a hierarchy. It also meant that statistically, each step was as important as the
other for proper functioning.
174
1.3 RECOMMENDATIONS
It is recommended that the strategic and production managers of the brewing companies studied
should be backed bythese policies:
That the use ofcapacity planning as a technique to improve all performance factors.
That the gain from the correlation of capacity requirements plans and materials requirements
planning.
Sustain the organizational distinctive competence standing using capacity planning.
Exploit the advantages of the positive synergy between capacity planning and capacity
building.
Going through the 12 steps were of capacity planning for proper functioning and a balanced
score card.
1.4 CONTRIBUTION TO KNOWLEDGE
Davis and Mabert (2000) worked on the effect of capacity planning decisions in organizational
performance. They found that it was useful in enhancing production planning and improving
organizational performance. Bary et al worked on the effect of time-phased capacity
requirements planning on materials requirements planning in manufacturing organizations. They
found that time-phased capacity requirements planning utilized the counterpart materials
requirements planning system. So the capacity needs and the time to utilize them are determined
by the materials needs and when and how they are met. They also found that both capacity
requirements planning and materials requirements planning had lead times which was the time
lag from the time orders are placed and the time the order is required in the production process.
Chen et al (2004) worked on the relationship between capacity planning and brewing
organization’s competitive position. The aspects of competitive position they covered were in the
areas of strategic purchasing and supply management. Both sections were important aspects of
materials management aimed at ensuring that materials were available in the correct quantity,
quality and at the time needed to ensure a continuous production process. They found that the
ability to match capacity and material requirements planning gave the brewing company a
competitive edge.
Vollmann (2000) worked on the correlation between capacity building and capacity planning.
They emphasized the need for a conducive capacity environment by ensuring that the business
kept to all the legal rules and utilized all effective human resource development and planning. So
175
capacity building ensured a congenial work environment. They found that there was a positive
correlation between capacity planning and capacity building.
Eta (2012) worked on indigenous capacity building for internationalization of burgeoning
medium-scale enterprises (MSEs) in Nigeria for his M.Sc thesis. He identified the major areas of
strategy development by indigenous MSEs for building capacity and improving competitiveness
in a globalised market.
In all, there have been a lot of empirical studies on the effect of capacity planning on the
performance of brewing companies in Europe and America. Some Nigerian researchers like
Ohno and Nwachukwu in 1998 and 2004 repectively, have worked on the effect of capacity
building on internationalization. However, to the best knowledge of the researcher, no other
researcher has worked on how capacity planning enhances performance in the brewing industry
in South Eastern Nigeria a prime focus of this study in a developing African country. Also, the
contingency theory of capacity planning and the multi period theory of capacity planning were
empirically applied to the brewing industry in South Eastern Nigeria.
1.5 SUGGESTIONS FOR FUTURE RESEARCH
This Research work has concentrated onhow capacity planning enhances the performance of five
brewing companies located in the five South Eastern States (Abia, Anabmra, Eboyi, Enugu and
Imo States). It will be worthwhile if the study is extended to the other 31 States in the other five
geopolitical zones in Nigeria and the Federal capital to make for a better generalization of
capacity planning and performance in the brewing industry in Nigeria.
176
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Vollmann, T.E., Cordon, C., and Heikkila, J., (2000), Teaching supply chain management to
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190
APPENDIX I
SECTION I: Personal Data
(1) Sex: (a)Male [ ] (b)Female [ ]
(2) Marital Status: (a) Married [ ] (b)Single [ ] (c) Divorced [ ] (d) Widowed
[ ] (e) Separated [ ]
(3) Age: (a) Less than 20 years [ ](b) 21 – 30 years [ ] (c) 31 – 40 years [ ] (d) 41 – 50
years [ ] (e) 51-60 years [ ]
(4) Highest Educational Qualifications: (a)Senior school certificate [ ]
(b) R.S.A [ ] (c) Diploma [ ] (d) O.N.D [ ] (e) H.N.D [ ]
(f) Second Degree [ ] (g) Ph.D. [ ] (h) A.C.A [ ]
(5) Status: (a) Senior Staff [ ] (b) Junior Staff [ ]
(6) Duration worked (tenure): (a) 1-10 years [ ] (b) 11-20 years [ ]
(c) 21-30 years [ ] (d) 31-40 years [ ]
SECTION II: Data on the effect of capacity planning on performance.
From question 7, give answers not Strongly Agree (SA), Agree (A), Undecided (U),
Disagree (D) and Strongly Disagree (SD).
191
S/NO STATEMENT SA A U D SD
Objective 1: What is the extent to which capacity
planning enhances the performance in the brewing
sector?
(7) Capacity planning to a little extent enhances the
performance in the brewing sector in South Eastern
Nigeria.
(8) Capacity planning to a large extent enhances the
performance in the brewing sector in South Eastern
Nigeria.
(9) Adding capacity in anticipation of an increase in
demand increases the performance in brewing sector
in Southern Nigeria.
(10) Adding capacity only after the organisation is
running at full capacity due to increase in demand
increases the performance in the brewing sector in
Southern Nigeria.
192
S/NO STATEMENT SA A U D SD
Objective II: to ascertain the nature of the
relationship between capacity requirements
planning and material requirements planning?
(11) There is significant positive relationship between
capacity requirements planning and material
requirement planning.
(12) There is significant negative relationship between
capacity requirements planning and material
requirements planning.
(13) There is no relationship between capacity
requirements planning and material requirements
planning.
(14) Material requirements planning have a positive
correlation with capacity requirements planning.
193
S/NO STATEMENT SA A U D SD
Objective III: to ascertain the extent to which
capacity planning sustains organization’s
competitive advantage.
(15) Capacity Planning sustains organization’s
competitive advantage to a large extent.
(16) Capacity Planning sustains organization’s
competitive advantage to a low extent.
(17) The extent to which Capacity Planning sustains
organization’s competitive advantage is not
obvious.
(18) The extent to which Capacity Planning sustains
organization’s competitive advantage is obvious.
S/NO STATEMENT SA A U D SD
Objective IV: To determine the extent of
relationship between capacityplanning and capacity
building.
(19) There is a significant positive relationship between
capacity building and capacity planning.
(20) There is a positive correlation between capacity
building and capacity planning.
(21) There is a negative correlation between capacity
building and capacity planning.
S/NO STATEMENT SA A U D SD
Objective V: To determine the steps towards
developing a capacity plan to improve the
profitability in the brewing sector in the area to be
studied.
(22) The first step is to determine service level requirements.
(23) The second step is to define workloads.
194
(24) The third step is to determine the unit of work.
(25) The fourth step is to identify service levels of each
workload.
(26) The fifth step is to analyze current system capacity.
(27) The sixth step is to measure service levels.
(28) The seventh step is to measure overall resource
usage.
(29) The eighth step is to measure resource usage by
workload.
(30) The ninth step is to identify components of response
time.
(31) The tenth step is to plan for the future
(32) The eleventh step is to determine future processing
requirements.
(33) The twelfth step is to plan future system
configuration.
195
APPENDIX II
ORAL INTERVIEW SCHEDULE
(1) What is the extent to which capacity planning enhances the performance in the brewing
sector in South Eastern Nigeria?
……………………………………………………………………………………………
…………………………………………….………………………………………………
(2) What is the nature of the relationship between capacity requirements planning and
material requirements planning?
……………………………………………………………………………………………
………………………………………..…………………………………………………
(3) What is the extent to which capacity planning sustain organisations; competitive
advantage?
……………………………………………………………………………………………
……………………………………………………………………………………………
(4) What is the extent of the relationship between capacity planning and capacity building?
……………………………………………………………………………………………
…………………………………………………….……………………………………..
(5) What are the steps that could be used to develop a capacity plan to improve the
profitability in the brewing sector in the area to be studied?
……………………………………………………………………………………………
……………………………………………………………………………………………
196
APPENDIX III
DICHOTOMOUS ORAL INTERVIEW SCHEDULE ON THE CONTINGE NCY
THEORY OF CAPACITY PLANNING
s/n Questions Yes No
1 Is the contingency theory related to capacity planning
to enhance the performance in the brewing industry?
2 Is the contingency theory related to the nature of the
positive relationship between capacity requirements
planning and materials requirements planning?
3 Is contingency theory related to ascertaining the large
extent of the positive relationship between capacity
planning and capacity building?
4 Does contingency theory relate to the large extent to
which capacity planning sustains the organization’s
competitive advantage?
5 Is the contingency theory related to the steps of
capacity planning which to a large extent aims to
develop the capacity plan that positively affects the
profitability of the brewing industry?
197
APPENDIX IV
DICHOTOMOUS ORAL INTERVIEW SCHEDULE FOR IMPLEMENTIN G THE
CAPACITY MULTI-PERIOD PROBLEM
S/n Question Yes No
1 Is the multi period capacity problem relevant
for capacity planning to enhance the
performance in the brewing industry?
2 Is the multi period capacity problem relevant
to the nature of the positive relationship
between capacity requirements planning and
materials requirements planning?
3 Is the multi period capacity problem relevant
to ascertaining that to a large extent there is a
positive relationship between capacity
planning and capacity building?
4 Is the multi period capacity problem relevant
to a large extent to which capacity planning
sustains the organizations competitive
advantage?
5 Is the multi period capacity problem relevant
to the development of the steps of the capacity
plan that positively affect profitability in the
brewing industry?
198
APPENDIX V
Calculation of Cronbach’s Alpha Co-efficient of Reliability
∑=
−=
2
2
11 x
yi
K
K
σσ
α
where:
α = Cronbach’s Alpha Co-efficient of Reliability
K = Number of questions in the questionnaire
2xσ = The variance of the observed total test scores
2yiσ∑ = The sum of the variance of the component, i for the pilot sample of persons
,53=k 2yiσ∑ = 0.0085, 2
xσ =0.05
∑=
−=
2
2
11 x
yi
K
K
σσ
α
=
=− 05.0
0085.01
153
53
α = 1.0192(1 – 0.17)
= 83.00192.1 ×
845936.0=
0.9
85.0
≈=α
199
APPENDIX VI
Results related to the Personal Data of the Respondents
For the purpose of the concise and focus discussion, Table 4.4 will be relevant. 300 out of the
740 respondents making 40.501 per cent of them strongly agreed while 367 of them making
49.595 per cent of them agreed that capacity planning to a large extent enhances the performance
in the brewing industry in South Eastern Nigeria. The extent of agreement in this statement is
also shown in which the mean score is 4.205 which exceeds the cutoff point of 2.00, and this is
in agreement with the contention of Schuler and Youngblood, 2006:) that says that
manufacturing companies in general and brewing companies in particular take the issue of
capacity planning and performance as very important. This is because without capacity planning,
they will not be able to determine the production capacity of their facilities in terms of the inputs,
throughput or produces and output and performance in terms of the extent to which they achieve
or achieving their organizational objectives.
It was found that out of the 740 respondents, for the sex of the respondents, 518 of them are
males while 222 of them are females, giving a ratio of 2.3:1. In the United States of America
there are equal opportunity laws that give women equal opportunity with men in recruitment
matters (Schuler and Youngblood, 2006). Recruitment is generally seen as the process of
searching for and obtaining qualified job candidates in sufficient numbers such that the
organization can select the appropriate people to fill its job needs. In Nigeria, there are now some
nongovernmental organizations and Women Associations that clamours for equal opportunities
in work organizations between women and men.
Schuler and Youngblood (2006) have observed in a study of the utilization goals and time tables
of some organizations, the ratio of men to women were as low as 193:7 in a sample of 100
respondents and percentages of 96.5:3.5. A utilization analysis determines the numbers of
different categories of people like men and women or white, Hispania, black, Asian, Indian and
Minorities in the organization. Tcheknavorian-Asenbaur (2004) has observed that despite the fact
that women are employed in low-skilled poorly paid positions, there is now an advance of an
increasing number of highly educated women who enter into senior decision positions.
For the marital statuses of the 740 respondents, it was found that 562 of them were married while
178 of them were single giving a ratio of 3:1 Marriage is seen from the perspective of maturity
200
and this is why some positions are reserved for married candidates hoping that married men and
women are more responsible than single men and women. Moreover, marital status is an
important demographic variable as it is only when people are married that they can freely give
birth without any social stigma. No wonder demographic trends are part of the socio-cultural
aspect of the social environment (Wheelen and Hunger, 2008). The marital bulge is at the point
where people are married and not when they are single.
Eight current socio-cultural trends in the United States of America are:
1. Increasing environmental awareness.
2. Growing health consciousness especially in the area of gynaecology for married women.
3. Expanding seniors’ market.
4. Impact of the Generation of Boomlet.
5. Declining mass market.
6. Changing pace and location of life.
7. Changing household composition. Single person households, especially those of single
women with children, could soon become the most common household type in the United
States of America. Married couple households decreased from nearly 80% in the 1950s to
50.7% of all households in 2002. A typical family household is no longer the same as it
was once portrayed in the Brady Bunch in the 1970s or even the Cosby show in the
1980’s (Wheelen and Hunger, 2008).
For the ages of the 740 respondents, it was found that they were less than 20 years, 21 – 30
years, 31 – 40 years, 41 – 50 years, 51 – 60 years and above 60 years with frequencies of 15,
155, 192, 200, 170 and 8 out of 740 respectively. This shows that the baby boomers with an age
range of 41 – 59 in the United States of America corresponds with those with the ages with the
highest frequency in this work. Wheeler and Hunger (2008) have observed that the group of 7
million people in their 405 and 505 is the largest age grade in all developing countries.
Although the median age in the United States of America will rise from 35 in 2000 to 40 in 2050
it will increase from 40 to 47 during the same time period in Germany. It will increase up to 50
in Italy as soon as 2025 (Wheeler and Hunger, 2008).
201
It was found that the highest educational qualifications of the 740 respondents were senior school
certificate, Royal Society of Arts, Diploma, Ordinary National Diploma, Higher National
Diploma, First Degree, Second Degree, Ph.D and Associate of Chartered Accountants at the
ratio of 104:44:30:141:129:211:49:1:30. The modal highest educational qualification is First
Degree with a frequency of 211 out of 740. Education is a process of teaching, training and
learning especially in schools, colleges, universities and tertiary institutions to improve
knowledge and develop skills further (Hornby, 2001).
Brewing companies in South Eastern Nigeria have become learning organizations, by the
technical nature of brewing. In the brewing department all the brewing staff are graduates that
studied such science subjects as brewing technology, botany, zoology, agriculture, etc. A
learning organization is one in which there is creation, acquisition and transfer of knowledge
which take place to seek relatively permanent change in behaviour (Sheikh, 2006) Human
behaviour is responsive to learning experiences. All individual activities in the organization such
as engendering loyalties, developing the awareness for organizational goals, performing on the
job, getting safety rewards and brewing are learnt.
The finding that out of the 740 respondents, 252 of them were senior staff while 408 of them
were junior staff, shows that status of the respondents is pyramidal with the tapering at the senior
staff and the junior staff at the base, no wonder Chiehezie, Nzewi and Ozogbu (2008) have
observed that the tapering starts from top managers to middle managers, first line managers and
non-managerial staff. The managers whether they are top managers, middle managers and first-
line managers are senior staff. The non-managerial staff are junior staff.
Managers at this level are the senior executive members of the organization that are responsible
for the overall management of the organization. These people occupy the topmost position of the
pyramid. They are the individuals who are responsible for making organization-wide decisions
and establishing the plans and goals that affect the entire organization. The top managers are the
strategic managers who are responsible for policy formulation implemented by people below
(Chiekezie et al, 2008).
The middle level managers are the managers that occupy the middle position of the
organizational hierarchy. They are responsible for implementation or executing the plans,
202
policies and programmes as directed by the top managers. The front line managers are the
operational managers. They occupy the bottom position in the organizational hierarchy. They are
responsible for implementing operations in support of the organizational strategies (Chiekezie et
al, 2008).
The durations of the work done by the 740 respondents in years were 0-10 years, 11 – 20 years,
21-30 years and above 30 years. They had the frequencies of 30, 342, 360 and 8 out of 740
respectively. If it is assumed that the workers are recruited when they are 20 years old then 21 –
30 years will correspond to the ages of 41-50 years with all that had been earlier written about
the baby boomers being applicable. This is because this duration class has the modal frequency.
203
APPENDIX VII
Multi-Period Capacity Planning Problem
In the previous section, we have studied the single period capacity planning problem. We now
discuss how to extend the single period model to a multi-period setting. In practice, a contract
will have duration. In the existing literature that studies capacity contracts, there are two different
ways to model the duration of a contract. If the contracts require a long term commitment, after
the firm signs the contract to acquire capacity from its supplier, the firms reserve or buy the same
amount of capacity in each period until the end of the planning horizon. On the other hand, if the
contracts are short term, the firm can reserve different amounts of capacity for different periods.
For example, Huang, and Ahmed (2006), Barahona et al (2005), and Martinez-de-Albniz and
Simchi-Levi (2005) consider long term contracts while Yazlali and Erhun (2006) use one-period
short term contract.
In the context of the design of a new supply chain, the firm does not own the capacity itself but
reserves capacity from its suppliers. The contract does not need to be for either the short term
such as one period or the long term such as to the end of the planning horizon. The firm and its
suppliers can reach agreement on a duration that is beneficial to both parties. For instance, a
supplier might want to offer a contract with median duration and better price to encourage the
firm to commit. For the firm, signing a long term contract might be too risky; on the other hand,
short term contracts might be too expensive. In this sector, we will study how the firm should
plan its capacity when it has the flexibility to choose the durations of the contracts (Huang, and
Ahmed, 2006).
In the single period problem, we can specify each contract with three terms: per-period unit price
of the fixed-price capacity, per-period unit price to reserve the option capacity, and per-period
unit exercise price of the option capacity. In a multi-period setting, we will add another
specification, which is the contract duration. For example a supplier quotes a three-month
contract with fixed-price N7500, option reservation price N7500, and option exercise price
N15000 to the manufacturer. The manufacturer decides to reserve 100 units of fixed-price
capacity and 20 units of option capacity under this contract. It must pay the price of 100 units
fixed-price capacity (N7500 x 100 = N750,000) and 20 units option capacity (N7500 x 20 =
N15000) in each of the three consecutive months starting with the first month of the contract.
The manufacturer then has 100 units of fixed-price capacity and 20 units of option capacity for
each of the three consecutive months.
204
The prices of the contract can depend on the duration. To encourage a longer commitment, the
prices might decrease as the duration of the contract increases. In these situations, the multi-
period capacity planning problem involves another type of tradeoff between the flexibility (or
duration) of the contract and its price. Contracts with shorter duration have more flexibility while
contracts with longer duration offer lower prices.
Let T be the length of the planning horizon. Resource k offers contracts with duration in the set
{ }LL ,,,1, , ikkk TTT = . To simplify the notation, we assume that for any resource all contracts
have different durations. This assumption can be relaxed and all the results still follow. Without
loss of generality, we assume that jkik TT ,, < if ji > . Therefore, we specify the set of contracts
that resource k offers as ( ) ( ) ( ){ }kikikkikkikk TTTeTqTp ∈,,,, ,,
Given the contracts that each resource offers, we assume that the firm will choose for each
resource a sequence of contracts { }LL ,,,1, , ikkk TTT = that satisfies the following conditions:
1. Contract ikT , has duration ikl , and it covers from period ∑−
=+1
1 , 1i
j jkl to period ∑ =
i
j jkl1 ,
2. Tt iki =∑ , for all k .
The first condition says a contract starts after the previous contract finishes. Condition 2
specifies that the manufacturer does not reserve capacity beyond the planning horizon. We call a
sequence feasible if it satisfies these two conditions. One implicit assumption here is that for
each period, we have only one contract active for each resource. In addition to deciding the
sequence of the contracts for each resource, the manufacturer needs to decide the corresponding
sizes: { }LL ,,,1, , ikk cc and { }LL ,,,1, , ikk gg we note that we permit zero capacity contracts at
zero cost, which allows the firm to not use a resource for any subset of periods. Since the first
two periods are cover by the same contract, the fixed-price and total capacity reserved for each
of these two periods are the same, which are 2c and 2g similarly a contract with duration 1
period is used to covered period 3 and a contract with duration 3 periods is used to cover the rest
of the horizon.
To simplify the notation and the representation of the multi-period capacity planning problem,
we will write a feasible sequence of contracts for resource k as follows:
205
{ },, ,,,1, LL ikkk TTT = where ikT , has duration ,,ikt and Tt iki =∑ ,
{ }Tkk gg ,,1, ,L and jkik gg ,, = if ∃a such that [ ]∑ ∑−
= =+Ξ 1
1 1 ,, ,1,,a
t
a
l lkik ttji
{ }Tkk cc ,,1, ,L and jkik cc ,, = if ∃a such that [ ]∑ ∑−
= =+Ξ 1
1 1 ,, ,1,,a
t
a
l lkik ttji
We use superscript to indicate time period. Given that the firm has decided its capacity planning
strategy, the sequence and sizes of the contracts for each resource, and given a multi-period
demand realization vector d, we can write the multi-period production planning problem as:
( ) ( ) iT
i
iiT
i
HyezrzyxdgcTzyx ∑∑
==
′−′=11
,,,,,,ˆ,,
maxπ (1)
idzts ii ∀≤ ,..
( ) idyxBAz iiii ∀+≤ ,,
icHx ii ∀≤ ,
( ) igyxH iii ∀≤+ ,
izyx iii ∀≥ ,0,,
Similar to the single period case, in a multi-period setting, the firm’s ultimate purpose is to
choose the strategy to maximize its expected profit with expectation taken over the distribution
of the multi-period demand random vector:
( ) ( )[ ] ( ) ( ) ( )iiT
i
iT
i
i cgqpzyxDgcTEDgcTgcT
−′−′
−= ∑∑== 11
*** ,,,,,,ˆ,,,ˆ,,
maxππ
igcts ii ∀≤ ,.. (2)
kT are feasible for all k.
We assume that unfilled demands are lost and unused capacity cannot be saved for future usage.
We also assume that the manufacturer will not use any unused capacity to build and store
inventory. Even though we do not allow inventory, the multi-period capacity planning problem is
not separable since the firm can use a contract to cover multiple periods.
We assume that the manufacturer needs to decide the sequence and sizes of the contracts for each
resource at the beginning of the planning horizon. To this extent, we also assume that it has a
demand forecast for each period at the beginning of the first period. In practice, capacity
206
decisions usually need to be made with a much longer lead time than the planning horizon. In
these situations, our two-stage decision process matches with the reality. Moreover, as we have
discussed in the earlier, since the manufacturer does not own the capacity, it is important for it to
secure the price and supply of the capacity by signing contracts at an early stage. However, this
is a restrictive assumption and it would be interesting to study the capacity planning problem in a
dynamic setting.