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CASE STUDY
Evaluation of Productivity, Quality and Flexibility of an AdvancedManufacturing System
S. K. Singh • M. K. Singh
Received: 12 September 2011 / Accepted: 15 September 2011 / Published online: 4 February 2012
� The Institution of Engineers (India) 2012
Abstract Productivity alone does not depict the overall
performance of a manufacturing system. Frequent changes
in the design and need for continuously improving product
quality require high degree of automation and flexibility of
the manufacturing system. Thus, productivity, quality and
flexibility have become critical measures of total manu-
facturing performance of a production system for justifying
the investment. These three measures are quantitatively
defined and their combined effect has been evaluated on
the manufacturing performance index of the system. A
mathematical model for performance index has been
developed. Further the relationship among productivity,
quality and flexibility has been investigated and a method
has been proposed to serve as decision support to
management.
Keywords Advanced manufacturing system �Integrated manufacturing performance index �Performance measure � Quality index � Productivity index �Flexibility index
Introduction
After economic globalization manufacturing organizations
are experiencing a common phenomenon, i.e., frequent
changes, uncertainty and unpredictability in the business
environments. In order to survive the organizations are
employing various techniques such as cellular manufac-
turing, JIT, flexible manufacturing or computer integrated
manufacturing for high production performance.
The complete evaluation of economic viability of a
system can not be done on the basis of only one mea-
surement variable such as productivity, profit or rate of
return, because it does not help in identifying specific areas
that need management’s attention [1]. Researchers identi-
fied three generic productivity measurement techniques.
These techniques are: the multi-factor productivity mea-
surement model, the multi-criteria performance measure-
ment technique, and a structured participative approach to
developing productivity and performance measurement [2].
Level of operational and labour productivity is greatly
influenced by the level of quality of work life and can be
used as a measure of performance of any manufacturing
organization [3, 4].
Globalization forces the companies to consider strate-
gies of quality improvement initiatives. Internal process
quality and product quality performance have its impact on
operational and business performance [5]. Improved pro-
cess quality reduces the cost of scrap and rework [6, 7].
Attempts to improve quality will lead to a better under-
standing of the firm’s processes, which can translate into
lower production costs [8]. The studies on the relationship
between quality performance and firm performance suggest
that: (i) quality performance has a significant and strong
effect on operational performance; and (ii) quality perfor-
mance has a week and not always significant effect on
business performance. [9–12] Performance of a company
can be measured on the basis of productivity, quality, and
profitability [13]. Voluminous work is available on quality
control, but only a few are on quality measures, particularly
in economic terms. Both quality and productivity should be
S. K. Singh (&), Non-memberBMAS Engineering College, Agra, India
e-mail: [email protected]
M. K. Singh, Non-memberNikhil Institute of Engineering & Management, Mathura, India
e-mail: [email protected]
123
Journal of The Institution of Engineers (India): Series C (January–March 2012) 93(1):93–101
DOI 10.1007/s40032-011-0002-0
expressed in monetary terms to establish a relation between
them.
Drastic changes in market demands and rapid techno-
logical development have created a need for more flexible
production system. This leads towards the use of mecha-
nized and automated equipment. At the same time there is a
need for flexibility towards the changes in the products.
These changes have to be made within a limited time and
without huge reinvestments. Thus, flexibility has become a
key factor in today’s dynamic and competitive manufac-
turing environment. However, there may be a conflict of
aims between flexibility and productivity. Strategically; the
production system should be so flexible that neither the
product nor the renewal of the process should be hindered
by ‘‘sunk’’ cost in production.
Flexibility has been defined as the ability of the firm to
anticipate, adapt or react to the changes in its environment.
Over 70 terms (types and measures) can be found in the
literature [14]. Flexibility has been classified as machine,
material handling, operation, process, product, routing,
volume expansion, production and marketing flexibilities
[15]. Process of establishing flexible production system
provide sense of greater humanization, better job condition,
motivation, job satisfaction, making decision on their own
work and generally contributing to a higher level of quality
of life [16]. Technological flexibility requires huge initial
investments and therefore a decision to acquire it is both
risky and strategic in nature. Discussion regarding flexi-
bility verses productivity must be done before any pro-
duction system is designed [17].
The decision maker has to decide an optimum level for
productivity, quality and flexibility of the production sys-
tem. It is also important to evaluate, how these three
parameters correlate with each other because these
parameters may have conflicting effects on the perfor-
mance index of the production system.
Proper evaluation of any production system relative to
these parameters requires precise measurement of produc-
tivity, quality and flexibility. Performance measurement of
a system is one of the most important engineering and
management tool for:
• Justifying the investment in integrated manufacturing
and production system.
• Continuous improvement of existing system.
• Providing an insight into where the change is needed.
• Predicting and estimating future productivity.
Literature Review
Performance prediction of advanced manufacturing system
is very essential before incorporation in the industry. Its
performance can be evaluated by three conflicting perfor-
mance parameters, i.e., productivity, quality and flexibility.
To consider these parameters in combined; their quantita-
tive measurement should be done on common scale. In this
direction some of researcher has contributed which is
presented here.
The review of literature on manufacturing system, pro-
ductivity measurement and improvement has been sum-
marized under four categories; they are operation research
(OR) based methods, system analysis based methods,
continuous improvement based methods and performance
metrics based methods. Five types of performance objec-
tives based on cost, flexibility, speed, dependability and
quality has been mentioned. It has revealed that perfor-
mance measure criteria must be driven by strategic
objectives and the measures must provide timely feedback.
Manufacturing systems often operate at less than full
capacity while producing quality product and frequent
change in design specification is required; so, the produc-
tivity is low and the costs of producing products are high
[18]. In order to reduce the cost of manufacturing, com-
panies are very much concern in effective utilisation of the
resources. Some researchers report that TPM is one such
tool to enhance equipment effectiveness and maximise
equipment output. The findings indicate that TPM not only
leads to increase in efficiency and effectiveness of manu-
facturing system, but also prepares the whole plant to meet
the challenges put forwarded by globally competing
economies [19]. The TPM model provides a quantitative
metric for measuring the productivity of individual pro-
duction equipment.
It is discovered that appropriate measurement is needed
for identifying the problems in order to improve and
increase productivity. To achieve this, it is necessary to
establish appropriate metrics for measurement purposes
[20]. Performance measurement system is underdeveloped
and under researched in small and medium enterprises [21].
Traditional performance measures are focusing into the
sub-optimisation instead of seeking into overall perfor-
mance [22]. The question of what particular performance
measures to use is a complex task, and is not being made
any easier as the number and type of performance measures
available continues to grow [23].
Overall equipment effectiveness (OEE) directly mea-
sures product quality, loss and the abilities to deliver to a
schedule [24]. Before the advent of OEE, only availability
was considered in equipment utilisation, which resulted in
the overestimation of equipment utilisation. OEE method-
ology is a proven approach to increase the overall perfor-
mance of equipment [25]. The ultimate objective of any
factory is to have a highly efficient integrated system and
not brilliant individual equipment [26]. Traditional OEE
used by the companies is inadequate, so, the managers
94 Journal of The Institution of Engineers (India): Series C (January–March 2012) 93(1):93–101
123
should focus on measuring the total effectiveness of the
manufacturing system [27]. The metrics for measuring and
analysing the productivity of manufacturing operations
from the equipment level to the system level are of
increasing importance to companies seeking to continu-
ously optimise existing operations [28].
A new approach is proposed to assess the quality rate of
manufacturing system using principal component analysis.
A detailed methodology for determining the overall line
availability, overall line performance and overall line
quality is developed using overall line effectiveness as an
index of performance evaluation [29].
Cost reduction without compromising on quality has
become the motto of every manufacturing organisation, to
survive in the global market. Due to the complex nature of
most manufacturing systems, at present it is still very dif-
ficult to analyse the overall performance of a complex
manufacturing system. The goal programming, multiple
regression equations and GP technique have been used for
modeling the relationship between product quality and
control variables [30]. The influence of quality improve-
ment aspects on a firm’s productivity performance has been
examined and it was found that quality awareness, sug-
gestions towards improvement, voluntary work and train-
ing influence productivity [31]. The study on the
relationship between quality and productivity reveals that
quality improvement measures play a fundamental role in
increasing productivity [32]. The importance of supply
quality management has been stressed in the improvement
of an organization’s quality performance [33]. A total cost
equation and evaluation of the optimal ordering quantities
for a production system with quality assurance and rework
have been developed [34]. Some of the researcher advocate
to optimize the assembly tolerances for quality, and mini-
mizes the manufacturing cost [35]. Author has conducted a
review on quality management research along five main
themes, i.e., definition of quality management, definition of
product quality, the impact of quality management on firms
performance, quality management in the context of man-
agement theory and the implementation of quality man-
agement [36]. An attempt has been made to measure,
examine and determine whether there exist possible inter
relationships between the terms quality of work-life and
productivity [37].
The issue of flexibility is assuming increasing impor-
tance in manufacturing decision making. Flexibility is
important to accommodate changes in operating environ-
ment. The manufacturing systems that are flexible can
utilize the flexibility as an adaptive response to unpre-
dictable situation. A lot of work has been done to define
properly the manufacturing flexibility and provided various
methods for measuring them [38]. A critical review of
selected measures from the literature is provided and a
flexibility measure is proposed and its application is
demonstrated through a hypothetical example. Perfor-
mance of the measures and proposed measures are com-
pared [39].
With change in market trend and technological
development it is essential to have flexible production
system without any compromise with the productivity.
This necessitates the discussion regarding productivity
verses flexibility before design of production system.
Various methods for calculation of different flexibility
level, strategy for a more flexible view upon product and
process in the context of Swedish industry are discussed
[17]. A method to evaluate flexibility of product design
has been developed and a set of guidelines to guide
product architecture to a desired state of flexibility is
derived [40]. Various evaluation variables of flexibility
and factors improving the flexibility of single stage
production system with different characteristics have
been proposed. It also pointed out the characteristics of
the amplification of multistage production system as one
of the important factors affecting the flexibility and
productivity of the system [41]. An investigation has
been made to evaluate the impact of shop and workers
flexibility on different assembly cell faced with product
mix variability over a wide range of experimental con-
ditions. In this case three types of cellular shops were
considered, i.e., strict cell shops, flexible cell shops and
hybrid cell shops. Result indicates that there is no cell
shop that outperforms other under all experimental
environments. Finally the result suggests that in most
cases the implementation of only worker flexibility
resulted in the majority of improvement with respect to
the average percentage of jobs tardy [42].
In this research work an attempt has been made to give
the improved definition and quantification of productivity,
quality and flexibility in monetary terms. A relationship
among productivity, quality and flexibility has been
investigated and finally a method has been proposed to
decide whether decision maker should opt automated
production system or not for high performance of
industry.
Objective
1. The objective of this work is to identify the various
measures of performances of a production system.
2. To define the productivity, quality and flexibility and
to quantify these terms on the monetary scale.
3. Establish a relationship between productivity, quality
and flexibility and their combined effect on the overall
performance of an automated production system.
4. A case study in ball bearing plant for the validation of
the proposed model.
Journal of The Institution of Engineers (India): Series C (January–March 2012) 93(1):93–101 95
123
Measurement of Variables
Here we have considered total productivity consisting of
labour, capital, raw material and miscellaneous produc-
tivity. Labour productivity includes direct and indirect
labour, material productivity includes direct and indirect
material and miscellaneous productivity is consisting of
energy efficiency, floor space reduction, increased use of
computer software, etc. In this study future productivity is
also being considered apart from past and present pro-
ductivity for the incorporation of capital intensive auto-
mated production system.
The productivity is concerned with the efficiency of
converting inputs to outputs. These outputs are expressed
in common terms, i.e., monetary terms so that they can be
added together. In this case total productivity is considered
under three different partial productivity heads, i.e., labour,
material and overhead productivity.
Productivity Measurement
Labour productivity (PL) measures labour performance
required to produce total output and is defined for a given
period of time as:
PL ¼OP
CL
Labour cost, CL ¼XL1
d¼1
cdNnd þXL2
i¼1
cini
ð1Þ
where L1 is the number of different jobs using direct
labour; L2 is the number of different jobs using indirect
labour; cd is the Wage of job d per unit time; N is the time
period; ci is the salary of job i during a time period; nd is
the number of direct labour units for job d; ni is the number
of indirect labour units for job i.
Material productivity (Pm) measures the efficiency of
raw material use and is defined for a given period as
Pm ¼OP
Cm
Material cost; Cm ¼ Co þXJ
j¼1
Cd jð Þnd jð Þ þ Cid
ð2Þ
where Co is the material ordering cost; J number of dif-
ferent parts; Cd (j) is the unit cost of direct material for part
j; nd (j) is the amount of direct material used for part j; Cid
is the indirect material cost except tools.
This measure is useful when material cost is a significant
part of the total cost.
Overhead productivity (POH) is the efficiency of all
resources except labour and material. This group of inputs
includes machines, tools, floor space cost. Machine cost
includes expenses such as energy, maintenance, repair,
insurance and property tax. As the level of automation
increases, overhead cost (COH) increases. For a given
period it is defined as
POH ¼OP
COH
COH ¼ Ct þ Cs
Tool cost, Ct ¼XM
m¼1
cd mð Þ nw mð Þþf nbðmÞg
ð3Þ
where cd (m) is the unit cost of tool type m; nw (m) is the
number of worn tools of type m; nb (m) is the number of
broken tools of type m; M is the number of different tools.
Floor space cost; Cs ¼ Cu þ Cm þ Cr þ Cið Þ Sm=St
� �
Cu is the total plant utility cost; Cm is the total plant
maintenance cost; Cr is the total plant repair cost; Ci is the
plant insurance cost; Sm is the manufacturing floor space; St
is the total plant space.
Productivity index (PI) is formed by integrating the
partial productivity measures and is used to measure the
total manufacturing efficiency. PI for a given period is
defined as:
PI ¼ OP
CL þ CM þ COHð4Þ
The Eq. 4 is similar to the productivity measure by
‘‘Craig and Harris’’ [43].
Obvious trade-offs exist among partial productivity
measures. Consider, for example, a company makes an
investment in automated machine it significantly reduces
the man-hours necessary for processing. Suppose this
increased cost of using automated machine equals the
saving in the labour cost. Although the firm’s total pro-
ductivity does not change, labour productivity would nat-
urally increase. This type of fallacy is inherent in all partial
productivity measures. Therefore, productivity index must
used for company wide management decisions.
Measurement of Quality
Quality is a measure of manufacturing performance which
indicates the degree of perfection in making products. In
this case two types of partial quality measures are con-
sidered, that is, process and product.
Process quality (QS) is the ability of processes to make
good product with a small prevention cost. The prevention
cost (CP) is the in-process inspection cost for checking and
maintaining quality before final inspection. We define it for
a given period as:
96 Journal of The Institution of Engineers (India): Series C (January–March 2012) 93(1):93–101
123
QS ¼OP
CP
Prevention cost, CP ¼XJ
j¼1
XK1
k¼1
cp j; kð ÞNð5Þ
where K1 is the number of machines except material
handling system; N is the time period; cp (j,k) is the pre-
vention cost of part j for machine k per unit time.
A reduction in prevention cost increases process quality.
Product Quality (QP) is the degree of excellence of
finished products. Product quality increases as product
defects decreases. The defects can be expressed in terms of
failure cost (CF). This failure cost may indicate loss due to
failure of finished products to meet quality standards.
Product quality for a given period is defined as:
QP ¼OP
CF
Failure cost, CF ¼XJ
j¼1
E cf ðjÞ� �
Qj
ð6Þ
where cf (j) is the failure cost of part j; Qj is the lot size of
part j
E cf ðjÞ� �
¼ cg þ cb þ cs þ ca
cg is the cost of reworking a good part because of mis-
classification; cb is the cost of reworking a bad(defective)
part; cs is the cost of scraping a defective part that could not
be restored; ca is the cost of accepting a defective part.
Quality Index integrates the partial quality measures and
may be used to measure the total quality performance. For
a given period it is defined as:
QI ¼ OP
CP þ CFð7Þ
A tradeoff exists between the two partial quality
measures. Assume, for example that tight process control
to reduce product defects raises prevention cost, but lowers
failure cost. If total output is constant, the use of partial
measures indicates a conflicting signal.
On the other hand, if the increase in the prevention cost
equals the decrease in failure cost the quality index remain
unchanged.
Measurement of Flexibility
Flexibility is a measure of manufacturing performance
which indicates a manufacturing system’s adaptability to
changes in manufacturing environments. Three different
types of partial flexibility measures are considered, i.e.,
equipment, product and demand.
Equipment flexibility (FE) is the capacity of equipment
to accommodate new products and some variants of
existing products. Equipment flexibility is measured in
terms of idle cost(CI), and is defined as:
FE ¼OP
CI
Idle cost, CI ¼ vXK2
k¼1
ð1� ukÞNð8Þ
where uk is the utilization of machine k; v is the profit per
unit time; k is the number of machines; N is the time
period.
Equipment flexibility measures the opportunity of
equipment to add value to raw materials. Equipment idle
time should be reduced whenever possible to increase
equipment flexibility.
Product flexibility (FP) is the adaptability of a manufac-
turing system to changes in product mix. Product flexibility
is measured in terms of setup cost (CS) and is defined as:
FP ¼OP
CS
Setup cost, CS ¼XK2
k¼1
CsuðkÞTsuðkÞð9Þ
where Csu (k) is the setup cost for machine k per unit time;
Tsu (k)is the setup time for machine k.
A frequently changing market demands new products
and variations of existing products. These changes in
product mix result in a shorter life cycle of product and a
smaller lot size as the variety increases. Smaller lot size
means higher set up cost. Reducing setup cost in spite of
small lot sizes is often viewed as the key to increased
product flexibility. Rapid exchange of tools and dies and
flexible fixtures are good examples of setup reduction
methods.
Demand flexibility (FD) is the adaptability to changes in
demand rate. The demand may be of two types: customer
demand for finished products and manufacturing system
demand for raw materials. Demand flexibility may be
measured in terms of inventory costs (CINV) of finished
products and raw materials. Demand flexibility for a given
period is defined as:
FD ¼OP
CINVð10Þ
Flexibility Index is formed by integrating the partial
flexibility measures and can be used as a global measure of
the opportunity of a manufacturing system to add value to
products. Flexibility index for a given period is defined as:
FI ¼ OP
CI þ CS þ CINVð11Þ
Tradeoffs may exist between different flexibility
measures. For example, assume that a conventional milling
Journal of The Institution of Engineers (India): Series C (January–March 2012) 93(1):93–101 97
123
machine is replaced by a CNC milling machine with a larger
capacity. Setup cost decreases but idle cost increases. For a
constant output, product flexibility increases, but equipment
flexibility decreases. If the decrease in setup cost equals the
idle cost the flexibility index remained unchanged.
Therefore, the partial flexibility concept may not measure
the total manufacturing adaptability to various changes.
Integrated Manufacturing Performance Index
Now productivity, quality and flexibility indexes are inte-
grated to form a global index that reflects these three
attributes. Productivity, quality and flexibility have some
correlation to each other, whether positive or negative.
Mass production with poor quality increases productivity
because of mere increase in total output. Quality and
flexibility, however, usually decreases because of relatively
large defects and product inventory. The use of only one
index over the others is not desirable in evaluating the
manufacturing performance as a whole. Thus, the inte-
grated manufacturing performance index (IMPI) for a
given period is defined as:
IMPI ¼ Total output Rs:ð ÞIP þ IQ þ IF
ð12Þ
where IP = CL ? CM ? COH, IQ = CP ? CF and
IF = CI ? CS ? CINV.
Model Formulation
Considering IMPI as dependent variable and PI, QI and FI as
independent variable, the multiple regressions analysis has
been conducted. The data used are collected from the
industry for a time horizon of 24 months. The required data
are total output, labour cost, material cost, overhead cost,
inspection cost, failure cost, idle cost, set-up cost and
inventory cost. From the above data Productivity, Quality
and Flexibility index for both manual and automatic pro-
duction line have been calculated (Tables 1, 2). The analysis
provided the following mathematical relationship between
PI, QI and FI for manual and automatic production line.
For manual line
IMPI ¼ �0:00215þ 0:04915 ðPIÞ � 2:55� 10�5 ðQIÞþ 0:6094 ðFIÞ ð13Þ
For automatic line
IMPI ¼ �0:004038 þ 0:070369 ðPIÞ � 1:2145
� 10�5 ðQIÞ þ 0:5457 ðFIÞ ð14Þ
Result Analysis
From Eq. 13, 14 it is clear that the productivity index has
positive regression coefficient. Although overhead pro-
ductivity of automatic line is comparatively less than that
of manual line but overall productivity is more significant.
The quality index has negative regression coefficient in
both the cases. But in case of automatic line, it is numer-
ically less. This shows that failure cost and prevention cost
is less in automatic system than manual system. The result
shows that flexibility index has positive impact on the
integrated manufacturing performance index. In the present
case product and demand flexibility of automated line is
higher than that of manual one but equipment flexibility is
less. This is because the automated line is underutilized and
its higher depreciation cost leads to higher idle cost.
Therefore, the coefficient of flexibility index of automated
line is lower than the manual line. The result may be
plotted on the graph between IMPI and any two parameters
keeping third parameter constant as shown in Figs. 1 and 2.
Table 1 IMPI for manual production system
S. no OP PI QI FI IMPI
1 824643 2.155124 112.61 0.613106 0.475301
2 848845 2.149274 120.6946 0.538785 0.42926
3 1088559 2.516993 153.7079 0.768053 0.586235
4 815538 2.141993 110.5664 0.506821 0.408333
5 863120 2.181833 117.2399 0.513069 0.413922
6 888175 2.156916 126.7554 0.541781 0.431541
7 883040 2.163932 125.2184 0.514546 0.414325
8 969653 2.351797 135.0868 0.638568 0.500347
9 901992 2.27344 127.1307 0.572075 0.455425
10 1095758 2.447866 152.8894 0.775243 0.586518
11 899691 2.317459 125.8837 0.632083 0.494677
12 1071019 2.411591 147.9104 0.827253 0.613405
13 1059121 2.427729 136.4847 0.646979 0.508937
14 870588 2.126238 109.5906 0.483904 0.392779
15 939848 2.301721 119.0284 0.629109 0.492027
16 838928 2.119372 107.2524 0.583488 0.455582
17 747193 2.073215 93.59802 0.54214 0.427795
18 829149 2.138503 104.0338 0.584112 0.456782
19 978660 2.404925 125.6303 0.642831 0.505206
20 966512 2.280623 122.7473 0.550187 0.441659
21 900671 2.393549 114.2838 0.549339 0.445056
22 792823 2.067513 100.0786 0.505035 0.404248
23 946272 2.382368 120.5442 0.63872 0.501586
24 998467 2.416893 128.7514 0.702732 0.542141
98 Journal of The Institution of Engineers (India): Series C (January–March 2012) 93(1):93–101
123
Performance Prediction of Production System
In an effort to improve the performance of a production
system, computer integrated manufacturing and production
system can play a significant role. But being a capital
intensive project, the decision makers scare to incorporate
such system until its economic viability has been estab-
lished. Manufacturer does not prefer the huge investment
for long-term under uncertain environment and without
foolproof economic justification.
The study has been carried out in a bearing manufac-
turing company having both the types of production sys-
tems: manual and automated and required data has been
collected. For evaluation of overall performance index of a
system three parameters are considered, i.e., productivity,
quality and flexibility and all are measured on same eco-
nomic scale. On the basis of economic analysis, viability of
capital intensive project has been evaluated.
Finally it is observed that the values of some partial
measures such as overhead productivity and equipment
flexibility decreases because of a large initial investment
for an automated production line. The value of remaining
partial measures increase significantly, particularly labour
productivity (223.89%), process quality (201.22%), prod-
uct quality (53.75%), and product flexibility (58.71%).
These results demonstrate that an automated line remark-
ably reduces product defects, setup cost, WIP, and labour.
Since most of the partial measures increase, each total
measure also increases: productivity by 7.56%, quality by
156.73% and flexibility by 34.38%. With significant
improvement in both quality and flexibility, despite a small
increase in productivity, the IMPI increases by 27.62%.
The results shown in the Table 3 are of single obser-
vation and on the basis of 24 such observations we obtain
at the 95% confidence level the IMPI as follows:
44:85\IMPI\50:01 For manual line
57:77\IMPI\64:17 For automated line
Since the confidence interval of IMPI for automated line
is totally to the right of the upper bound of that of IMPI
for manual line and hence, there is strong evidence that
the automated line provides better total manufacturing
Fig. 2 Relation between IMPI, PI and FI when QI remains fixed for
automatic line
Fig. 1 Relation between IMPI, PI and FI when QI remains fixed for
manual line
Table 2 IMPI for automatic production system
S. no OP PI QI FI IMPI
1 2483971 2.3181946 289.1028 0.82391 0.606592
2 2556871 2.309636 318.6529 0.727362 0.5522
3 3278932 2.6994182 403.8094 1.034144 0.746319
4 2456547 2.3031265 282.4916 0.684526 0.526705
5 2599871 2.344279 299.939 0.693837 0.534427
6 2675341 2.3116013 335.5501 0.734443 0.556435
7 2659875 2.3215264 329.9274 0.696882 0.535118
8 2920768 2.5200042 351.5609 0.864082 0.642275
9 2716960 2.437048 333.574 0.775485 0.587252
10 3300618 2.617359 398.3367 1.051573 0.748765
11 2710028 2.4907636 328.6077 0.858873 0.637413
12 3226100 2.5826238 382.602 1.12433 0.781717
13 3190260 2.5968954 361.5435 0.879935 0.656044
14 2622366 2.2806594 285.4742 0.656583 0.508904
15 2830988 2.466935 311.4056 0.853572 0.632863
16 2527000 2.2749738 282.441 0.787581 0.583833
17 2250677 2.2394774 243.0012 0.728963 0.548709
18 2497544 2.3014151 270.4433 0.786205 0.584746
19 2947898 2.5811637 331.8584 0.870617 0.649753
20 2911305 2.4457513 321.7623 0.750825 0.573444
21 2712980 2.5762922 299.3798 0.748033 0.578592
22 2388125 2.2222289 261.2257 0.682342 0.521005
23 2850340 2.5582401 316.6341 0.865134 0.645185
24 3007560 2.5928381 341.264 0.951479 0.694636
Journal of The Institution of Engineers (India): Series C (January–March 2012) 93(1):93–101 99
123
performance. This justifies the capital investment in
advanced manufacturing system to improve overall
performance.
Conclusion
The purpose of this research is to quantify and combine
three critical performance measures into one global index.
The conventional productivity measures are improved and
its index has been developed for use in IMPI. Quality and
flexibility measures are defined, quantified and an index
has been developed. These three indexes are combined
together to form IMPI which evaluate a manufacturing
system as a whole. IMPI is the primary measure of man-
ufacturing performance. PI, QI, FI and partial measures are
surrogates to the IMPI. The proposed model can be applied
in any engineering industry manufacturing variety of
products and needs implementation of automated system
for higher performance.
The study indicates that the adoption of an automated
manufacturing system improves the overall manufacturing
performance, compared with a conventional manual man-
ufacturing system. However, just because an automated
system performs better does not mean we abandon the
conventional manual system. The adoption of automated
system requires a large initial investment under a long-term
environment. Economics of the project should be consid-
ered over the project’s life cycle. The performance indexes
developed here can be useful in strategic planning since
they not only evaluate past or current manufacturing per-
formance, but can also predict the effect of capital
investment on future performance.
Scope for Future Work
The present study is concerned to quantify and combine
three critical performance measures i.e. productivity,
quality and flexibility to obtain IMPI. The evaluation of
IMPI for conventional and automated production system is
done with certain assumptions. However, this work can be
further extended as:
1. In the present study we have considered only the
labour productivity, material productivity and over-
head productivity. For calculation of total productivity,
the capital productivity should also be considered.
Because it measures the efficiency of capital invested
on equipment and building. This measure is useful in
capital intensive project. Similarly one can form
energy productivity when energy cost is a large part
of the total cost.
2. For calculation of process quality index in this case we
have considered labour and equipment cost in approx-
imate involved in inspection process on average basis.
More precise result can be obtained by using control
chart. In this case cost of sampling, cost of improving
assignable cause, cost of improving process capability
in each cycle etc. are also required to be calculated.
3. For demand flexibility we have considered only
inventory cost of finished product and WIP of product.
For better result, cost of service and risk per unit raw
material of part, back order cost of unit raw material,
back order cost of unit finished product should also be
considered.
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