The implications of fit between planning environments and ... · volume/variety, competitive priorities, and process technology and infrastructure availability within a ® rm. Berry

The implications of regt betweenplanning environments andmanufacturing planning and

control methodsPatrik Jonsson

Chalmers University of Technology Department of Logistics andTransportation Gothenburg Sweden and

Stig-Arne MattssonLund University Department of Industrial Management and Logistics

Lund Sweden

Keywords Manufacturing resource planning Control User satisfaction Production methods

Abstract The applicability of manufacturing planning and control methods differs betweenenvironments This paper explains the regt between the planning environment and material andcapacity planning on the detailed material planning and shop-macroor planning levels The study isbased on a conceptual discussion and a survey of 84 Swedish manufacturing companies Resultsshow the use of planning methods and their levels of user satisfaction in complex customer orderproduction conreggure to order production batch production of standardized products andrepetitive mass production respectively

1 IntroductionThe suitability of various manufacturing planning and control methodsdepends on the demand products and manufacturing characteristics Amethod that works perfectly well in one situation can be a completely wrongapproach in another For example the objectives of both re-order point andmaterial requirements planning (MRP) methods are to plan when and howmuch to order of individual items The re-order point method requires evendemand and is possible to use without computerized support while MRP needsa computerized system and is more applicable to products with complexproduct structures dependent demand long lead-times and erratic demandthan the re-order point method Similar differences in applicability can beidentireged for other material planning capacity planning (eg capacity bills vscapacity requirements planning) and shop macroor control methods (eginputoutput control vs regniteinregnite capacity scheduling) (eg Fogerty et al1991 Vollmann et al 1997)

Only a very limited amount of research linking planning methods to speciregcenvironments has been found One group of research compares single materialplanning methods for example MRP and kanban and analyses their usabilityin various situations Newman and Sridharan (1995) identireged through a

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International Journal of Operations ampProduction ManagementVol 23 No 8 2003pp 872-900

q MCB UP Limited0144-3577DOI 10110801443570310486338

survey that companies could be high performers no matter whether they areusing MRP kanban or re-order point methods The method merely had tomatch the environmental characteristics Krajewski et al (1987) and Gianqueand Sawaya (1992) used simulation models and conceptual discussion toidentify differences in planning environments for MRP and kanban Similarapproaches exist for shop macroor control methods Bergamaschi et al (1997)presented eight dimensions that describe the fundamental characteristics andproperties of an order release procedure The framework is used in order toclassify research in the area but could also be an input for classifying planningmethods Reed (1994) compared the usability of dispatch lists and kanbans injob shops

Berry and Hill (1992) and Schroeder et al (1995) emphasized the importanceof understanding the characteristics of the planning environment Theydescribed cases where a mismatch between the market requirementsmanufacturing process design and choice of planning method affected theperformances of manufacturing regrms Olhager and Rudberg (2002) discussedthe importance of process choice when choosing planning methods on variouslevels These studies describe the planning environment in a more structuredbut still quite broad way They do not identify speciregc variables to differentiateunique planning environments and do not match unique planningenvironments and speciregc planning methods However there are somestudies that have developed more detailed frameworks for differentiatingvarious planning environments One quite comprehensive approach is that ofHoward et al (2002) who developed a rule-base for the speciregcation ofmanufacturing planning and control activities The model uses more than 100input characteristics to describe the speciregcs of a manufacturing company Theobjective of the model is to make recommendations about the suitability of 14main categories of manufacturing planning and control activities to individualmanufacturing companies The model is only valid for batch manufacturingand the presented research does not explain in detail the suitability of speciregcplanning and control methods in various manufacturing environments AlsoAng et al (1997) used a structured model to specify the characteristics of amanufacturing system However they did not link the manufacturing speciregcsto manufacturing planning and control activities Another approach is that ofAmaro et al (1999) who developed a classiregcation scheme for nonmake-to-stock manufacturing companies that could be used to differentiatethe planning environments of companies

Operations management and manufacturing planning and control textbooksalso discuss characteristic planning environments for material planningmethods (Fogerty et al 1991 Vollmann et al 1997 Olhager 2000 Jonsson andMattsson 2003) although not based on empirical data Most of the previousresearch also focuses strongly on materials planning methods

The implicationsof regt

873

Consequently there are some studies that compare the effects of usingvarious planning methods and some studies developing frameworks todifferentiate manufacturing planning environments However there is a lack ofempirical studies that match characteristic planning environments and types ofplanning methods This paper seeks to regll some of the gaps in the literature byproviding practical knowledge on how to differentiate various manufacturingplanning environments and conducting conceptual and empirical matchesbetween planning methods and planning environments We propose thatunsuccessful utilization of manufacturing planning and control systems aswell as not fully achieved production goals is often the result of not usingappropriate planning methods for the actual planning environment or usingthe methods in the wrong way (eg infrequent reviewing of parametersnon-optimum lot-sizing techniques etc) Therefore it is important tounderstand the appropriateness of using the planning methods in variousplanning environments The objective of the paper is to explain the regt betweenthe planning environment and planning methods as well as the perceivedsatisfaction or dissatisfaction with the chosen methods

In this paper we regrst conceptually identify product demand andmanufacturing process-related variables The variables are used to describefour main types of planning environments Then the appropriateness ofmaterial planning capacity planning and shop macroor control methods in thevarious environments is conceptually explained Finally the use and level ofperceived satisfaction and dissatisfaction with the various methods in the fourtypes of planning environments are empirically analyzed through survey dataThe design and structure of the analysis is presented in Figure 1

2 A framework for planning methods and planning environments21 Planning methodsThe planning methods are used on various planning horizons and levels ofdetail In long-term planning the planning object is often the end product orproduct group while on the detailed material planning level andmanufacturing operations on shop macroor level the planning object is theindividual dependent items The focus of the present study is on detailedmaterial planning shop macroor control and capacity planning levels At each

Figure 1Design of the analysis

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level there are a number of planning methods For each type of planningmethod there are several variants No variants are studied here onlyaggregated types of methods The choice of planning methods included in thisstudy is representative of those most readily available in commercial softwarepackages and included in common textbooks (Fogarty et al 1991 Vollmannet al 1997) and hence most likely to be used Therefore some options may bemissing for example speciregc order release rules and the theory of constraintmethods The methods included are presented in Figure 2

22 Environmental variablesNewman and Sridharan (1995) Krajewski et al (1987) Gianque and Sawaya(1992) have conducted focused studies on the choice of methods at the detailedmaterial planning level They especially compared material requirementsplanning re-order point system and kanban Newman and Sridharan (1995) forexample characterized the manufacturing environment in terms of productvolumevariety competitive priorities and process technology andinfrastructure availability within a regrm Berry and Hill (1992) stated that forcompanies to be successful it is necessary to link market requirements toprocesses and processes to manufacturing planning and control They showedexamples where a changed manufacturing process leads to a change inplanning methods Olhager and Rudberg (2002) further concluded that market(demand) characteristics are important at the higher planning levels (sales andoperations planning) while manufacturing process characteristics are mostimportant at lower levels (detailed material planning and shop macroor controllevels) Product characteristics on the other hand are important at all levelsThe rule-base for specifying manufacturing planning and control activitiespresented by Howard et al (2002) is based on similar market and company

Figure 2Planning methods

included in the study


875

characteristics Mattsson (1999) developed a framework of product demandand manufacturing process variables to characterize detailed material planningenvironments The planning environments in this paper are mainly based onthis framework

Consequently the planning environment can be characterized by a numberof variables related to the product the demand and the manufacturing processrespectively The following product related variables are considered criticalfrom a planning and control perspective

BOM complexity The number of levels in the bill of material and thetypical number of items on each level

Product variety The existence of optional product variants

Degree of value added at order entry The extent to which themanufacturing of the products is regnished prior to receipt of customerorder

Proportion of customer speciregc items The extent to which customerspeciregc items are added to the delivered product eg the addition ofaccessories

Product data accuracy The data accuracy in the bill of material androuting regle

Level of process planning The extent to which detailed process planningis carried out before manufacture of the products

The demand-related variables characterize demand and material macrow from aplanning perspective The following variables are considered critical

PD ratio The ratio between the accumulated product lead time and thedelivery lead time to the customer

Volumefrequency The annual manufactured volume and the number oftimes per year that products are manufactured

Type of procurement ordering Order by order procurement or blanketorder releases from a delivery agreement

Demand characteristics Independent or dependent demand

Demand type Demand from forecast calculated requirements or fromcustomer order allocations

Time distributed demand Demand being time distributed or just anannual reggure

Source of demand Stock replenishment order or customer order

Inventory accuracy Accuracy of stock on hand data

A third group of environmental variables characterizes the manufacturingprocess from a planning perspective The following variables are included inthis group

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Manufacturing mix Homogeneous or mixed products from amanufacturing process perspective

Shop macroor layout Functional cellular or line layout

Batch size The typical manufacturing order quantity

Through-put time Typical manufacturing through-put times

Number of operations Number of operations in typical routings

Sequencing dependency The extent to which set-up times are dependenton manufacturing sequence in work centers

Table I summarizes the detailed sub-variables of the three environmentalcharacteristics

23 Main types of planning environmentsThe manufacturing planning environment is company speciregc and normallydiffers from company to company To be able to compare companies withdifferent planning environments four main groups of companies were deregnedThe objective of the grouping was to get strong intra-groups similarities butdiscrimination between the groups with respect to their manufacturingplanning and control environments Therefore all individual companies do notnecessarily regt into any of the main groups The following groups were formed

(1) Complex customer products (type 1)

(2) Conreggure to order products (type 2)

(3) Batch production of standardized products (type 3)

(4) Repetitive mass production (type 4)

These four types are similar to Hillrsquos (1995) process choice types ie projectjobbing batch line and continuous However the types presented here arederegned purely from a manufacturing planning and control perspective while

Environmental variables

Product relatedDemand (material-macrow)related

Manufacturing processrelated

BOM complexity (depth) PD ratio Manufacturing mixBOM complexity (width) Volumefrequency Shop macroor layoutProduct variety Set-up times Batch sizeDegree of value added at

order entryType of procurement ordering Through-put time

Proportion of customerspeciregc items

Demand characteristics Number of operations

Product data accuracy Demand type Sequencing dependencyLevel of process planning Time distributed demand

Source of demandInventory accuracy

Table IEnvironmental

variables


877

Hillrsquos types are more general operations management types The classiregcationis based on the product demand and manufacturing process characteristicspreviously discussed in the paper The product complexity degree of valueadded at order entry volume and frequency of customer orders productionprocess shop macroor layout batch sizes and manufacturing through-put timewere used for differentiating the planning environments of various companies(see Appendix)

Complex customer order production type 1 implies a low volume lowstandardization and high product variety type of production It has similaritieswith Hillrsquos project and jobbing types The most characteristic feature of thistype of planning environment is that the products are more or less designedand engineered to customer order ie it is an engineer-to-order type ofoperation Manufacturing batch sizes are typically small and equivalent to thecustomer order quantity Products are complex with deep and wide bills ofmaterial The manufacturing throughput times and the delivery lead-times arelong The production process is designed for one-off production and afunctional layout is often applied

In the type 2 environment conreggure-to-order products the products haveless complexity and are assembled in small batches This type has most incommon with Hillrsquos jobbing and line processes It can be characterized as anassembly- or made-to-order type of operation where many optional productscan be conreggured and manufactured by combining standardized and stockedcomponents and semi-regnished items The number of customer orders is ratherlarge and the delivery lead-times much less than for the complex customerorder type of planning environment The throughput times for the assembly orregnishing operations are short and the batch sizes are typically small Line andcellular layouts are more common than functional layouts

The planning environment termed batch production of standardizedproducts can mainly be characterized as manufacturing to stock ofstandardized products in medium to large sized quantity orders This type ofenvironment is closest to Hillrsquos line process but could also exist in companieswith jobbing processes The number of customer orders is large eachcorresponding to a small quantity compared to the manufacturing batch sizesTypical throughput times and batch sizes are neither long and large nor shortand small when compared with the conditions in planning environments1 and 4

Repetitive mass production represents a planning environment whereproducts are made in large volumes on a repetitive and more or less continuousbasis This environment is closest to Hillrsquos continuous and line processes Itcannot exist in companies with jobbing processes It concerns standardizedproducts or optional products made or assembled from standardizedcomponents characterized by having macrat and simple bills of materials Theproducts are made-to-stock or made-to-schedule In this environment the

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customer orders are small and frequent in many cases call-offs from a deliveryschedule Typical throughput times are very short and the manufacturing iscarried out in some form of line layout

24 Conceptual matching of planning environments and planning methodsBased on a conceptual analysis of the characteristics of various planningmethods an assessment has been made of how well the various planningmethods can be expected to perform in the four types of planningenvironments Because of the scarcity of literature and reference support forthis assessment it is mainly based on logical assumption The conclusionsfrom this assessment are summarized in Table II The matrix can be seen as ahypothesis of to what extent the various planning methods can be effectivelyused and to what extent the users in each of the planning environments aresatisreged

Two plusses indicates a good match between the planning method and theplanning environment ie the planning method can be expected to performwith a high degree of effectiveness when used in that particular environmentand accordingly result in a high perception of satisfaction One plus means apoor match ie the planning method can be expected to work reasonably welland satisfactory A minus means a mismatch between the planning method

Planning environmentPlanning method Type 1 Type 2 Type 3 Type 4

Detailed materials planningRe-order point system + ++ +Runout-time planning + ++ +Material requirements planning + ++ ++ +Kanban - + + ++Order-based planning ++ + -

Capacity planningOverall factors - - ++Capacity bills + + +Resource proregles ++ + + +Capacity requirements planning + + ++ +

SchedulingInregnite capacity scheduling ++Finite capacity scheduling + ++Inputoutput control ++ + ++

SequencingSequencing by foremen + -Priority rules + +Dispatch lists ++ ++ ++ +

Note ++ Strong match + Poor match - Mismatch

Table IIConceptual matching ofplanning environments

and methods


879

and planning environment ie the planning method should not be used in thatparticular environment If still applied user satisfaction is not to be expectedCombinations with no marking are considered as neutral from the point of viewof effectiveness and user satisfaction

Detailed materials planning Considering that re-order point systems arecomponent rather than product oriented and basically designed for items withindependent demand they cannot be expected to perform very effectively inany of the four environments particularly in type 1 environments withcomplex product structures and long manufacturing lead-times Re-order pointsystems can however be used reasonably effectively the more standardizedthe product components are the longer life cycles they have and the morestable the demand (Jacobs and Whybark 1992 Newman and Sridharan 1995)These conditions apply particularly to type 3 environments The samearguments apply to runout-time planning (Jonsson and Mattsson 2002a)which is basically a re-order point type of system using time instead ofquantity as the controlling variable

Material requirements planning can be seen as a generally applicablematerial planning method It works reasonably well in all manufacturingcompanies irrespective of the speciregc planning environment (Newman andSridharan 1992) not least because of its strength in planning items withdependent demand Material requirements planning is however most effectivein environments with complex standardized products or product options longmanufacturing lead-times and items with time variations and uneven demand(Plenert 1999) Partly because the information provided by materialrequirements planning better captures the actual assembly requirementsmany regrms have converted from re-order point systems to materialrequirements planning systems (Bregman 1994) Jacobs and Whybark (1992)further showed that it requires a very proper relationship between the forecastand the actual demand before a re-order point system beats a materialrequirements planning system on inventory efregciency The conditionsdiscussed above are especially present in planning environments of types 2and 3 Accordingly a good match between material requirements planning andthese environments can be expected

Contrary to material requirements planning the relative strength of kanbanis greatest in environments with a regular and steady demand and where theproducts have a simple and macrat bill of material (Gianque and Sawaya 1992)Short lead-times and small order quantities also favor kanban (Newman andSridharan 1992) Accordingly environment 4 is the most suitable environmentfor kanban However in many cases a good performance can also be found inthe environments of types 2 and 3 especially if the order quantities arereasonably small Even if exceptions can be found for some items kanban isgenerally not a suitable method in planning environments of type 1 Thecomplex products often designed to order the long lead-times and the

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unpredictable and lumpy demand that characterize this environment are so farremoved from what kanban can cope with effectively that this combinationhas to be considered as a mismatch However integrated MRPkanbanapproaches can successfully cope with planning problems in high varietylowvolume manufacturing environments (Stockton and Lindley 1995)

The most characteristic feature of order-based planning is its ability tomanage complex and customer order speciregc products whether designed toorder or madeassembled to order Its relative strength is also in environmentscharacterized by long lead-times lumpy demand and items with dependentdemand (Jonsson and Mattsson 2002a) These conditions are present to a largeextent in type 1 environments and to some degree in type 2 environments alsoA match between order-based planning and these two environments cantherefore be expected Environment 4 with its even and repetitive demand ofstandardized items and simple products is more or less the opposite toenvironment 1 It is accordingly highly unlikely that order-based planning canbe implemented with any degree of success or that satisreged users will be foundin this environment This combination of planning method and environmentcan be considered as a mismatch

Capacity planning Capacity planning using overall factors represents thesimplest method of capacity planning Of the four capacity planning methodsstudied it requires the least detailed data and the least computational efforts Itis also the approach that is most affected by changes that occur in productvolume or the level of effort required to build a product (Blackstone 1989)Consequently a prerequisite for its successful application is that the productsare homogeneous from a manufacturing point of view The method assumesthat the load from manufacturing a product is in the same planning period asthe delivery date This means that the method should only be used inenvironments with a macrat bill of material and short lead-times compared to thelength of the planning period (Vollmann et al 1997 Jonsson and Mattsson2002b) This environment is present in particular in type 4 The complexproducts and typically long lead-times that characterize environments 1 and 3render overall factors inappropriate for use in these environments andaccordingly a mismatch is to be expected in the matrix for these combinations

Having a homogeneous type of manufacturing is less important when usingcapacity bills (also known as bill of labor or bill of resources approach) as thecapacity planning method It employs detailed data on the time standards foreach product Therefore poor time standards could become obstacles whenusing this method (Fogarty et al 1991) Burcher (1992) identireged that the lackof optimum use of resource and capacity planning was the absence of timestandards and routing information or the unreliability of this data This effecthowever becomes even greater for capacity planning employing resourceproregles and capacity requirements planning The capacity bills method alsoassumes that the load from manufacturing a product is in the same planning


881

period as the delivery date and like overall factors does not take stock-on-handfor components into account (Vollmann et al 1997) The match betweencapacity bills and planning environments 2 3 and 4 is therefore consideredpoor

Resource proregles allow the lead-time off-setting of a load relative deliverydate and accordingly this capacity planning method has advantagescompared to the previously mentioned methods for planning environmentswith long lead-times In common with capacity bills resource proregles rely ontime standards and do not consider the stock-on-hand of components used inthe products (Blackstone 1989) This means that only a poor match can beexpected for environments 2 and 3 In environment 4 the lead-time off-settingcapacity is less important and the simpler method capacity bills can bepreferable from a user point of view The relative strength of resource proreglesis in type 1 environments This is particularly relevant for engineer-to-ordertype of products because the method allows for capacity planning prior to theconclusion of the detailed design and production planning phase thus bills ofmaterial and routing regles are already available (Jonsson and Mattsson 2002b)

The most generally applicable capacity planning method irrespective ofplanning environment is capacity requirements planning It can be usedsuccessfully in all four types of environment but its relative strength is inenvironments with complex standard products or complex products that arecustom built from standardized components Capacity requirements planningalso considers stock-on-hand of components which means that it has majoradvantages in environments where components are manufactured in batches tostock (Fogarty et al 1991 Vollmann et al 1997 Jonsson and Mattsson 2002b)These characteristics can typically be found in planning environments 2 and 3

Shop macroor control The shop macroor control methods consist of scheduling(order release) and sequencing (dispatching) methods There are several rulesand approaches for solving scheduling and sequencing problems (Bobrowskiand Mabert 1988 Karmarkar 1989a b Melnyk and Ragatz 1989 Rohlederand Scudder 1993 Bergamaschi et al 1997) Here speciregc rules or variants ofmethods are not evaluated but instead general types of commonly usedmethods are compared Three types of scheduling methods (inregnite capacityscheduling regnite capacity scheduling and inputoutput control) and threetypes of sequencing methods (sequencing by foremen priority rules anddispatch lists) are studied

Of the various types of scheduling methods inregnite capacity scheduling isthe simplest It basically means that orders are released to the shop macroorirrespective of whether or not the current load is above available capacity Thismethod of releasing orders to the shop macroor is easiest to apply when the loadcan be expected to be reasonably even such as in environments with smallorder quantities and a smooth product demand ie in planning environmentsof type 4 The larger the order quantities for semi-regnished items the more

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uneven is the load experienced in manufacturing despite the fact that the loadis relatively even on the master production schedule level

In environments with uneven product demand and large order quantitiessupport from a scheduling system capable of loading orders to regnite capacitybecomes more important This makes it possible to more effectively avoidoverload and underload situations on the shop macroor Finite loading does notsolve the under-capacity problem It will however determine which jobs willbe dealt with based on priorities (Melnyk et al 1985) Unstable andunpredictable demand favors the use of regnite capacity scheduling as it allowsfor more frequent rescheduling and more sophisticated considerations to theentire scheduling situation These conditions can be found in particular inplanning environment types 1 and 3

With inputoutput control the release of orders to the shop macroor is managedon the basis of available capacity in the gateway work center (Vollmann et al1997) Long lead-times many operations in the routings and a functional layoutmake it difregcult to avoid overload or underload situations occurring in workcenters for the downstream operations The method is effective in situationswhere it is important to monitor backlog and to control queues work inprocess and manufacturing lead times (Fogerty et al 1991) As a consequenceinputoutput control can be expected to perform less satisfactorily in planningenvironment type 1 and to some extent in type 3 compared to types 2 and 4Several simulation studies have found that shop macroor workload reducingmethods show poor due date performance (Melnyk and Ragatz 1989 Land andGaalman 1998) However these methods should still provide relative beneregtscompared to inregnite capacity scheduling methods in the environmentsdiscussed

In a repetitive type of planning environment such as type 4 the shop macroorlayout is in most cases line oriented In this kind of environment it is ofparamount importance that the macrow of semi-regnished items and sub-assembliesis carefully coordinated with the manufacture of the end products This meansthat there is not much space for the foremen on the shop macroor to take locallyinmacruenced decisions concerning the sequencing of operations Accordinglysequencing by foremen is not an appropriate method in type 4 environmentsThe foreman has a good grasp of the departmentrsquos capabilities efregciency oforder sequencing and the actual conditions in the work center Allowing theshop foreman to take sequencing decisions is therefore of greater importancein environments where the manufacturing demands are more unpredictableand where it is difregcult to achieve accurate schedules This is especially thecase in environment 1 and consequently a poor match can be expected for thiscombination

The main difference between priority rules and dispatch lists is that thepriority rule method does not allow as frequent updating of the current statusas does sequencing based on dispatch lists Also the current loads in various


883

work centers and the status of work in progress along the routings can only betaken into account to a lesser degree when using the priority rule methodPriority rules differ for example in terms of whether they are static ordynamic or local or global (Melnyk et al 1985) Dispatch lists could be basedon priority rules or be more detailed and for example focus on capacityconstraints The most volatile situation from a scheduling point of view can befound in planning environments 1 2 and 3 In these environments the dispatchlist method can be expected to perform more effectively than the priority rulemethod This is especially the case in environments 1 and 3 where complexproducts routings with many operations and long lead-times add to theimportance of considering the current status of the entire scheduling situationwhen sequencing Another advantage of these centralized dispatch methods isthat they can improve communications among dispatchers (Fogerty et al1991)

3 MethodologyThe regt between planning environments and planning methods was analyzedconceptually in Section 2 as well as empirically in Section 4 Here themethodology of the empirical study is discussed

31 The sampleA mailed survey was sent to 380 members of the Swedish Production andInventory Management Society (PLAN) each representing differentmanufacturing companies The distribution of PLAN members inmanufacturing companies is in line with the Swedish average for theindustry (ie about half of the companies are in the mechanical engineeringindustry) A reason for sending the questionnaire to PLAN members was that itcould be expected that they would be interested in manufacturing planning andhave common knowledge about the terminology used in the survey PLANmembership is personal Therefore the studied companies were not expected toemploy more advanced planning methods than the average Swedishmanufacturing company Only one person per company was approachedand they were requested to answer only those sections with which they werefamiliar and to pass on the questionnaire to colleagues in a better position toanswer certain sections

Of the 380 companies surveyed 84 responded This is equivalent to aresponse rate of 22 per cent The questionnaire was rather long which mayexplain the relatively low response rate Almost half of the respondents were inthe mechanical engineering industry and more than half were employed bylarge companies (Table III) Companies with a turnover under 100 millionSwedish Kronas (MSEK) or less than 50 employees were deregned as smallThose with a turnover of between 100 and 300 MSEK and with more than 50employees were deregned as medium-sized companies

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32 Measures usedThere are three types of variables in this study The regrst measures the use ofthe respective planning methods The second describes the planningenvironment in each of the studied companies and the third type evaluatesthe perceived level of satisfaction with the planning methods used

Planning methods The respondents were given four alternatives whenevaluating the use of planning methods

(1) The method is not used

(2) Complementary use

(3) Main method

(4) Donrsquot Know

Respondents marking alternatives 2 or 3 were coded as usersPlanning environments A classiregcation system was developed and used to

characterize the type of planning environment in the 84 studied companiesOnly a few companies belonged to more than one environment and severalcould not be included in any of the four deregned generic planning environmentsOf the 84 studied companies 54 could be linked to speciregc types ofenvironments and were therefore included in the analysis (see section 41)

The small number of respondents within each planning environment made ithard to use statistical methods when analyzing the data The number of usersand the number of satisreged and dissatisreged users in the four planningenvironments were statistically tested (Chi-square) The proportion of satisregedand dissatisreged users were also identireged within each environment andcompared between environments without testing for statistical signiregcance

Perceived satisfaction The perceived satisfaction with planning methodswas based on the question ordfHow well do you consider that the method works inits practical applicationordm The answers were measured on a regve-point Likertscale where ordf1ordm was equivalent to ordfbadordm ordf3ordm to satisfactory and ordf5ordm to ordfvery

Respondents

SizeSmall 10 13Medium 26 33Large 42 54

IndustryFood manufacturing and chemistry 20 24Mechanical engineering 36 43Other industries 28 33

Note The food manufacturing and chemistry industries include the food manufacturing pulpand paper chemistry and plastic industries Other industries include the timber and ironsteelindustries

Table IIICharacteristics of

respondents


885

wellordm Respondents marking either of the alternatives ordf1ordm or ordf2ordm were deregned asordfunsatisregedordm users while those marking ordf4ordm or ordf5ordm were ordfsatisregedordm users Thisis not an objective measure as it only measures the perception of the managerIt is for example not directly related to relevant operations performancemetrics

33 The reliability and validityThe questionnaire was pre-tested and some questions were adjusted beforebeing distributed to the respondents All respondents were members of PLAN

A 12-page document with deregnitions and descriptions of the studiedmethods for materials planning capacity planning and shop macroor control wasattached to the survey with the aim of further improving the understandingand reliability of the study

The materials planning section of the survey had been tested andsuccessfully used in a previous study (Mattsson 1993) The capacity and shopmacroor control sections were formulated in a similar way to the materialsplanning section which increases the validity of the survey instrument

4 Empirical regndingsSection 24 discusses and explains why some planning methods can beassumed to be more common than others regardless of the planningenvironment It also indicates that the choice of method should depend on theenvironment and that the perceived satisfaction with a method should behigher if the method regts the environment This section empirically identiregesgeneric planning environments and analyzes the empirical regt betweenplanning methods and planning environments

41 Identifying generic planning environmentsIn order to characterize the four basic types of planning environment it wasconsidered sufregcient to use seven of the most important environmentalvariables in Table I (see Table IV) A simple classiregcation system based on theanswers in the questionnaire was then used to classify a company as belongingto one of the included types

For each of the seven environmental variables in Table IV different possibleordfvaluesordm were deregned as described in the Appendix The respondents selectedone of these alternatives to characterize their environments Each variable wasalso allocated a number of points up to three remacrecting how well that speciregcordfvalueordm characterized the respective environmental types (see Appendix) Thevalue ordfmore than regve levels in the bill of materialordm is for instance very typical oftype 1 environments and is allocated two points The total score for everycompany and each environmental type was calculated

A companyrsquos environmental type was then calculated based on the highestscore Companies with an identical or similar score for more than oneenvironmental type were eliminated from further analyses Companies for

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886

which the variable ordfdegree of value added at order entryordm did not match thespeciregcation in Table IV were also eliminated The purpose of this procedurewas to ensure that only companies with planning environments closelyresembling the four main types were included Out of the 84 companies thatresponded 30 were eliminated from further investigations Of the remaining 5411 belonged to type 1 (complex customer order production) 22 to type 2(conreggure to order products) 14 to type 3 (batch production of standardizedproducts) and seven to type 4 (repetitive mass production)

42 Empirical matching of planning environments and planning methodsThe utilization of the methods in different environments and the number ofsatisreged and dissatisreged users in the four main types of environments weretested with Chi-square statistics The levels of satisfaction and dissatisfactionwith each individual method in the respective environment were also studiedempirically although not statistically tested owing to too few counts in someof the analyzed data cells

Table V shows the number of satisreged and dissatisreged users in the fourplanning environments (irrespective of planning method) Conreggure to orderproducts (type 2) has signiregcantly less satisreged and more dissatisreged userscompared to the other environments In particular the capacity planningmethods have greater proportions of dissatisreged users in the type 2environment Batch producers of standardized products (type 3) on the otherhand have signiregcantly more satisreged and less dissatisreged users than in theother environments This can be interpreted to mean that most satisreged usersare to be found in stable planning environments while dissatisreged users tendto be found in dynamic environments (Table V)

Detailed materials planning The use of and perceived satisfaction with thematerials planning methods studied are distributed among planning

Planning environmentType 1 Type 2 Type 3 Type 4

Product characteristicsProduct (BOM) complexity High Medium Medium LowDegree of value added at order

entryETO ATOMTO MTS MTSATO

Demand characteristicsVolumefrequency Fewsmall Manymedium Manylarge Call-offs

Manufacturing processcharacteristicsProduction process One-off Batch MassShop macroor layout Functional Cellularline Cellularfunctional LineBatch sizes Small Small MediumlargeThrough-put times Long Short Medium Short

Table IVClassiregcation of

planning environments


887

environments as illustrated in Table VI Re-order point and MRP are the twomost commonly used planning methods MRP however is by far the mostwidely used ordfmain methodordm All four types of planning environments containedplanning methods with which the majority of the users were satisreged(Table VI)

The re-order point system is an important complementary method in mostenvironments The empirical analysis also reveals that it is one of the mostwidely used methods in all environments except that of repetitive massproduction It is not used to a signiregcantly greater extent in any oneenvironment Batch production of standardized products is the onlyenvironment where more than half (64 per cent) of the users were satisregedwith the chosen method and none were dissatisreged This is the environmentwhere the method has a strong conceptual match In all the other environmentsonly 34 per cent of users were satisreged and between 11 and 33 per cent weredissatisreged

Runout-time planning where orders are initiated when the runout-time ofthe available inventory is less than the sum of the lead-time and a safety time isan alternative to the re-order point system It is not a very common method Ithas signiregcantly (p 020) fewer users in the type 1 environment andsigniregcantly more in the type 3 environment (where it has a strong conceptualmatch) compared to the other two environments It has an overall higherproportion of satisreged users than the re-order point system In the type 3environment for example 80 per cent of the users were satisreged None weredissatisreged with this method

MRP is one of the most common methods in all environments Its use is notsigniregcantly higher in any one environment Kanban is signiregcantly lesscommon in the type 1 environment (p 005) where a conceptual mismatchwas identireged and signiregcantly (p 020) more common in repetitive massproduction where it has a strong conceptual match MRP and kanban controlare the only methods where more than 50 per cent of the users were satisregedand only a small proportion were dissatisreged irrespective of the planning


Satisreged 24 51 51 16(-) (+)

Dissatisreged 9 27 4 8(+) (-)

Notes The table shows the number of satisreged and dissatisreged users in respective planningenvironment Chi-square = 1394 (p = 0003) The sign within the parentheses indicates cellsthat differ signiregcantly (p 005) from the expected values (-) is signiregcantly lower and (+) issigniregcantly higher than expected

Table VSatisreged anddissatisreged users in theplanning environments

IJOPM238

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Pla

nnin

gen

vir

onm

ent

Type

1T

ype

2T

ype

3T

ype

4P

lannin

gm

ethod

sT

otal

Use

rsSat

isf

Dis

sat

Use

rsSat

isf

Dis

sat

Use

rsSat

isf

Dis

sat

Use

rsSat

isf

Dis

sat

Re-

order

poi

nt

syst

em40

(74)

837

1218

336

1164

03

3333

Runou

t-ti

me

pla

nnin

g11

(20)

04

500

580

02

00

Mat

eria

lre

quir

emen

tspla

nnin

g41

(76)

667

016

5019

1275

87

5714

Kan

ban

contr

ol19

(35)

0

978

06

6717

475

0O

rder

-bas

edpla

nnin

g23

(43)

8

3812

863

06

500

110

00

Note

sordfT

otal

ordmm

easu

res

the

num

ber

ofuse

rsa

nd

the

pro

por

tion

ofuse

rsam

ong

all54

com

pan

ies

(wit

hin

par

enth

eses

)ordfU

sers

ordmm

easu

res

the

num

ber

ofuse

rsof

resp

ecti

ve

met

hod

ordfSat

isfordm

mea

sure

sth

eper

centa

ge

ofsa

tisreg

eduse

rsordf

Dis

satordm

mea

sure

sth

eper

centa

ge

ofdis

sati

sreged

use

rs

Chi-sq

uar

e=

158

(p=

020

)

Sig

nireg

cantl

ydif

fere

nt

from

the

expec

ted

val

ue

(even

lydis

trib

ute

dam

ong

envir

onm

ents

)at

the

p

020

level

Sig

nireg

cantl

ydif

fere

nt

from

the

expec

ted

val

ue

(even

lydis

trib

ute

dam

ong

envir

onm

ents

)at

the

p

005

level

Table VIMaterial planning

methods withsatisreged users


889

environment (the only exception being kanban control which has a mismatchand is not used at all in the complex customer order environment)

Most kanban users in the type 2 3 and 4 environments were satisreged withthe method Conreggure to order products (type 2) and repetitive massproduction (type 4) are typical ordfKanban control environmentsordm and includemost of the satisreged and less dissatisreged kanban users

The high overall level of satisfaction with MRP is somewhat surprising inview of the fact that the method has been subject to much criticism over theyears and that it requires more accurate planning data than the other methodsOf all MRP users 61 per cent were satisreged with the method and only 12 percent were dissatisreged It has most satisreged users (75 per cent) in the batchproduction of standardized products environment which is a typical ordfMRPenvironmentordm The large difference between the proportions of satisreged anddissatisreged users also verireges that the method regts in this environment

MRP also has the highest proportion of satisreged users and has nodissatisreged users in the complex customer order production environment Thisseems to indicate that the ability of MRP to deal with high bill-of-materialcomplexity is more important than the ordering of customer speciregc items

Order-based planning which is considered to be a more appropriate methodin complex customer order environments has signiregcantly (p 005) moreusers in that environment compared to the other environments but only 38 percent were satisreged with the method and 12 per cent were dissatisregedOrder-based planning is also used in the other planning environments whereits levels of satisfaction are higher In those environments the method is not theordfmain methodordm but a complementary method

Capacity planning Capacity planning with overall factors and capacityrequirements planning are the two most commonly used capacity planningmethods (Table VII) They are even more dominant as ordfmain methodsordm Themethods capacity planning with capacity bills and resource proregles were onlyused by 15 and 20 per cent of the 54 companies respectively

The overall level of user satisfaction with capacity planning methods ismuch lower than for materials planning methods Of the users 22 per cent weresatisreged and 33 per cent dissatisreged Corresponding reggures for materialsplanning methods are 31 per cent and 13 per cent respectively

Conreggure to order products is the environment with the least number ofsatisreged and the most dissatisreged users Accordingly it would appear thatcapacity planning does not add any value in situations with small batch sizesand short through-put times and that frequent customer orders of customizedproduct variants drastically complicates capacity planning (Table VII)

The use of capacity planning with overall factors does not differsigniregcantly between environments About half (45 per cent) of the overallfactors users were satisreged with the method (Table VII) It is the simplestmethod to use which may explain why it has the highest proportion of satisreged

IJOPM238

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Pla

nnin

gen

vir

onm

ent

Type

1T

ype

2T

ype

3T

ype

4P

lannin

gm

ethod

sT

otal

Use

rsSat

isf

Dis

sat

Use

rsSat

isf

Dis

sat

Use

rsSat

isf

Dis

sat

Use

rsSat

isf

Dis

sat

Over

all

fact

ors

20(3

7)3

670

1030

305

400

210

00

Cap

acit

ybills

8(1

5)0

425

753

330

10

0R

esou

rce

pro

regle

s11

(20)

5

400

425

251

00

10

0C

apac

ity

requir

emen

tspla

nnin

g39

(72)

1010

2017

2935

1040

102

050

No

tes

ordfTot

alordm

mea

sure

sth

enum

ber

ofuse

rsan

dth

epro

por

tion

ofuse

rsam

ong

all54

com

pan

ies

(wit

hin

par

enth

eses

)ordfU

sers

ordmm

easu

res

the

num

ber

ofuse

rsof

resp

ecti

ve

met

hod

ordfSat

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mea

sure

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eper

centa

ge

ofsa

tisreg

eduse

rsordf

Dis

satordm

mea

sure

sth

eper

centa

ge

ofdis

sati

sreged

use

rs

Chi-sq

uar

e=

766

(p=

057

)

Sig

nireg

cantl

ydif

fere

nt

from

the

expec

ted

val

ue

(even

lydis

trib

ute

dam

ong

envir

onm

ents

)at

the

p

020

level

Sig

nireg

cantl

ydif

fere

nt

from

the

expec

ted

val

ue

(even

lydis

trib

ute

dam

ong

envir

onm

ents

)at

the

p

005

level

Table VIICapacity planning



891

users All of its users in the repetitive mass production environment weresatisreged This is the typical ordfoverall factors environmentordm with a strongconceptual match The statistical testing could not however reveal anyenvironment where the method was signiregcantly more popular For overallfactors a large proportion of satisreged users (67 per cent) were found amongcomplex customer order producing companies (type 1) This however iscontradictory to the conceptual matching but may be owing to the use of themethod for long-term resource planning

Capacity bills have only sporadic users and few of them are satisreged Themethod has signiregcantly fewer users (p 020) in the type 1 environmentwhere it has its poorest conceptual match compared to the other environmentsThe resource proregles method has signiregcantly more users (p 010) and is thesecond most common method in the type 1 environment which is its typicalplanning environment (with a strong conceptual match) compared to the otherenvironments Two out of regve users were satisreged with the method and nonewere dissatisreged which indicates that the method regts the environment

As expected capacity requirements planning is also the most frequentlyused capacity planning method in all environments The use of the methoddoes not differ signiregcantly between environments It has the highestproportion of satisreged users (40 per cent) in the type 3 environment which isthe typical ordfCRP environmentordm (strong conceptual match) This is the onlyenvironment in which it has more satisreged than dissatisreged users The methodis frequently used perhaps owing to its existence in most enterprise resourceplanning (ERP) software but it requires more accurate planning data thanother methods which may be a reason for user dissatisfaction

Shop macroor control Inregnite capacity scheduling is the most commonly usedscheduling method and dispatch lists is the most common method to supportsequencing of orders in production groups The number of users of shop macroorcontrol methods is lower than those of materials planning and capacityplanning methods The statistical testing could not reveal any signiregcantlydifferent patterns of use between environments Overall scheduling andsequencing methods have the highest proportion of satisreged users amongbatch producers of standardized products and the highest proportion ofdissatisreged users among conreggure to order and repetitive mass producersObviously the methods (especially sequencing) fail to properly manage andcontrol shop macroor activities in dynamic planning environments with shortthrough-put times Furthermore the four types of planning environment arenot very discriminating in respect of shop macroor control methods Therefore it ishard to draw too many conclusions from the data in Table VIII

The proportion of satisreged users of the three scheduling methods are 33 percent 30 per cent and 56 per cent respectively Corresponding reggures for thethree sequencing methods are 33 per cent 50 per cent and 38 per centInputoutput control is the least common but most satisfactory scheduling

IJOPM238

892

Pla

nnin

gen

vir

onm

ent

Type

1T

ype

2T

ype

3T

ype

4P

lannin

gm

ethod

sT

otal

Use

rsSat

isf

Dis

sat

Use

rsSat

isf

Dis

sat

Use

rsSat

isf

Dis

sat

Use

rsSat

isf

Dis

sat

Sch

edulin

gIn

regnit

eca

pac

ity

sched

uling

30(5

6)8

370

1414

217

710

10

0F

init

eca

pac

ity

sched

uling

20(3

7)5

2020

633

336

500

333

67In

put

outp

ut

contr

ol9

(17)

250

04

500

10

100

250

50

Seq

uen

cing

Seq

uen

cing

by

fore

men

21(3

9)4

250

729

07

430

333

33P

rior

ity

rule

s10

(19)

20

505

4020

250

01

010

0D

ispat

chlist

s37

(69)

1030

3014

2129

1060

03

670

Note

sordfT

otal

ordmm

easu

res

the

num

ber

ofuse

rsa

nd

the

pro

por

tion

ofuse

rsam

ong

all54

com

pan

ies

(wit

hin

par

enth

eses

)ordfU

sers

ordmm

easu

res

the

num

ber

ofuse

rsof

resp

ecti

ve

met

hod

ordfSat

isfordm

mea

sure

sth

eper

centa

ge

ofsa

tisreg

eduse

rsordf

Dis

satordm

mea

sure

sth

eper

centa

ge

ofdis

sati

sreged

use

rs

Table VIIIShop macroor methods with

satisreged users


893

method Sequencing with dispatch lists is the most popular sequencing methodand also the one with the highest proportion of satisreged users

Inregnite capacity scheduling requires low demand and capacity variations inorder to be used successfully and is therefore most applicable to the type 4environment It is however the most frequently used method in environments1 2 and 3 (although the differences are not statistically signiregcant) This isquite contradictory to the expectations Possible reasons may be that themethod is very simple to apply and that companies do not possess thenecessary software for regnite capacity scheduling or inputoutput control Thesmall number of respondents from the type 4 environment makes it difregcult toanalyze the use of shop macroor control methods in that environment Finitecapacity scheduling manages to control variations in demand and capacity in amore efregcient way than inregnite scheduling and therefore it regts the type 3environment even better Batch producers of standardized products (type 3) arethe most satisreged and least dissatisreged users of inregnite as well as regnitecapacity scheduling This supports the conceptual regt for regnite scheduling inthe environment as well as indicating that inregnite scheduling could also be anappropriate method Inputoutput control should regt environments with cellularlayouts but the small number of users makes it hard to statistically analyze theusability of the method

Dispatch lists are applicable to and used in all environments The types 3and 4 environments contain the highest proportion of satisreged and lowestproportion of dissatisreged users althoughdespite the fact that in the type 4environment this method has only a poor conceptual match

5 Conclusions and discussionIt should be possible to identify any of the four main types of planningenvironments (complex customer order production conreggure to orderproducts batch production of standardized products and repetitive massproduction) in manufacturing companies Each type has various productdemand and manufacturing process characteristics The appropriateness ofmanufacturing planning and control methods depends on the characteristics ofthe actual product demand and manufacturing process Consequently eachplanning method is applicable in varying degrees to the various planningenvironments

Most of the proposed conceptual matches between planning environmentand materials planning methods were identireged empirically (The conceptuallyderived appropriateness and the empirical use of the planning methods in thefour planning environments are summarized in Table IX The percentages ofsatisreged users are shown in Tables VI-VIII) Several matches could be veriregedfor capacity planning methods although the empirical data could not verifyany strong match between environment and planning methods for shop macroorcontrol methods The four environments are consequently most relevant for

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differentiating between materials planning and capacity planning methodsMore manufacturing process related variables should be used fordifferentiation of shop macroor control methods (Table IX)

51 Use and level of satisfaction with planning methodsMRP is the most applicable planning method at a detailed materials planninglevel It is used as the main planning method in most companies irrespective ofthe planning environment At least half of its users were satisreged with themethod regardless of the planning environment It is close to a perfect matchbetween the method and the environment characterized by the batchproduction of standardized products The empirical study further indicates thatMRP also functions well in complex customer order production Howeverorder-based planning is equally appropriate to that environment and it hassigniregcantly more users although fewer are satisreged and more are dissatisregedConsequently the ability to control bill of material complexity seems to bemore important than allowing for customized engineering


Planning method CM EM () CM EM () CM EM () CM EM ()

Detailed material planningRe-order point system 73 + 82 ++ 79 + 43Runout-time planning 0 + 18 ++ 36 + 29Material requirements planning + 55 ++ 73 ++ 86 + 100Kanban - 0 + 41 + 43 ++ 57Order-based planning ++ 73 + 36 43 - 14

Capacity planningOverall factors - 27 45 - 36 ++ 29Capacity bills 0 + 18 + 21 + 14Resource proregles ++ 45 + 18 + 7 + 14Capacity requirements planning + 91 + 77 ++ 71 + 29

SchedulingInregnite capacity scheduling 73 64 50 ++ 14Finite capacity scheduling + 45 27 ++ 43 43Inputoutput control 18 ++ 18 + 7 ++ 29

SequencingSequencing by foremen + 36 32 50 - 43Priority rules 18 + 23 14 + 14Dispatch lists ++ 91 ++ 64 ++ 71 + 43

Note CM = Conceptual match EM = Empirical match ++ Strong conceptual match + Poorconceptual match - Conceptual mismatch = Percentages of the respondents that use themethod Percentages in bold differ signiregcantly from the percentages of the method in the otherenvironments

Table IXSummary of matches

between planningenvironment and

planning methods


895

Most kanban users are satisreged with the method irrespective of theplanning environment It is appropriate for the repetitive mass productionenvironment but not for the production of complex customer-order productsRunout-time planning which is an alternative to the re-order point system ismost appropriate for the batch production of standardized products and it hasan overall higher proportion of satisreged and a lower proportion of dissatisregedusers compared to re-order points

The overall level of satisfaction with capacity planning and shop macroorcontrol methods is lower than with materials planning methods Capacityplanning with overall factors is the simplest capacity planning method and theone with the highest proportion of satisreged users Although capacityrequirements planning is the most complex and also the most common methodit is only in the batch production of standardized products environment that ithas more satisreged than dissatisreged users

Inregnite capacity scheduling is the most popular loading method despitebeing too simple for some environments Inputoutput control should be anappropriate method in product oriented environments but it is the leastcommon loading method

Sequencing by dispatch lists is the sequencing method with the highestproportion of satisreged and lowest proportion of dissatisreged users It is the mostadvanced sequencing method and it should regt most environments

52 Planning environmentsThe proportion of satisreged users differs between planning environments Batchproduction of standardized products has a signiregcantly higher proportion ofsatisreged users and a signiregcantly lower proportion of dissatisreged userscompared to the other planning environments The make-to-stock anddeliver-from-stock strategies result in stable planning environments which areconsequently very important for planning method applicability

Conreggure to order manufacturing is the only environment with signiregcantlyless satisreged and signiregcantly more dissatisreged users compared to the otherenvironments This indicates that it may be hard to carry out priority andcapacity planning in dynamic environments with short time-to-consumer

53 Future researchThe aim of the paper was to explain the appropriateness and regt of planningmethods in four different planning environments Several of the conceptuallyderived matches between environments and material and capacity planningmethods were verireged in the empirical study For shop macroor control howeversome other environmental variables (especially manufacturing process relatedvariables) should be used to differentiate between planning methods Theempirical study was based on a limited number of respondents which made itdifregcult to test for statistical signiregcance Therefore a replication study

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including a larger number of respondents and environmental variables morerelevant to shop macroor control would be of great value

Conreggure to order products and complex customer order production are theenvironments with the lowest proportion of satisreged users This study did notidentify the major reasons behind this regnding Therefore explorative casestudies are important in order to gain a deeper understanding of theappropriateness of various planning methods in any given environment andperhaps to develop new planning approaches for turbulent and dynamicenvironments

Choosing a planning method that regts an environment and that is applicableto the planning situation does not necessarily result in a satisfactory utilizationof the method It only improves the chances of user satisfaction with themethod The method also needs to be applied in a proper manner ie withcorrect parameter settings planning frequency etc The people responsible forplanning need the correct training and knowledge and the computer systemneeds appropriate hardware and software The present study did not evaluatethe modes of application or any other variables that may affect the satisfactoryusage of the planning methods The measure of satisfaction that was used inthe present study could also be developed and linked directly to companiesrsquoobjective operational performances Studies that focus on the modes ofapplication of methods and that link various modes to objective andoperational levels of satisfaction and performance would therefore beinteresting Such studies would further regll some of the gaps in the literatureand provide practical knowledge of manufacturing planning and controlmethods

References

Amaro G Hendry L and Kingsman B (1999) ordfCompetitive advantage customisation and anew taxonomy for non make-to-stock companiesordm International Journal of Operations ampProduction Management Vol 19 No 4 pp 349-71

Ang C Luo M Khoo LP and Gay R (1997) ordfA knowledge-based approach to the generationof IDEFO modelsordm International Journal of Production Research Vol 35 No 5 pp 385-413

Bergamaschi D Cigolini R Perona M and Portioli A (1997) ordfOrder review and releasestrategies in a job shop environment a review and classiregcationordm International Journal ofProduction Research Vol 35 No 2 pp 399-420

Berry WL and Hill T (1992) ordfLinking systems to strategyordm International Journal ofOperations amp Production Management Vol 12 No 10 pp 3-15

Blackstone JH (1989) Capacity Management South-Western Publishing Cincinnati OH

Bobrowski PM and Mabert VA (1988) ordfAlternative routing strategies in batchmanufacturing an evaluationordm Decision Sciences Journal Vol 19 No 4 pp 713-33

Bregman RL (1994) ordfAn analytical framework for comparing material requirements planningto reorder point systemordm European Journal of Operations Research Vol 75 No 1 pp 74-88

Burcher PG (1992) ordfEffective capacity planningordm Management Services Vol 36 No 10 pp 22-7


897

Fogarty DW Blackstone JH and Hoffmann TR (1991) Production and InventoryManagement South-Western Publishing Cincinnati OH

Gianque WC and Sawaya WJ (1992) ordfStrategies for production controlordm Production andInventory Management Journal Vol 33 No 3 pp 36-41

Hill T (1995) Manufacturing Strategy Text and Cases Macmillan London

Howard A Kochhar A and Dilworth J (2002) ordfA rule-base for the speciregcation ofmanufacturing planning and control system activitiesordm International Journal ofOperations amp Production Management Vol 22 No 1 pp 7-29

Jacobs FR and Whybark DC (1992) ordfA comparison of reorder point and materialrequirements planordm Decision Sciences Vol 23 No 2 pp 332-42

Jonsson P and Mattsson S-A (2002a) ordfThe selection and application of material planningmethodsordm Production Planning and Control Vol 13 No 5 pp 438-50

Jonsson P and Mattsson S-A (2002b) ordfThe use and applicability of capacity planningmethodsordm Production and Inventory Management Journal Vol 43 No 34

Jonsson P and Mattsson S-A (2003) ordfProduktionslogistikordm Studentlitteratur Lund (inSwedish)

Karmarkar US (1989a) ordfCapacity loading and release planning with work-in-progress (WIP)leadtimesordm Journal of Manufacturing and Operations Management Vol 2 No 2pp 105-23

Karmarkar U (1989b) ordfGetting control of just-in-timeordm Harvard Business Review Vol 67 No 9pp 60-6

Krajewski L King B Ritzman L and Wong D (1987) ordfKanban MRP and shaping themanufacturing environmentordm Management Science Vol 33 No 1 pp 39-57

Land M and Gaalman G (1998) ordfThe performance of workload control concepts in job shopsimproving the release methodordm International Journal of Production Economics Vol 56-57pp 347-64

Mattsson S-A (1993) ordfMaterialplaneringsmetoder i svensk industriordm (Institute forTransportation and Business Logistics) Vaxjo University Vaxjo (in Swedish)

Mattsson S-A (1999) Produktionslogistik Planeringsmiljoer och planeringsmetoder PermatronMalmo (in Swedish)

Melnyk SA and Ragatz GL (1989) ordfOrder reviewrelease research issues and perspectivesordmInternational Journal of Production Research Vol 27 No 7 pp 1081-96

Melnyk SA Carter PL Dilts DM and Lyth DM (1985) Shop Floor Control DowJones-Irwin Homewood IL

Newman W and SridharanV (1992) ordfManufacturing planning and control is there one deregniteanswerordm Production and Inventory Management Journal Vol 22 No 1 pp 50-4

Newman W and Sridharan V (1995) ordfLinking manufacturing planning and control to themanufacturing environmentordm Integrated Manufacturing System Vol 6 No 4 pp 36-42

Olhager J (2000) Produktionsekonomi Studentlitteratur Lund (in Swedish)

Olhager J and Rudberg M (2002) ordfLinking process choice and manufacturing planning andcontrol systemsordm International Journal of Production Research Vol 40 No 10 pp 2335-52

Plenert G (1999) ordfFocusing material requirements planning (MRP) towards performanceordmEuropean Journal of Operations Research Vol 119 pp 91-9

Reed R (1994) ordfPlant scheduling for high customer serviceordm in Wallace T (Ed) Instant AccessGuide World Class Manufacturing Oliver Wight Publications Essex Junction VTpp 377-85

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Rohleder TR and Scudder G (1993) ordfA comparison of order release and dispatching rules forthe dynamic weighted earlylate problemordm Production and Operations Management Vol 2No 3

Schroeder DM Congden SW and Gopinath C (1995) ordfLinking competitive strategy andmanufacturing process technologyordm Journal of Management Studies Vol 32 No 2pp 163-89

Stockton DJ and Lindley RJ (1995) ordfImplementing kanbans within high varietylow volumemanufacturing environmentsordm International Journal of Operations amp ProductionManagement Vol 15 No 7 pp 47-59

Vollmann T Berry W and Whybark C (1997) Manufacturing Planning and Control SystemsIrwinMcGraw-Hill New York NY

Further reading

Haddock J and Hubicki D (1989) ordfWhich lot-sizing techniques are used in materialrequirements planningordm Production and Inventory Management Journal Vol 30 No 3pp 29-35

Hendry L (1998) ordfApplying world class manufacturing to make-to-order companies problemsand solutionsordm International Journal of Operations amp Production Management Vol 18No 11 pp 1086-100

LaForge L and Sturr V (1986) ordfMRP practices in a random sample of manufacturing regrmsordmProduction and Inventory Management Journal Vol 27 No 3 pp 129-36

The Appendix follows overleaf


899

Appendix

Type 1 Type 2 Type 3 Type 4

Product (BOM) complexity1-2 levels in the bill-of-material and few included items 0 0 0 21-2 levels in the bill-of-material and several included

items 0 2 1 23-5 levels in the bill-of-material 1 1 2 0More that 5 levels in the bill-of-material 2 0 0 0

Degree of value added at order entryMake-to-stock and deliver from stock 0 0 3 2Assembly-to-order or plan 0 3 0 2Manufacturing-to-order 0 3 0 0Engineer-to-order 3 0 0 0

VolumefrequencyFew large customer orders per year 2 0 0 0Several customer orders with large quantities per year 0 0 2 0Large number of customer orders with medium

quantities every year 0 2 2 1Frequent call-offs based on delivery schedules 0 0 0 3

Production processContinuous process production 0 0 0 2Continuous mass production 0 0 0 2Frequent batch production (more frequent than

monthly) 0 0 2 0Batch production (less frequent than monthly) 0 0 1 0One-off or infrequent batch production 2 0 0 0

Shop macroor layoutFunctional layout (process layout) 2 0 0 0Cellular layout (macrow layout) 0 2 0 0Continuous line layout 0 2 0 2

Batch sizesEquivalent to customer order quantitiescall-off

quantities 2 2 0 0Small equivalent to one week of demand 0 0 0 0Medium equivalent to a few weeks of demand 0 0 2 0Large equivalent to a months demand or more 0 0 2 0

Through-put times in manufacturingShort through-put times a week or less 0 2 0 2Medium through-put times a few weeks 1 0 2 0Long through-put times several weeks 2 0 0 0

Table AIClassiregcation ofplanning environment

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900

survey that companies could be high performers no matter whether they areusing MRP kanban or re-order point methods The method merely had tomatch the environmental characteristics Krajewski et al (1987) and Gianqueand Sawaya (1992) used simulation models and conceptual discussion toidentify differences in planning environments for MRP and kanban Similarapproaches exist for shop macroor control methods Bergamaschi et al (1997)presented eight dimensions that describe the fundamental characteristics andproperties of an order release procedure The framework is used in order toclassify research in the area but could also be an input for classifying planningmethods Reed (1994) compared the usability of dispatch lists and kanbans injob shops

Berry and Hill (1992) and Schroeder et al (1995) emphasized the importanceof understanding the characteristics of the planning environment Theydescribed cases where a mismatch between the market requirementsmanufacturing process design and choice of planning method affected theperformances of manufacturing regrms Olhager and Rudberg (2002) discussedthe importance of process choice when choosing planning methods on variouslevels These studies describe the planning environment in a more structuredbut still quite broad way They do not identify speciregc variables to differentiateunique planning environments and do not match unique planningenvironments and speciregc planning methods However there are somestudies that have developed more detailed frameworks for differentiatingvarious planning environments One quite comprehensive approach is that ofHoward et al (2002) who developed a rule-base for the speciregcation ofmanufacturing planning and control activities The model uses more than 100input characteristics to describe the speciregcs of a manufacturing company Theobjective of the model is to make recommendations about the suitability of 14main categories of manufacturing planning and control activities to individualmanufacturing companies The model is only valid for batch manufacturingand the presented research does not explain in detail the suitability of speciregcplanning and control methods in various manufacturing environments AlsoAng et al (1997) used a structured model to specify the characteristics of amanufacturing system However they did not link the manufacturing speciregcsto manufacturing planning and control activities Another approach is that ofAmaro et al (1999) who developed a classiregcation scheme for nonmake-to-stock manufacturing companies that could be used to differentiatethe planning environments of companies

Operations management and manufacturing planning and control textbooksalso discuss characteristic planning environments for material planningmethods (Fogerty et al 1991 Vollmann et al 1997 Olhager 2000 Jonsson andMattsson 2003) although not based on empirical data Most of the previousresearch also focuses strongly on materials planning methods


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899

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Satisreged 24 51 51 16(-) (+)




IJOPM238

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IJOPM238

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891






IJOPM238

892

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nd

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tion

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sure

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ge

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sati

sreged

use

rs


satisreged users


893






IJOPM238

894












planning methods


895








IJOPM238

896




References










897























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900
























variables


877






IJOPM238

878











and methods


879





IJOPM238

880






881






IJOPM238

882







883





IJOPM238

884





(3) Main method

(4) Donrsquot Know





Respondents





respondents


885









IJOPM238

886













887






Satisreged 24 51 51 16(-) (+)




IJOPM238

888

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889










IJOPM238

890

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891






IJOPM238

892

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por

tion

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centa

ge

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sati

sreged

use

rs


satisreged users


893






IJOPM238

894












planning methods


895








IJOPM238

896




References










897























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900






IJOPM238

878











and methods


879





IJOPM238

880






881






IJOPM238

882







883





IJOPM238

884





(3) Main method

(4) Donrsquot Know





Respondents





respondents


885









IJOPM238

886













887






Satisreged 24 51 51 16(-) (+)




IJOPM238

888

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889










IJOPM238

890

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sat

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sat

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891






IJOPM238

892

Pla

nnin

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gm

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sat

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04

500

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100

250

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03

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res

the

num

ber

ofuse

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nd

the

pro

por

tion

ofuse

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ong

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pan

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ecti

ve

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sure

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eduse

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satordm

mea

sure

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eper

centa

ge

ofdis

sati

sreged

use

rs


satisreged users


893






IJOPM238

894












planning methods


895








IJOPM238

896




References










897























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900











and methods


879





IJOPM238

880






881






IJOPM238

882







883





IJOPM238

884





(3) Main method

(4) Donrsquot Know





Respondents





respondents


885









IJOPM238

886













887






Satisreged 24 51 51 16(-) (+)




IJOPM238

888

Pla

nnin

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vir

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sat

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rsSat

isf

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sat

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rsSat

isf

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sat

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order

poi

nt

syst

em40

(74)

837

1218

336

1164

03

3333

Runou

t-ti

me

pla

nnin

g11

(20)

04

500

580

02

00

Mat

eria

lre

quir

emen

tspla

nnin

g41

(76)

667

016

5019

1275

87

5714

Kan

ban

contr

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(35)

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-bas

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nnin

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(43)

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ber

ofuse

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nd

the

pro

por

tion

ofuse

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ong

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eduse

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eper

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005

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889










IJOPM238

890

Pla

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num

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sure

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onm

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ong

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onm

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005

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891






IJOPM238

892

Pla

nnin

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sat

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isf

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sat

Sch

edulin

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ity

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res

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num

ber

ofuse

rsa

nd

the

pro

por

tion

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ong

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pan

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ve

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sure

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eduse

rsordf

Dis

satordm

mea

sure

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eper

centa

ge

ofdis

sati

sreged

use

rs


satisreged users


893






IJOPM238

894












planning methods


895








IJOPM238

896




References










897























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900





IJOPM238

880






881






IJOPM238

882







883





IJOPM238

884





(3) Main method

(4) Donrsquot Know





Respondents





respondents


885









IJOPM238

886













887






Satisreged 24 51 51 16(-) (+)




IJOPM238

888

Pla

nnin

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sat

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order

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889










IJOPM238

890

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891






IJOPM238

892

Pla

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num

ber

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nd

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por

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eduse

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satordm

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sure

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eper

centa

ge

ofdis

sati

sreged

use

rs


satisreged users


893






IJOPM238

894












planning methods


895








IJOPM238

896




References










897























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900






881






IJOPM238

882







883





IJOPM238

884





(3) Main method

(4) Donrsquot Know





Respondents





respondents


885









IJOPM238

886













887






Satisreged 24 51 51 16(-) (+)




IJOPM238

888

Pla

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Chi-sq

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onm

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005

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889










IJOPM238

890

Pla

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sat

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sat

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sat

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ofsa

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891






IJOPM238

892

Pla

nnin

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100

250

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03

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res

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num

ber

ofuse

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nd

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por

tion

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ong

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pan

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eduse

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satordm

mea

sure

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eper

centa

ge

ofdis

sati

sreged

use

rs


satisreged users


893






IJOPM238

894












planning methods


895








IJOPM238

896




References










897























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900






IJOPM238

882







883





IJOPM238

884





(3) Main method

(4) Donrsquot Know





Respondents





respondents


885









IJOPM238

886













887






Satisreged 24 51 51 16(-) (+)




IJOPM238

888

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por

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ong

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onm

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)at

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005

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889










IJOPM238

890

Pla

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ong

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onm

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the

p

005

level




891






IJOPM238

892

Pla

nnin

gen

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Use

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Use

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Use

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sat

Sch

edulin

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ity

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uling

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20(3

7)5

2020

633

336

500

333

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put

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03

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num

ber

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nd

the

pro

por

tion

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pan

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satordm

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sure

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centa

ge

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sati

sreged

use

rs


satisreged users


893






IJOPM238

894












planning methods


895








IJOPM238

896




References










897























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900







883





IJOPM238

884





(3) Main method

(4) Donrsquot Know





Respondents





respondents


885









IJOPM238

886













887






Satisreged 24 51 51 16(-) (+)




IJOPM238

888

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Chi-sq

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IJOPM238

890

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Chi-sq

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891






IJOPM238

892

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num

ber

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por

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satordm

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sure

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eper

centa

ge

ofdis

sati

sreged

use

rs


satisreged users


893






IJOPM238

894












planning methods


895








IJOPM238

896




References










897























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900





IJOPM238

884





(3) Main method

(4) Donrsquot Know





Respondents





respondents


885









IJOPM238

886













887






Satisreged 24 51 51 16(-) (+)




IJOPM238

888

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IJOPM238

890

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891






IJOPM238

892

Pla

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por

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eduse

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satordm

mea

sure

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eper

centa

ge

ofdis

sati

sreged

use

rs


satisreged users


893






IJOPM238

894












planning methods


895








IJOPM238

896




References










897























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900





(3) Main method

(4) Donrsquot Know





Respondents





respondents


885









IJOPM238

886













887






Satisreged 24 51 51 16(-) (+)




IJOPM238

888

Pla

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ong

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onm

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IJOPM238

890

Pla

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ofsa

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ong

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onm

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891






IJOPM238

892

Pla

nnin

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7)5

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outp

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ol9

(17)

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04

500

10

100

250

50

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cing

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men

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9)4

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430

333

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rior

ity

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s10

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20

505

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01

010

0D

ispat

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1060

03

670

Note

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ordmm

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num

ber

ofuse

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nd

the

pro

por

tion

ofuse

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ong

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ve

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sure

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ge

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eduse

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Dis

satordm

mea

sure

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eper

centa

ge

ofdis

sati

sreged

use

rs


satisreged users


893






IJOPM238

894












planning methods


895








IJOPM238

896




References










897























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900









IJOPM238

886













887






Satisreged 24 51 51 16(-) (+)




IJOPM238

888

Pla

nnin

gen

vir

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ent

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2T

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3T

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Chi-sq

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ong

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onm

ents

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005

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889










IJOPM238

890

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Chi-sq

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ong

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onm

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005

level




891






IJOPM238

892

Pla

nnin

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sat

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edulin

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nd

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por

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pan

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sure

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eduse

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Dis

satordm

mea

sure

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eper

centa

ge

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sati

sreged

use

rs


satisreged users


893






IJOPM238

894












planning methods


895








IJOPM238

896




References










897























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900













887






Satisreged 24 51 51 16(-) (+)




IJOPM238

888

Pla

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sat

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889










IJOPM238

890

Pla

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ong

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891






IJOPM238

892

Pla

nnin

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sat

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por

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pan

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eduse

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Dis

satordm

mea

sure

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eper

centa

ge

ofdis

sati

sreged

use

rs


satisreged users


893






IJOPM238

894












planning methods


895








IJOPM238

896




References










897























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900






Satisreged 24 51 51 16(-) (+)




IJOPM238

888

Pla

nnin

gen

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sat

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sat

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sat

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order

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336

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eria

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por

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ong

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Chi-sq

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158

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onm

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ong

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onm

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889










IJOPM238

890

Pla

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sat

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isf

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sat

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fact

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20(3

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670

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5

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251

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No

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mea

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ong

all54

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ofsa

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eduse

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ge

ofdis

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sreged

use

rs

Chi-sq

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e=

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057

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onm

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ong

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onm

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891






IJOPM238

892

Pla

nnin

gen

vir

onm

ent

Type

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gm

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sat

Use

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isf

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sat

Sch

edulin

gIn

regnit

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ity

sched

uling

30(5

6)8

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init

eca

pac

ity

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uling

20(3

7)5

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333

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put

outp

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(17)

250

04

500

10

100

250

50

Seq

uen

cing

Seq

uen

cing

by

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men

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250

729

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20

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010

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03

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Note

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res

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ber

ofuse

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nd

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por

tion

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ong

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pan

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enth

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res

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num

ber

ofuse

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ecti

ve

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mea

sure

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centa

ge

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tisreg

eduse

rsordf

Dis

satordm

mea

sure

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eper

centa

ge

ofdis

sati

sreged

use

rs


satisreged users


893






IJOPM238

894












planning methods


895








IJOPM238

896




References










897























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900

Pla

nnin

gen

vir

onm

ent

Type

1T

ype

2T

ype

3T

ype

4P

lannin

gm

ethod

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otal

Use

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isf

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sat

Use

rsSat

isf

Dis

sat

Use

rsSat

isf

Dis

sat

Use

rsSat

isf

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sat

Re-

order

poi

nt

syst

em40

(74)

837

1218

336

1164

03

3333

Runou

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me

pla

nnin

g11

(20)

04

500

580

02

00

Mat

eria

lre

quir

emen

tspla

nnin

g41

(76)

667

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5019

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5714

Kan

ban

contr

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(35)

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6717

475

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-bas

edpla

nnin

g23

(43)

8

3812

863

06

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otal

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res

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num

ber

ofuse

rsa

nd

the

pro

por

tion

ofuse

rsam

ong

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pan

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hin

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ofuse

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ecti

ve

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hod

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mea

sure

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eper

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ge

ofsa

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eduse

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satordm

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sure

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eper

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ofdis

sati

sreged

use

rs

Chi-sq

uar

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158

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020

)

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ted

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ute

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ong

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onm

ents

)at

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005

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889










IJOPM238

890

Pla

nnin

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Type

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2T

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ype

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gm

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sT

otal

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isf

Dis

sat

Use

rsSat

isf

Dis

sat

Use

rsSat

isf

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sat

Use

rsSat

isf

Dis

sat

Over

all

fact

ors

20(3

7)3

670

1030

305

400

210

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Cap

acit

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8(1

5)0

425

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330

10

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(20)

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IJOPM238

892

Pla

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num

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the

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por

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sure

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centa

ge

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sati

sreged

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rs


satisreged users


893






IJOPM238

894












planning methods


895








IJOPM238

896




References










897























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900










IJOPM238

890

Pla

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IJOPM238

892

Pla

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IJOPM238

894












planning methods


895








IJOPM238

896




References










897























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900

Pla

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IJOPM238

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sreged

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satisreged users


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IJOPM238

894












planning methods


895








IJOPM238

896




References










897























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900






IJOPM238

892

Pla

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sati

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satisreged users


893






IJOPM238

894












planning methods


895








IJOPM238

896




References










897























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900

Pla

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ge

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sati

sreged

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satisreged users


893






IJOPM238

894












planning methods


895








IJOPM238

896




References










897























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900






IJOPM238

894












planning methods


895








IJOPM238

896




References










897























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900












planning methods


895








IJOPM238

896




References










897























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900








IJOPM238

896




References










897























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900




References










897























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900























IJOPM238

898





Further reading






899

Appendix














IJOPM238

900





Further reading






899

Appendix














IJOPM238

900

Appendix














IJOPM238

900

Documents

The implications of fit between planning environments and ... · volume/variety, competitive priorities, and process technology and infrastructure availability within a ® rm. Berry