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A multi-dimensional classification of production

systems for the design and selection of

production planning and control systemsBart L. Maccarthy

a & Flavio C. F. Fernandes

b

a Division of Maufacturing Engineering and Operations Management , University

Nottingham , University Parkb Department of Production Engineering , Federal University of Sao Carlos , via

Washington Luiz, KM 235-13565-905, Sao Carlos, SP, Brazil

Published online: 15 Nov 2010.

To cite this article: Bart L. Maccarthy & Flavio C. F. Fernandes (2000) A multi-dimensional classification of production systems for the design and selection of production planning and control systems, Production Planning

Control: The Management of Operations, 11:5, 481-496, DOI: 10.1080/09537280050051988

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PRODUCTION PLANNING & CONTROL, 2000, VOL. 11, NO. 5, 481 ± 496

A multi-dimensional classi®cation of production

systems for the design and selection of productionplanning and control systems

BART L. MACCARTHY and FLAVIO C. F. FERNANDES

Keywords production systems, classi®cation, productionplanning and control, analysis and design of production systems

A bstract. A primary requirement for improved understandingof the management of production systems is an appropriateclassi®cation of such systems. This paper proposes a classi®ca-tion that facilitates a better understanding of real productionsystems. It combines all the essential features, e.g. the ¯ow of materials with new classi®cation perspectives with respect toresponse time, repetitiveness and work organization. As muchprevious work has in¯uenced the approach proposed here, thepaper also presents a review of relevant literature. The classi®-cation has four groups of characteristics, comprising eightdimensions of descriptors, encompassing 12 variables. Choosingor designing an appropriate production planning and controlsystem ( PPCS) is a dicult task due to the integrative character

of the production planning and control area: it tends to interface with all functional areas of the enterprise. An importanobjective of the classi®cation system proposed here is to provida tool to assist in undertaking this dicult task. R eal productiosystems are becoming more hybrid in order to be able to copwith change. We show that our classi®cation can successfulldeal with such systems.

1. Introduction

Scienti®c knowledge is based on classi®cation. It habeen asserted for instance that expert systems are, in general, classi®cation systems ( Jain 1988) . It is not surprisintherefore that a classi®cation of classi®cations has beengiven. Good (1965) proposed the following classes base

A uthor s: B. L. MacCarthy, Division of Maufacturing Engineering and Operations ManagementUniversity of Nottingham, University Park, Nottingham NG7 2RD, UK, and F. C. F. FernandeDepartment of Production Engineering at Federal University of SaÄo Carlos, via Washington LuizKM 235-13565-905, SaÄo Carlos, SP, Brazil.

B AR T M A CC AR THY is a Senior Lecturer in the Division of Manufacturing Engineering anOperations Management at the University of Nottingham. Prior to his appointment in 1987 hhad substantial experience in research and management in manufacturing industry including thengineering, textiles and clothing sectors. He has been Vice-Dean of the Faculty of Engineering fo3 years and was acting Head of Division during the academic year 1996/1997. His research spansystems analysis, modelling, optimization, and simulation in manufacturing, operations and logistics. He has researched and published widely on decision support for planning and schedulin

Current research projects include the analysis and modelling of responsiveness in order ful®llmenprocesses, statistical process control for performance measurement in scheduling and the modellinof supply chains in the public sector.

FLAVIO C . F . FERNANDES has been an Assistant Professor at the Federal University of SaÄo Carloin Brazil since 1991. He has authored more than 30 papers on production planning and control anoperations research, and refereed for several academic and professional journals. During 1988 hhas been a visiting scholar in the Division of Manufacturing Engineering and OperationManagement of the University of Nottingham in the UK. One of his principal current interestis how to reduce the gap between theory and practice in the production planning and control ®eld

Production Planning & Control ISSN 0953 ± 7287 print/ISSN 1366 ± 5871 online # 2000 Taylor & Francis Ltdhttp://www.tandf.co.uk/journals

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on the purposes of producing classi®cations: ( i) for men-tal clari®cation and communication; (ii) for discoveringnew ®elds for research; ( iii) for planning an organiza-tional structure or machine; (iv) as a check list; (v) forfun. The classi®cation we propose in this paper falls prin-cipally into the third group but, as our classi®cationargues a new point of view, it is also useful for mental

clari®cation and communication.Burbidge (1985) noted that when an engineer designs amachine, laws of physics and metallurgy help to producean ecient design, but there is not a set of scienti®c lawsto help a production engineer faced with the problem of designing an ecient production system. We contendthat this situation has not changed signi®cantly in theintervening years. One of the reasons for this is thatthere is not an appropriate classi®cation of real produc-tion systems. Existing classi®cations are oversimpli®ed,only consider a limited number of aspects or take a spe-ci®c perspective. These classi®cations tend to be of little

or limited value for analysing complex real productionsystems or for aiding operations managers in selecting ordesigning appropriate production planning and controlsystems (PPCSs). Selecting or designing an appropriatePPCS is a dicult task due to the integrative character of the production planning and control function: it tends tointerface with all functional areas of the enterprise. A keyaspect of designing or selecting an eective PPCS is theability to classify production systems. This paper focuseson this area, as we believe it constitutes a signi®cant gapbetween theory and practice in operations management.

It would be pretentious to propose a totally watertight

classi®cation. Moreover, any such claim is likely to beeasily contradictable. In practice any classi®cation is atrade-o between the level of detail needed for usefulnessand the level of aggregation desirable for usability. Webelieve our classi®cation here will be a signi®cant con-tribution for understanding complex real productionsystems, and for aiding design or selection of productionplanning and control systems in practice.

This paper has been in¯uenced by much previouswork. Therefore, a literature survey is presented in sec-tion 2. The multi-dimensional classi®cation is presentedin section 3. Section 4 shows how to apply the approach.

The ®nal section presents the conclusions.

2. Review of literature on the classication of

production systems

Here we survey the most important literature directlyrelevant to the aim of this paper. We have selected refer-ences that are illustrative of approaches taken to thesubject. We present a review of classi®cations of produc-tion systems in section 2.1 and a fairly brief review of

classi®cations of production subsystems in 2.2. In sectio2.3 we highlight the limitations of existing classi®cationsespecially from the perspective of PPCS. In particular woutline the major de®ciencies that need to be addressedwith respect to contemporary manufacturing enterprisein section 2.4.

2.1. A review of production system classication

2.1.1. P ioneering classications

The pioneering classi®cations are well known. Mallicand Gaudreau (1951) identi®ed three types of production: ( i) continuous process (with disintegration, e.gpetrol re®ning; or with integration, e.g. synthetic rubbeprocessing) ; (ii) mass production; and ( iii) intermittenprocess. Wild ( 1971) splits the last type, names some othe classes dierently and presents the following simila

classi®cation: (i) process manufacture; (ii) mass production; (i ii) batch production ; ( iv) jobbing manufactur(unitary production) . Many textbooks have followethis type of scheme.

Burbidge (1962) de®ned the following productiotypes: (i) line production [batch quantity (BQ) : 1; typof ¯ow ( TF) : line] ; (ii) batch production (BQ: more than1; T F: functional) ; ( iii) jobbing production (BQ: same aorder quantity, generally small; T F: functional) ; ( iv) process batch production (BQ: more than 1; TF: line) ; (vprocess jobbing production (BQ: same as order quantitygenerally small; TF: line). The following types of layou

were presented: ( i) functional layout; ( ii) group layoutand (iii) line layout. Burbidge (1971), relating these typeof layout and some characteristics of production controlde®nes seven types of production systems: (i) line production; ( iii) line batch production; ( iii) group batch production; ( iv) functional batch production; (v) line jobbinproduction; (vi) group jobbing production; (vii) functional jobbing production.

Insucient attention has been given to empirical daton real production systems. Woodward (1965, 1980) conducted research on manufacturing ®rms in a region in thUK. From information collected from 92 ®rms their pro

duction systems were then classi®ed into 11 categories: (iproduction of units to requirements; ( ii) production oprototypes; (iii) fabrication of large equipment in stages( iv) production of small batches to customers’ orders; ( vproduction of large batches; (vi) production of customerslarge batches on assembly lines; (vii) mass production(viii) intermittent production of chemicals in multi-purpose plant; ( ix) continuous ¯ow production of liquidgases and crystalline substances; (x) production of standardized components in large batches subsequentlassembled diversely; (xi) process production of crystallin

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substances, subsequently prepared for sale by standard-ized production methods. Eight ®rms did not ®t any of the categories, a further four were extremely mixed andthe other two were in transition.

The classi®cation of Conway et al . (1967) should alsobe noted as it is used particularly in operational research:( i) single machine; ( ii) parallel machines; ( iii) ¯ow-shop;

and ( iv) job-shop. As noted by Botta et al . (1997), thiswas expanded by Lenstra to include hybrid organizationscharacterized by parallel machines at any processingstage.

2.1.2. Classications derived by attributes

Here we consider classi®cations based on attributesthat are perceived as important in production systemsor manufacturing enterprises more generally. Johnsonand Montgomery (1974) classify production systems

based on the types of products and processes as follows.( i) Continuous system: few families of similar productsproduced on a large scale. ( ii) Intermittent system: fre-quent changes in the production stages from one productto another, as a consequence of the large variety of man-ufactured items. They identify two subclasses: (a) inter-mittent ¯ow-shop system: the ¯ow pattern of all items isthe same; (b) intermittent job-shop system: items do nothave the same ¯ow pattern. ( iii) Large project system:products are complex and special, and in many cases, areproduced in unitary quantities. They also consider afourth typeÐ(iv) the pure stock systemÐwhere items

are bought, warehoused, distributed and sold, withouta processing phase.

Observing that cellular manufacturing is intermediatein terms of application between the job-shop and the¯ow-shop, Black (1983) proposed the following classi®ca-tion: (i) large project system; (ii) job-shop; (iii) cellularmanufacturing; ( iv) ¯ow-shop ; (v) continuous system.Putnam (1983) summarized the basic dierencesbetween job-shop and ¯ow-shop systems.

Constable and New (1976) consider three characteris-tics in their approach: the structure of products (simpleor complex) ; the layout ( in line, functional or group lay-

out) ; and the nature of the customers’ orders ( for stock orby order). Bua and Miller (1979) adopt a classi®cationwith four types of inventory production systems: ( i) con-tinuous system for stock; (ii) continuous system by order;( iii) intermittent system for stock; and (iv) intermittentsystem by order. An intermittent system indicates thatproduction occurs in lots. Large projects are includedin category (iv).

Nys ( 1984) de®ned the technological system (TS) asthe part of a manufacturing system that comprises a set of equipment for executing the technological process, the

equipment being linked by a material ¯ow and an information ¯ow. He presented two equivalent classi®cationon the basis of features of useÐa parallel classi®cationand a morphological block classi®cation.

Schmidt et al . (1985) proposed classes derived from threlationship between task divisibility, routing restrictionand production rate uniformity. Frizelle (1989) present

a categorization for plants by means of three letters (V, Aand T) that resemble the `shape’ of the plant. The `Vplant’ `is characterized by few raw materials subdividininto many ®nished products’. The `A plant’ present`many raw materials being assembled into few ®nishedproducts’. The `T plant’ has a number of componentthat `can be assembled in a multiplicity of waysSipper and Shapira (1989) classify production systemin accordance with the inventory control policy as: (ipure WIP system; ( ii) modi®ed WIP system (planneto partly satisfy expected shortages) ; ( iii) modi®ed JITsystem (without any intermediate buer stock and lot

greater than one) ; and (iv) pure JIT system (unitarlots).

In order to clarify the meaning of repetitive and intermittent manufacturing, De Toni and Panizzolo ( 1992de®ne six categories of manufacturing systems: ( i) indvidual [type of plant (TP) : yards] ; (ii) unique (TPlaboratories) ; (iii) intermittent (T P: job-shops ancells) ; ( iv) discontinuous (TP: batch plants) ; ( v) repetitive (TP: discrete lines) ; and (vi) continuous ( TP: proceslines). The classes were obtained by combining classi®cation of manufacturing systems, production volume an

how the product is produced.Wild (1995) in his well-known textbook ®rst classi®ethe operational system by function (manufacture, transport, supply and service), and then by structure. Wilclassi®es manufacturing systems as: (i) make from stockto stock, to customer; (ii) make from source, to stock, tcustomer; (iii) make from stock, direct to customer; (ivmake from source, direct to customer.

Pyoun et al . (1995) present a classi®cation of the realizable ¯exibility in automated manufacturing systemsThey identify: (i) mass production; (ii) mid-variety andmid-volume; and ( iii) multi-variety and small-volum

production systems. Jichao (1996) classi®es productiosystems for the purpose of variability detection, as: ( isimple production system (includes either a single procesor independent multiple processes) ; and (ii ) complex production system (many processes with close inter-relationships). Dulmet et al. ( 1997) propose a classi®cation oprocesses according to the relationship between procesand product. In their approach the degree zero object ithe product, degree one object has direct leverage on thproduct (e.g. tools and pallets) up to the degree n objectwhich has direct leverage on the degree n ¡ 1 object. They

M ulti-dimensional classi cat ion of product ion syst ems 48

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state that it is su cient to dene ve levels for a description of a

give n shop.

2.1.3. Descriptive classications

These are classi®cations based on a description of the

attributes of ®rms or production systems in a ®nite num-ber of classes. Ingham (1971) de®ned eight types of busi-ness according to the relationships between marketingand production. From his work, it is possible to identifythe following classi®cation: make to stock; customize toorder, i.e. the client can specify its needs in terms of design of certain class of products; make to order, i.e.the items among a wide range of options are manufac-tured after the con®rmation of customers’ orders; make toorder and to stock, i.e. make from orders for a wide rangeof products and make for stock in the case of standardproducts with considerable and continual demand; make

products to order and major components to stock (thisclass corresponds to what is now known as assemble toorder) ; totally customized to order, i.e. `the ®rm does notoer a range of products but a production service withinthe limits of its equipment’.

Barber and Hollier (1986a, 1986b) developed a classi-®cation of engineering batch manufacturing companiesbased on questionnaire evidence. They propose sixgroups according to the level of production control com-plexity. It is clear that several of the measures have beendicult to estimate or interpret for many ®rms andresponses were necessarily subjective.

McCarthy et al. (1997) consider the evolution of manu-facturing changes in order to show how to construct aclassi®cation based on cladistics (Kitching e t al . 1998)and give an example using the automotive sector. Theyemphasize that with cladistics `it is possible to examinethe way in which characters change within groups overtime, the direction in which characters change, and therelative frequency with which they change . . . Thus,organizational cladograms can be used as a tool forachieving successful organizational change’. Un-fortunately, other sectors are not nearly as well documen-ted in the literature as the automotive sector.

2.2. Classications of production subsystems

Important contributions were made by Petrov (1966),Wild (1972) and Carrie (1975). Aneke and Carrie (1984)integrated existing classi®cations of simple ¯ow lines(mass production) and group technology ¯ow line usingsix criteria. The classi®cation reduces to: (i) single-prod-uct single-machine system; ( ii) single-product multi-machine system; (iii) mixed-product single-machine

system; (iv) multi-product single-machine system; (vmixed-product sequential ¯ow line I, where all the products have the same sequence of operations and there is nnecessity for machine resetting; (vi) multi-producsequential ¯ow line where products have the same operational sequence but they have to be produced separatelin batches; (vii) mixed-product bypass ¯ow line I wher

some products are not processed in all machines anequipment resetting is not necessary; (viii) mixed-product bypass ¯ow line I I, where some products are noprocessed in all machines and equipment resetting inecessary; ( ix) multi-product backtracking ¯ow linwhere equipment resetting occurs, production is ibatches and the variation in operational sequences idue to omitted and/or backtracked operations and thproduct ¯ow is bidirectional; and (x) multi-producmultidirectional backtracking system where the productare batched, the operational sequences are so varied tha¯ow is multidirectional, and therefore, line production i

not feasible.Burbidge (1970) identi®ed four categories of grou

technology systems: (i) single-machine system; (iigroup layout system; (iii) total group layout system(group layout plus classi®cation and coding systemvalue analysis, variety reduction, standardization) ; an(iv) line ¯ow system, with characteristics between clas(iii) and mass production.

Many classi®cations of ¯exible manufacturing system(FMS) have been presented, e.g. Groover (1980), Kusia(1985) , and Maimon and Nof (1986) . Browne e t a(1984) classi®ed FMSs as: ¯exible machining cells, ¯ex

ible machining systems, ¯exible transfer lines and ¯exibltransfer multi-lines. Stecke and Browne ( 1985) added thdescriptor `type of material handling system’ to the previous classi®cation, so the resulting classi®cation is basedon the ¯ow pattern of parts and more speci®cally on throuting ¯exibility. MacCarthy and Liu (1993) and Liuand MacCarthy (1996 ) presented a classi®cation schemfor FMSs based on a consistent set of de®nitions. Thedistinguished between: (i) a single ¯exible machin(SFM) ; (ii) a ¯exible manufacturing cell (FMC) ; (iiia multi-machine ¯exible manufacturing system(MMFMS) ; and ( iv) a multi-cell ¯exible manufacturin

system (MCFMS), and show the relationship betweethem.

2.3. General comments on existing classications

The only signi®cant previous paper that we have foundthat has reviewed production system classi®cations is thaby McCarthy (1995) with 14 references. He noted thaprevious classi®cations, with the exception of Barber andHollier (1986a), were subjective and that cladistics pro


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vided a way of making an objective classi®cation. Wewould argue however that all classi®cations are necess-arily subjective and that the important issue is not thesubjectivity of the classi®cation but rather its usefulness.Classi®cation has to be subjective because it represents anauthor’s perspective on production systems. AlthoughBarber and Hollier (1986a) use a quantitative approach

in arriving at their classi®cation scheme, it is also subjective because the number of groups, the characteristicsthe system may possess and the factors utilized to measurethe characteristics are all chosen. Moreover, McCarthyet al . (1997) also present an essentially subjective classi®-cation, e.g. there are many subjective choices in thechosen clade.

All attempts at classi®cation are necessarily approxi-mations and can always be criticized on this basis. Anyclassi®cation involves choosing between the level of detailand the level of aggregation. However, if the perspectiveand the rationale are clear then the bene®ts of a good

classi®cation are many. Some of the classi®cations dis-cussed here are useful in providing insight and under-standing of production systems, and some have clearlydemonstrated their usefulness in particular domains.We argue, however, that there is a real need for classi®-cations with de®ned objectives and areas of application.Unfortunately this is not the case in many of the classi®-cations above.

2.4. Developing a classication for the design or selection of

PPCS

The need for improved classi®cations for the study,analysis, design and management of production systemsin today’s global economies is clear. For example,Banerjee (1997) shows evidence that ` in spite of manymillions having been spent in manufacturing planningand control systems . . . no real solution to the need forgreater responsiveness and ¯exibility has been found’. Inthe context of contemporary manufacturing organiza-tions, and in particular their control systems, the follow-ing are drawbacks that can be applied in some measure

to the classi®cations discussed above.

. Too general or very super®cial to be of use in anycontext.

. Disregard of important concepts, e.g. ¯ow pattern.

. Consideration of ¯ow-shop characteristics onlywithin group technology cells.

. Placing the concept of job-shop in the context of ¯ow lines.

. Failing to distinguish between assembly lines andproduction lines.

. Consider assembly as a process related only tassembly lines, ignoring the fact that other typeof installations may also carry out assembly operations.

Many further issues are crucial in contemporarmanufacturing organizations and need to be addresse

in eective classi®cations. In the past, changes in businesneeds occurred at low speeds and simple classi®cations oproduction systems were appropriate as a basis fodesigning a production system. In today’s environmentsin order to accommodate more rapid changes, manufacturing systems have become more and more hybridThere is a real need for a classi®cation that treats hybriproduction systems in depth. A further issue is that owork organization . Previous classi®cations do not consider this issue but it is vital in contemporary organizations. Response time and repetitiveness are also criticavariables from a control viewpoint and an eective clas

si®cation must address them. It is not sucient just tadd a new variable to an existing classi®cation. The overall scheme must be coherent in order to be applied eectively.

The classi®cation we present in the remainder of thipaper addresses these issues and most of the points notedabove in a rational way. Our principal objective is to aidthe design and selection of production planning and control systems. The previous classi®cations that were mosin¯uential to us are: Wild (1971), Burbidge (1971Ingham (1971), Johnson and Montgomery (1974Constable and New (1976) , Black (1983) and Anek

and Carrie (1984).

3. A multi-dimensional classication for

production systems

3.1. Overall structure

A production system may be de®ned as a set of interrelated elements that are designed to act in a manner thagenerates ®nal products whose commercial value exceedthe costs of generating them. From the nature of thes

elements, two types of subsystem may be identi®edphysical systems and managerial systems. In the ®rsthe elements are physical entities ( e.g. a machine) anin the second the elements are procedures that transformdata into information in a decision process (e.g. an MRPsystem) . Naturally people design and operate both typeof subsystem and can therefore be considered as elementof both.

Here we propose a multi-dimensional classi®cation foproduction systems. We identify four main groups ocharacteristics, comprising eight dimensions (A/B/C/D


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E/F/G/H). Where appropriate, important variableswithin some of the dimensions have also been identi®ed.The four groups and their associated dimensions are asfollows.

(1) General characterization: encompasses the follow-ing dimensions: enterprise size ( A) ; response time

(B) ; repetitiveness ( C) ; and automation level (D).(2) Product characterization: encompasses the prod-

uct description (E).(3) Processing characterization : encompasses the pro-

cessing description (F).(4) Assembly characterization: encompasses the

dimensions: types of assembly (G) and types of work organization (H).

The choice of these groups, dimensions and variableswas determined by our principal objective, i.e. that theclassi®cation be a valuable tool for designing or choosing

a PPC system. Our selection is in¯uenced by our indus-trial and academic experience in PPC. It is our view thatthe dimensions, variables and levels represent a sucientset in terms of breadth, depth and level of detail to cap-ture the salient characteristics of most production systemsfrom the perspective of production planning and control.In particular we have addressed the de®ciencies of exist-ing classi®cations with respect to the reality of contem-porary manufacturing organizations. The relevance of the scheme to production planning and control systemsis discussed in section 4, and examples of applications arepresented in section 4.2.

In the following section we present a description of thescheme. We enlarge on the discussion where we feel itnecessary to justify our selection of dimensions and vari-ables. We also emphasize some parts of the classi®cationbecause of their importance and novelty. In particularwe present new insights in relation to the existing classi-®cation literature on response time, repetitiveness level,¯ow of materials and types of work organization. Thelatter we have highlighted under assembly but in some

cases it may also be necessary to consider it under processing characteristics.

Table 1 illustrates the structure of the classi®catioscheme. We use the forward slash symbol …=† to separatthe dimensions and the underscore symbol …¡† to separate the variables. Letters and numbers are used as shorthand notation for levels or categories of each within each

dimension.

3.2. Dimensions and variables of the classication system

3.2.1. General characterization

3.2.1.1. First dimension ( A) : enterprise size Several descriptors may de®ne the size of an enterprise

turnover, number of employees, market share, etc. Fothe purpose of analysing the PPCS, turnover or revenu

are not good descriptors because either or both may blarge if, e.g. raw materials are very expensive. The morrelevant descriptor of enterprise size is the number oemployees. By the number of employees, a ®rm in thUK is considered large if it has more than 250 employee(in Brazil, more than 500) and it is considered mediumsized if the number of employees is between 50 and 25(between 100 and 500 in Brazil).

3.2.1.2. Second dimension ( B) : response time Simply stating that a production system is make-to

stock or make-to-order, etc. is of little use from thpoint of view of designing modern production planninand control systems. Here we identify the dimensionresponse time, which speci®es how, strategically, aenterprise wants to attend to its customers’ needs.

Figure 1 illustrates three important parameters of anindustrial production system: supplier lead time (SL)production lead time (PL) and distribution lead tim(DL). The ` ®rst tier’ refers to the ®rst vendor in thsupply chain. The response time (RT) of the productionsystem is the sum of SL, PL and DL. Eective manage


Table 1. The multi-dimensional classi®cation system ( MDCS).

General characterization * Enterprise size* Response time* Repetitiveness level* Automation level

Product characterization Product description * Product structure* Level of customization* Number of products

Processing characterization Processing description * Types of buer* Type of layout* Types of ¯ow

Assembly characterization * Types of assembly* Types of work organization

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ment of response time is central to the achievement of competitive advantage. In the extreme case where theenterprise maintains stocks of all purchased materialsand all ®nal products, RT equals DL. Essentiallyresponse time is a policy decision in¯uenced by techno-logical and operational constraints, marketing, andcustomers’ requirements and strategy.

We identify the following values for the parameter B of the classi®cation:

. B ˆ SL‡ PL‡ DL if the system produces by order;

. B ˆ DLa (P%) if the system produces for stock andthe service level is equal to P%;

. B ˆ DLb (P%) if the system does not produce (onlybuys, stocks, sells and delivers items) and the servicelevel is equal to P%;

. B ˆ PL‡ DL if the system produces to order butmaintains stocks of raw materials;

.

B ˆ SL‡ DL if the system does not produce but itsells to order.

3.2.1.3. Third dimension ( C) : repetitiveness In the report by APICS (1982), the term `repetitive-

ness’ is associated with the production volume of discreteitems: the larger the volume, the more repetitive theproduction system is considered to be. However, in anenvironment where the production volume is very lowdue to very large processing times, e.g. one item permonth, and the system only produces this item, then, inspite of the low production volume, clearly the system

must be considered to be repetitive. Repetitiveness musttherefore be considered to be a function of more variablesthan just production volume. The approach we adopt isto ®rstly de®ne what we mean by a repetitive productand then de®ne what we mean by repetitiveness of aproduction system. We de®ne a product to be repetitiveif it consumes a signi®cant percentage of the annualavailable time of the production unit (we specify at least5%). We de®ne a production system to be repetitive if atleast 75% of the items that it produces are repetitive ones.We de®ne a production system to be non-repetitive if at

least 75% of the items are non-repetitive and to bsemi-repetitive if at least 25% of the items are repetitivand at least 25% of the items are non-repetitiveUndoubtedly these cut-o points are somewhat arbitrary, but they do re¯ect our experience of real production systems. Using these de®nitions we identify a rangfrom maximum repetitiveness (the pure continuou

system) to the minimum repetitiveness (large-scalprojects) .

. C ˆ PC: pure continuous system, e.g. petroleumre®ning.

. C ˆ SC: semi-continuous system, e.g. each processing unit is a pure continuous system and therare combinations of routes through processing unitsIn the process industries these are sometimes knowas batch processing production systems.

. C ˆ MP: mass production system. Almost all itemare repetitive.

. C ˆ RP: repetitive production system. At least 75%of the items are repetitive. In the case of the metalmechanical parts sector, a typical RP productiosystem is the cellular manufacturing system wit¯ow-shop pattern of ¯ow.

. C ˆ SR : semi-repetitive production system. Therare a considerable number of both repetitive annon-repetitive items. In the case of the mechanicaparts sector, a typical SR production system is thcellular manufacturing system with job-shop pattern of ¯ow.

. C ˆ NR : non-repetitive production system. Th

majority (at least 75%) of items are not repetitive. C ˆ LP: large projects.

3.2.1.4. Fourth dimension ( D) : automation level The importance of the automation level for the contro

of production systems has been recognized for a lontime. Bright (1958) demonstrated that the nature of thcontrol has a close relationship with the levels of automation. We dierentiate the following states.

. N. Normal automation comprises all types o

mechanization where the human has a high degreof participation at the operational or executiolevel. Here we include classical ¯ow-shop and jobshops, cellular manufacturing systems with ¯owshop characteristics (CM1 ) ; cellular manufacturinsystems with job-shop characteristics (CM2 ) . ICM1 the ¯ow pattern is common and in CM 2 th¯ow pattern is variable, allowing stages to be misseout and allowing counter-¯ows (®gure 2).

. F. Flexible automation has, at the operational oexecution level, computer control taking the mai


Figure 1. Response time ( RT ) of a production system.

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role by means of technologies, e.g. local area net-works and computer numerical control, and willoften be accomplished by some form of FMS tech-nology. Here we distinguish between ¯ow-shop ¯ex-ible manufacturing system (FMS1) and job-shop¯exible manufacturing system (FMS2) .

. R. Rigid automation is the type found in transferlines with highly specialized and dedicated auto-matic equipment.

. M. Mixed automation occurs where the productionsystem has processing units with dierent automa-tion levels. For example it could be composed of amanufacturing cell with normal level of automationand a FMS (¯exible automation).

3.2.2. Product characterization

3.2.2.1. F ourth dimension ( E) : product descriptionWe identify three variables under the product descrip-

tion.

. Product structure: here we simply dierentiatebetween:SL: denoting single-level products requiring noassembly; andML: denoting multi-level products requiring ass-embly.

. Level of customization: we distinguish between the

following.(1) Customized products where the clients de®ne

all the parameters of the product design.(2) Semi-customized products where the clients

de®ne part of the product design.(3) `Mushroom’ customization. Mather (1998)

describes this concept as delaying product dif-ferentiation as late as possible in the productionsystem. There are a number of standard com-ponents or modules that are combined in alarge number ofways in the ®nal stages of the

production system with just a few additionaloperations.

(4) Standard products: the clients do not interferin the product design.

. Number of products: we distinguish between:S: for a single product; andM: for multiple products.

Thus, a product characterization of ML_2_Mdescribes a production system with multiple producthaving multi-levels and at least some customer-de®neproduct design parameters. The idea of a homogeneouproduct range is important when we come to applyingthe classi®cation. By this we mean that all products falinto the same product characterization. We have sevepossible sets of homogeneous products (SL_1, SL_2SL_4, ML_1, ML_2, ML_3, ML_4). The case SL_(single level and `mushroom’ customization) is not possible.

3.2.3. Processing characterization

3.2.3.1. Fifth dimension ( F) : processing descriptionThis important aspect of production systems is repre

sented by three variables.

. Types of layout: for this variable we identify thfollowing types of layout:

P: product layout;F: functional layout or layout by process;G: group layout;FP: ®xed position layout: the resources (humanequipment) move and not the product.

. Types of buer: for this variable we distinguisbetween the following types of buer:(1) buers before the ®rst production stage ;(2) intermediary buers between productiostages;(3) buers after the last production stage.

. Types of ¯ow: the third variable de®nes the follow

ing types of ¯ow:F1: single-stage, e.g. a machining centre;F2: single-stage with identical machines in paralleF3: single-stage with non-identical machines iparallel;F4: unidirectional multi-stage processing (e.g. classical ¯ow-shop system) ;F5: unidirectional multi-stage processing that allowstages to be skipped;F6: unidirectional multi-stage processing with equamachines in parallel;


Figure 2. Typical material ¯ow in the semi-repetitive cellularmanufacturing …CM2†.

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F7: unidirectional multi-stage processing with iden-tical machines in parallel but allowing stages to beskipped;F8: unidirectional multi-stage processing with non-identical machines in parallel;F9: unidirectional multi-stage processing with non-identical machines in parallel, allowing stages to be

skipped;F10: multi-directional multi-stage processing (e.g. aclassical job-shop) ;F11: multi-directional multi-stage processing withidentical machines in parallel;F12: multi-directional multi-stage processing non-identical machines in parallel.

Obviously F12 is the most complex type of ¯ow and aprevious types are particular cases of this one. A description, e.g. G_1-3_F5 is a processing facility with grou

layout and buers before the ®rst and after the last production stages with type of ¯ow F5 (unidirectional multistage processing that allows stages to be skipped).

3.2.4. Assembly characterization

3.2.4.1. Seventh dimension ( H)We distinguish between nine types of assembly.

. A1: Mixing ( e.g. chemical ingredients) .

. A2: Assembly of a large engineering project ( e.g. large bridge) typically in a layout of ®xed position

. A3: Assembly of heavy products (e.g. a large toolmachine) in a layout of ®xed position.

. A4: Assembly of light products (e.g. a medicaequipment) in one workstation or in one of a seof parallel workstations.

. A5: Paced assembly line where a conveyor nevestops and the workers may move to perform theitasks.

. A6: Paced assembly line where a conveyor stopfor a number of units of time (cycle time) and th

workers remain ®xed at their individual workstations.

. A7: Semi-paced assembly line where a conveyoalways moves and the worker releases the produconly when he ®nishes his tasks.

. A8: Unpaced assembly line where a conveyoalways moves and the worker simply attaches thproduct to the conveyor when he ®nishes his tasks

. A9 : Unpaced assembly line where a transporteonly moves when a worker activates it after ®nishinhis tasks (e.g. an overhead travelling crane).


Figure 4. Uni-directional, multi-stage processing with identicalparallel machines.

Figure 3. Single-stage processing, the general case.

Figure 5. Uni-directional, multi-stage processing with non-identical parallel machines.

Figure 6. Multi-directional, multi-stage processing with nonidentical parallel machines.

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3.2.4.2. Eighth dimension ( J) : type of organization of the work For this dimension we adopt a classi®cation of the

organization of the work based on Johnson (1991). Ourclassi®cation is the ®rst to encompass this aspect. Workorganization may also be relevant to other types of pro-cesses, but from a production control perspective it hasmuch greater impact in assembly operations. We distin-

guish the following ®ve types.

. ( I ) Individual work: the number of workers is equalto the number of workstations. In the case of as-sembly lines, the criterion for allocating each taskto a workstation is the balancing of the whole line.Two speci®c categories can be identi®ed.(Ia) Without rotation. Each worker is ®xed on a

workstation.(Ib) With rotation. After each task has been allo-

cated to a workstation, the ®rst set of workersof the ®rst matched set of workstations form

the ®rst team and the second set of workersform the second team, etc. Workers in thesame team may rotate their workstations.

. (T) Team work. T he workstations ( or sublines) arepre-de®ned and each one is operated by a singleteam with several workers. The tasks carried outby each worker of the teams are decided by balanc-ing of the subline. Two speci®c cases can be identi-®ed.(T a) Every task is assigned to a speci®c work-

station.(Tb) Just some tasks are assigned to a speci®c work-

station.. (G) Self-managed work groups. As in the team work

cases the workstations are ®rst de®ned and then thetasks to be performed by each workstation, but thegroup of workers of each workstation has autonomyto organize the work inside the group.

4. Appraisal and application of the multi-

dimensional classication system

4.1. Relationships between the dimensions in the classicationscheme

Certain groups of characteristics occur quite com-monly in production systems. Here we illustrate sometypical relationships between the general characteriza-tion dimensions we have identi®ed. For instance, thenumbers of employees, level of automation and size of the production system tend to be correlated. The size of the production system is closely related to important fea-tures of the production system, e.g. the amount of capitalthat can be invested in a PPCS. Also in real production

systems there is a correspondence between the volume oproduction per product and variety of products, size othe production system and level of repetitiveness of thproduction system.

The relationship between volume of production Q anvariety of products P is well known. For high Q and low

P a product layout is appropriate, for medium Q anmedium P a group layout is appropriate, and for low Qand high P a functional layout is appropriate. The repetitiveness level essentially combines P and Q in one variable. This is illustrated in ®gure 7 relating repetitiveneslevel and automation level. Here we plot the typical leveof repetitiveness against the level of automation for somimportant modern manufacturing systems. The level orepetitiveness increases from job-shop, to CM2, CM1 and¯ow-shop systems. Naturally FMS1 has greater repetitiveness than FMS2. Transfer lines have the greatest repetitiveness level. What is uncertain is the position of lines

and II; this depends very much on the technology ancontrol system employed in the FMS.

The response time, level of customization and the wawe apply our classi®cation enables us to treat the relationship between the production system and the customers in a deeper way than in traditional textbooclassi®cations, e.g. Ingham (1971) and Wild (1995)The response time and the level of customization describthe relationship between the production system and thmarket, and it is important therefore that literaturrelated to PPC systems speci®es this relationship.


Figure 7. The level of automation and the level of repetitivenes

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4.2. Using the classication to choose the overall structure of a PPCS

All the 12 variables considered in our multi-dimen-sional classi®cation have direct impact on the complexityof the production planning and control (PPC) activities.Table 2 indicates the typical impact of each variable on

the complexity of PPC activities and the relationshipbetween each variable and the level of repetitiveness.Repetitiveness is an important variable in our classi®ca-tion and we believe it to be the key variable for choosingthe overall structure of the PPCS. Table 2 also indicatesthe relationship between the variables and the choice of aPPCS. The last line of table 2 is justi®ed by the followingreasoning: for discrete items the more repetitive the pro-duction system is the more likely that the simplest of allPPCSÐthe kanbanÐcan be chosen; for intermediatesituations period batch control (PBC; Burbidge 1996) islikely to be appropriate and for non-repetitive situations

an MRP-based approach is likely to be necessary.Kanban and PBC due to their logic and procedures do

not work well in non-repetitive situations. For some casethere is the possibility to choose OPT type systems. Folarge projects, PERT/CPM may be the most appropriatchoice.

While the level of repetitiveness has a strong impact onthe choice of basic PPCS, the other variables have signi®cant impact on the complexity of the detailed system

to be de®ned. For example, MRP may be chosen as thbasic system but the parametrization of the systemdepends on the complexity of the PC activities. Thesare related to the variables in table 2 and also the constraints that are restricting the production system.

4.3. Applying the scheme

Table 3 gives a complete view of the attributes of thmulti-dimensional classi®cation scheme.

If the production system is composed of one set o

homogenous products and one processing unit and/oone assembly unit, the application of the multidimen


Table 2. The variables and the choice of a PPC system.

Level of repetitiveness of production systems

Pure Semi Mass Semi- Non- LargeOther var iable s c ontinuous con tinuous production R epetitive repetitive repetitive p rojec ts

Enterprise size For all levels of repetitiveness, the larger the enterprise the greater the complexity of production planning ancontrol (PPC) activities

R esponse time DL( a7P%) DL( a7P%) DL( a7P%) DL( a7P%) PL‡ DL PL‡DL or SL‡PL‡DL

SL‡ PL‡DLAutomation level Rigid Rigid Rigid Normal or Normal or Normal or Normal¯exible ¯exible ¯exible

Product structure For all level of repetitiveness, the PPC activities for multi-level products are much more complex than for singlelevel products

Level of Standard Standard Standard Standard Mushroom Semi- Customizedcustomization products or mushroom or mushroom or mushroom or semi- customized

customized or customized

Number of products For all levels of repetitiveness, the PPC activities for multi-products are much m ore complex than for single-product

Types of layout Product Product Product Group Group Functional Fixedlayout layout layout layout layout layout position

Types of buer (i) and ( iii) ( i) , ( ii) ( i) , ( ii) ( i) , ( ii) ( i) , ( ii) ( i), ( ii) Without

and ( iii) and ( iii) and ( iii) or ( i) or (ii) buersTypes of ¯ow The complexity of PPC activities increases from (F1) in direction to (F12)T ypes of assembly ( A1 ) or ( A1) or ( A5) or ( A6 ) ( A5) or ( A6 ) ( A7) or ( A8) ( A3 ) or ( A4) ( A2)

disassembly disassembly or ( A7) or or ( A7) or or ( A9) or or nono assembly no assembly no assembly assembly

T ypes of work If there is assembly, the type of work organization hasorganization a direct impact on the wa y you will balance t he work

in the assemblyBasic production A spread- A spread- Kanban Kanban or PBC or MRP PERT/ control system sheet to sheet to PBC OPT CPMpossible to be cont rol the schedulechosen rate of ̄ ow the work

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sional classi®cation is straightforward. Otherwise, wemust consider the following general approach (®gure 8).

We have to make the following explicit.

(1) The number ni of products in each case i …n1 ˆjSL_1j ; . . . ; n7 ˆ jML_4j; where the modulus sym-bol indicates the number of elements in the set).

(2) If the level of automation is mixed (dimensioB ˆ M) then we have to specify for each processing unit the level of automation (ALi ) another descriptors ( type of layout, type of bueand type of ¯ow).

(3) An assembly characterization ( dimensions G anH) for each assembly unit …1; . . . ; n†.


Table 3. Summary of multi-dimension classi®cation system ( MDCS).

General characterization Processing characterization

(A) * Enterprise size ( F) * Processing description(L) : large number of employees * Types of layout(M) : medium number of employees ( S) : single workstation(S) : small number of employees ( P) : product layout

(B) * Response time ( F) : functional layout (layout by process)

…SL‡ PL‡ DL† if the system produces by order ( G) : group layout…DL…a ¡ P%†† if the system produces for stock and (FP) : layout of ®xed positionthe service level is equal to P% * Types of buer

…DL…b ¡ P%†† if the system does not produce (only buy, (1) : buer before the ®rst production stagestock, deliver and sell items) and the services level is (2) : buers between intermediary stagesequal to P% ( 3) : buer after the last product stage

…PL‡ DL† if the system produces by order but maintains * Types of ¯owstocks of raw materials ( F1) : mono-stage

…SL‡ DL† the system does not produce and attends the (F2) : mono-stage with equal machines in parallelclients by order ( F3) : mono-stage with unequal machines in paralle

(C) * Repetitiveness level ( F4) : uni-directional multi-stages…PC† ! pure continuous system ( F5) : variable unidirectional multi-stages…SC† ! semi-continuous system ( F6) : uni-directional multi-stages with equal…MP†!mass production system machines in parallel

…RP† ! repetitive produ ction system ( F7) : variable uni-directional multi-stages with…SR† ! semi-repetitive production system equal machines in parallel…NR†! non-repetitive production system (F8 ) : uni-directional multi-stages with unequal…LP† ! large project machines in parallel

(D) * Automation level ( F9) : variable uni-directional multi-stages with(N) : normal automation unequal machines in parallel(F) : ̄ exible automation ( F10) : multi-directional multi-stages(R) : rigid automation ( F11) : multi-directional multi-stages with equal(M) : mix automation machines in parallel

( F12) : multi-directional multi-stages with unequalmachines in parallel

Product characterization Assembly characterization

(E) * Product description ( G) * Types of assembly* Product structure ( A1) : mixture of chemical ingredients

(SL) : single-level products ( A2) : assembly of a large project( ML) : multi-level products ( A3) : assembly of heavy products in a layout of

* Level of customization ®xed position(1) : customized products ( A4) : assembly of light products in one or in(2) : semi-customized products parallel workstations(3) : mushroom ( A5) : paced assembly line where the conveyor(4) : standard products never stops

* Number of products ( A6) : paced assembly line where the conveyor stop(S) : single-products for C (cycle time) units of time(M) : multi-products ( A7) : semi-paced assembly line

( A8) : unpaced assembly line I( A9) : unpaced assembly line II

( H) * Types of work organization

( I) : individual workers(T ) : work teams( G) : work groups

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(4) Depending on the complexity of the productionsystem, we may need to show the relationship

between the processing units and buers. A direc-ted graph may be an appropriate approach.

Here we ®rst present a concise illustrative example andthen apply the approach to four examples from theliterature.

A production system manufactures machines utilizedin the construction of roads. There are 2000 employees.There are processing units with ¯exible levels of automa-tion and others with normal levels of automation. Thesystem produces to order but maintains stocks of rawmaterials. It is a semi-repetitive production system. The

products are complex, standard, multilevel ones. Thereare ®ve processing units. The ®rst one has a functionallayout with buers between the intermediate stages,before the ®rst and after the last stage, a normal levelof automation and a multi-directional multi-stage ¯owpattern with non-identical machines in parallel. The sec-ond and third processing units are manufacturing cellswith normal levels of automation. The second one has abuer before the ®rst stage and a uni-directional ¯owpattern with identical machines in parallel. The thirdone has a buer after the last stage and a unidirectional

¯ow-shop ¯ow pattern. The fourth and ®fth processinunits are ¯exible manufacturing systems with job-shop

type ¯ow patterns and buer after the last stage. Theris one assembly unit of the unpaced type where the conveyor always moves and the worker only attaches thproduct to the conveyor when he ®nishes his tasks withnormal level of automation operated by work groupsFigure 9 shows the classi®cation for this productiosystem and ®gure 10 uses a directed graph to show threlationship between the units and the buers.

We have also applied the approach to four productionsystems described in recent literature in production planning and control. Obviously the amount of detail given ineach source is limited and variable, and the results ar

necessarily incomplete, but it was felt to be an objectivtest of the applicability of the approach. Table 4 summarizes the major characteristics of each of the systemfrom the information provided in the source.

Artiba (1994) gives more detail for the processing characteristicsÐa production system composed of six processing units all with ¯ow pattern (F4) and thtransport of the materials between adjacent workstationis made through pipes. The relationships among the siunits are shown in ®gure 11 [e.g. the products that pasprocessing unit 1 (PU1) follow through to PU4 or PU5]


Figure 8. The general approach to apply the multi-dimensional classi®cation.

Figure 9. The classi®cation for the example problem.

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5. Conclusions

Dierent classi®cations and taxonomies are necessaryfor dierent purposes as dierent perspectives on manu-facturing systems are important for dierent aspects of analysis and design. However, many previous classi®ca-tions have limited application: some are not explicitabout the purpose of the proposed classi®cation and/or

are very super®cial whilst others disregard important elements. The lack of useful classi®cations is one reason thawe believe underlies the lack of progress in operationmanagement generally and production planning ancontrol in particular.

The classi®cation scheme described here can be successfully applied to real production systems because ican treat in-depth the typical hybrid cases that arise in


Figure 10. The relationship between units and bu ers.

Figure 11. The relationships among the six units (based on Artiba 1994).

Table 4. Application of the MDCS to some literature examples.

References

Characterization Variables [1] [2] [3] [4]

General * Enterprise size * * * ** Response time * * * ** Repetitiveness level NR MP MP SC* Automation level F R R R

Product Product description:* Product structure ML ML ML ML* Level of customization 1 3 4 3

* Number of products M M M MProcessing Proce ssing discription :* Types of layout F S P P* Types of buers 2 1 ± 3 2 1 ± 3* Types of ¯ow F11 F1 F4 **

Assembly * Types of assembly A4 A4 A5 A1* Types of organization of work Ð Ð Ð 1

Notes: A trace …¡† means that the descriptor considered does not apply; an asterisk …¤† means that the information was omitted; and a double asteris…¤¤† means that additional description will be provided on the information cannot be put into a simple table.References:[1] Anwar, M. F. and Nagi, R. , 1998, Integrated scheduling of material handling and manufacturing activities for just-in-time production of compleassemblies. International Journal of Production Research , 36, 653 ± 681.[2] Maimon, O. Z. and Braha, D., 1998, A genetic algorithm approach to scheduling PCBs on a single machine. International Journal of ProductioResearch , 36, 761 ± 784.

[3] Dowlatshahi, S., 1996, An ecient solution for determining location and size of buer stocks. Production Planning & Control , 7, 282 ± 291.[4] Artiba, A., 1994, A rule-based planning system for parallel multiproduct manufacturing lines. Production Planning & Control , 5, 349 ± 359.

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practice. Each speci®c production system demands aspeci®c, if not unique, production control system. Forexample, under the general heading of assembly lines,there are in reality, several dierent types, each of which requires a dierent production control system.Our classi®cation highlights the essential characteristicsthat enable a better understanding of production systems

and their relationship with the production planning andcontrol function. In future research, we intend to demon-strate further its utility for aiding in the selection ordesign of appropriate production planning and controlsystems. More generally it may be used for research,particularly in studying the gap between practice andtheory in the production planning and control area. Itmay also be used for teaching in operations managementand industrial engineering. We see it being used tohelp students in classifying systems from detaileddescriptions and in classifying production systems studiedin the ®eld.

Acknowledgement

The authors wish to acknowledge the support of theFAPESP in funding this work.

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