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layout organization that has been used to meet this challenge. CM is aphilosophy that attempts to recognize and exploit similarities amongcomponents. Components are grouped into families based on similarity inshapes or production processes or both. Machines are then grouped into cells toproduce these components. The advantage is that the time required for the set-up changes between components that are similar is shorter which reducescomponent flow times, lowers work-in-process inventories, and increasesproductivity for machines. The major disadvantage of this scheme is that itdoes not quite meet the competitive need for flexibility.

This paper compares a CM layout to a much discussed but seldomresearched layout focused cellular manufacturing (FCM). We define FCM asa layout scheme that groups components by end-items and forms cells ofmachines to fabricate and assemble end-items. It is not classified as a CMlayout since it does not attempt to take advantage of process similarities so as

to reduce set-up times (Wemmerlov and Hyer, 1987). It also is not classified as aflow shop since there are no machines dedicated to individual operations andthe machines are not arranged in a series.

FCMs major advantages are its ability to reduce completion times forassembled end-items and work-in-process inventories while maintaining somedegree of flexibility. Another advantage is that it would be easy to install in afirm that has a few end-items produced in large annual volumes and many end-items produced in small volumes. Such a firm might opt for a mixture of shoplayouts (i.e. process and batch). Many firms have such a combination (Flynn and

Jacobs, 1987). Installing a single focused cell for a few end-items would be morepractical than installing the many required cells for a cellular manufacturing

layout. The focused cellular schemes major disadvantage is in load imbalancesthat may develop as different product markets grow at different rates.

The remainder of this paper is divided into the following sections: the secondsection provides a literature review, the third section describes the experimentand the simulation models used in this study, the fourth section provides theresults from the simulation runs, and the last section presents the conclusions.

Literature reviewSkinner (1974) suggested that some batch processing factories would benefit ifthey became more focused. He explained that this could be done by grouping

various products and resources into several manufacturing units with each unitfocusing on a limited, concise, and manageable set of products, technologies,volumes, or markets. Hill (1989) furthered this discussion by providing a moregeneral framework for defining focus. He suggests that a facilitys focus can bebased on one of three criteria: process, product line or order winners.

Sheu and Krajewski (1990) researched a batch processing environmentwhich focused on Hills order winning criterion. They developed a methodologyfor grouping product and machines into cells such that the engineering

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considerations of process similarities and the strategic considerations ofhomogeneous manufacturing tasks are recognized. They found that therecognition of process similarities as well as strategic considerations isimportant in the development of focused manufacturing facilities.

Schonberger (1982) presents a convincing argument for organizing batchprocessing plants by end-items, however, very few researchers have studied thislayout. Several authors have reported on different factories which have adoptedFCM (Wemmerlov and Hyer, 1989; Dumolien and Santen, 1983; Smith, 1987).These authors mention many benefits of this layout scheme which confirm itspractical importance. In addition, Dale and Dewhurst (1984) studied this layoutscheme through a simulation study investigating different aspects of schedulingand sequencing of products through focused cells. They, however, did notcompare this layout scheme to other schemes nor did they discuss howcomponents were grouped into families or how machines were grouped into cells.

Research on batch processors that focus on process have recently concentratedon CM (Askin and Selim, 1997; Cheng and Kumar, 1995; Daniels and Burns, 1997;Kannan and Ghosh, 1996a; Marshet al., 1997; Reismanet al., 1997; Seifoddini andManoocher, 1996; Shafer and Meredith, 1990, 1993; Shambu and Suresh, 1996;Suresh, 1992; Vakaharia and Kaku, 1994). A number of researchers havecompared JS layouts to CM layouts (Shafer and Meredith, 1990, 1993; Agarwaland Sarkis, 1998; Farrington and Nazemetz, 1996; Garza and Smunt, 1991;Kannan and Ghosh, 1996b; Morris and Tersine, 1990, 1994; Shambu and Suresh,2000) and have generally concluded that manufacturing with a CM layoutperforms better than JS layouts. In addition, several surveys of firms that haveadopted CM have been conducted to investigate this schemes performance

(Burbidge, 1979; Wemmerlov and Hyer, 1989; Wemmerlov and Johnson, 1997).These surveys report reduction in set-up times, reduction in flow times, reductionin work-in-process inventories, reduction in material handling, and improvedquality. These benefits highlight the practical importance of this layout scheme.Agarwal and Sarkis (1998) provide an analysis and review of comparative studieson JS and CM layouts based on: simulation, empirical and analytical studies. Theyconclude that the findings from these studies are not consistent.

Wemmerlov and Hyer (1989, p. 421) recognized the need to research FCMfurther by stating:

It would be of great interest to study the relative advantages/disadvantages of cell systemsbased on component similarities versus those based on product line affiliation . . . An

expected benefit would be the ability to react quickly to market demand changes.

We have conducted an experiment which makes this comparison by simulatinga batch processing plant laid-out in a CM organization versus a plant laid-out ina focused cellular organization. An assumption of the experiment is that thefirm being modeled needs to be responsive to customers and has access tospecialized resources and expertise by either similarity in process or by productline.

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Experimental designSimulation experiments are conducted to compare the performance of CM andFCM layouts. The simulation experiments included six independent variables(the shop layout organization, the demand distribution, batch size, set-up timemagnitude, product structures and priority dispatching rule) and threedependent variables (average component flow time, average end-itemcompletion time and average work-in-process inventory).

Independent variablesA number of shop operational parameters are considered as independentvariables. Changes in the level of these variables are expected to affect thebehavior of the layouts and hence their performances. Therefore, rather thanholding these parameters constant, they are varied in order to analyze theperformance of the two layouts under different scenarios. The first independent

variable is the layout organization of the shop (i.e. CM or FCM). The FCMlayout first grouped components that belong to a set of end-items into familiesand then assigned machines to cells to produce and assemble these end-items.On the other hand, cellular manufacturing layout first grouped components bysimilarities in their processes and then assigned machines to cells so that thisfamily of components could be produced by the cell.

Demand distributions.A major benefit of organizing a factory based on aFCM layout is the ability to respond to different demand patterns and the speedwith which products are completed. Demand for products with high or lownumbers of components (parts) is increased or decreased, and this is expectedto produce different results in the CM and FCM layouts. Three differentdemand distributions are investigated. Their differences lie in the frequencywith which each of the product types is being ordered. Products differ mainlyon the number of parts from which they are assembled. Products with high andlow numbers of parts per product are ordered at different frequencies in each ofthe three demand distributions.

One of the claimed disadvantages of FCM is that end-item markets may grow atdifferent rates, resulting in load imbalances (Albino and Garavelli, 1999; Vakahariaand Kaku, 1994). Therefore, end-item demand distributions are included as anindependent variable. In this study, we considered five end-items with the numberof components being three, four or five. Table I shows the three levels of this factor

with the end-items and number of components for each end-item.The first distribution, equal products distribution (EPD), assumes that everyproduct has the same probability of being ordered. That is, each of the fiveproducts, regardless of the number of components it is made of, has a 20percent probability of being ordered every time an order is received. Thesecond type of demand distribution gives products with more componentshigher probabilities of being ordered than those with fewer components. Forexample, product number 1, which has five parts, has a probability of 30

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percent of being ordered. However, products with fewer components, such asproduct number 2, are ordered less frequently (10 percent). This demanddistribution will be referred to as large products distribution (LPD). The final

type of demand distribution allows products with fewer components to beordered more frequently than those with more components. This distribution isreferred to as small product distribution (SPD).

Batch size. Batch size is the number of parts that are grouped together andprocessed thereafter in a group or batch. The batch size is an important factorand directly influences the average completion time of parts (Shafer andCharnes, 1993). That is, at an extreme batch size of one, parts are routed throughthe shop individually and exit as soon as all of their operations are performed.This extreme case would obviously result in a short average completion time forall parts. The way batching was done was different for the two layout

organizations.In the CM layout, components are grouped based on the degree of processing

similarity. Therefore, parts that may belong to the same or different productswould be eligible to join in a family of parts. Once this family reached a pre-setbatch size, it was released to the appropriate cell for processing. In the FCMlayout all components of an end-item were grouped into one family. The batchsize in this case was the number of end-items released to the system.

Batch size is an independent variable in the experiments and Table II showsthe three levels of this factor. The number of components in each batch size waskept equal for both layout schemes to allow similarity in operating conditionsfor the simulation experiments.

End-item Number of components SPD EPD LPD

1 5 0.1a 0.2 0.3

2 3 0.3 0.2 0.13 4 0.2 0.2 0.24 3 0.3 0.2 0.15 5 0.1 0.2 0.3

Notes: a The numbers are the probability of an order being for particular end-itemSPD: small product distributionEPD: equal product distributionLPD: large product distribution

Table I.End-item demand

distribution

Batch sizeCM model FCM model

components End-items Components

Small 4 1 4Medium 8 2 8Large 16 4 16

Table II.Batch sizes


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Since the average number of parts per product is four, as shown in Table III,batch sizes are made up of multiples of four: four, eight and 16 parts for the CMlayout. Table III lists the different product types, the number of parts perproduct and average number of operations per part for each product. Eachlayout processes all components of the five different products. Consistent withother research studies, the number of parts per product ranges between threeand five, values within the lower range reported by Wemmerlov and Hyer(1989) in their survey of cellular shop practices. Krajewski et al. (1987)simulated products with an average of 4.5 components per parent, for end-items and of two, for intermediate items. Flynn and Jacobs (1987) simulated anactual shop which had two to five parts per product.

Set-up time. The processing time is the time required to perform a singleoperation on a single part on a machine. The processing time in this experimentis randomly generated from a uniform distribution over an interval between

four and eight hours which is 0.5 to 1.0 days, where each day is eight workinghours long. The set-up time is the time required to prepare a machine to startprocessing a part. The processing and set-up times for each operation on eachpart were pre-set before the start of the simulation runs. The same randomizedvalues were fixed in both of the layouts. This was necessary to avoidinfluencing the results by processing time differences. Set-up time is animportant factor in the CM and FCM layouts, and is used in several recentstudies comparing JS and CM layouts (i.e. Farrington and Nazemetz, 1996;Shambu and Suresh, 2000). We evaluated the effect of set-up time on the resultsat two levels: small and large. The small set-up time per part was chosen torange between 0.2 and 0.4 days and large set-up time ranged between 0.6 and1.2 days per part per operation. The set-up time magnitudes, ratio of set-time toprocessing time (which averaged 40 percent for the small setting), and thedifference between small and large set-up times were established according toprevious research settings or survey findings (Flynn and Jacobs, 1987;Krajewskiet al., 1987; Wemmerlov and Hyer, 1989).

Product structure. Product structure refers to the relationship betweencomponents and the end-item. Product structure is another independentvariable used in the simulation experiments, where the effect of differentproduct structures on both layouts is investigated. Two sets of products are

Product type Number of parts per product Average number operations per part

1 5 2.802 3 3.003 4 2.754 3 3.005 5 2.60Average 4.0 2.80

Table III.Product types

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being processed in both layouts. The first set is a simple, one-level product. Theother set is where products have a multi-level product structure. The number oflevels for the latter type ranges from two to four. Figure 1 shows the productstructures for the two levels.

The number of levels selected in this research is consistent with thatused by Krajewski et al. (1987). They observed that an average of three

Figure 1.Product structure


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BOM levels per end-item (with a range from two to four) reflects USindustrial practice.

Priority dispatching rules. Parts accumulate in queues waiting formachines to be available for processing. When a machine becomes idle andthere is more than one part waiting in its queue, the dispatching rule selects thenext part for processing. The last independent variable is the prioritydispatching rule which was used to determine which component, of thosewaiting in the machines queue, to process next. It was felt that this rule mightinfluence end-item completion times and was therefore included as anindependent variable. Two rules were tested first-in-first-out (FIFO) andfirst-in-system-first-out (FISFO). The FIFO rule has been consistently used inall simulation studies that compare JSs to CM models (Flynn and Jacobs, 1987;Morris and Tersine, 1990, 1994; Garza and Smunt, 1991; Shafer and Charnes,1993; Shambu and Suresh, 2000). The FISFO rule gives the part with the

earliest system entering time when it entered the system the highest priority tobe processed when the machine becomes available.

Dependent variablesThere are a number of dependent variables that vary as the independentvariables change. Any of these dependent variables could be used as aperformance measure to compare the two layouts being simulated in theexperiments. Some of the most common and traditional measures that havebeen used in previous similar studies (Farrington and Nazemetz, 1996; Flynnand Jacobs, 1987; Garza and Smunt, 1991; Shafer and Charnes, 1993; Shafer andMeredith, 1993; Shambu and Suresh, 2000) are used in this study. Marketing

and inventory managers are usually interested in the mean completion time ofend-items which includes processing, batching and assemble times. Ourresearch includes criteria with all three of these factors. We feel this givesinsights into the efficiency and performance of the entire process.

The three dependent variables used in this experiment are averagecomponent flow time (ACFT), average end-item completion time (AEICT), andaverage work-in-process inventory (AWIP). The average componentcompletion time included the time between the components batch releaseuntil all components of the batch were completed. The average end-itemcompletion time included the time it took between order release and end-itemassemble. The AWIP is the average number of components and subassemblies

waiting to be batched, processed, rebatched or assembled.

SimulationIn todays competitive manufacturing environment simulation has become oneof the most powerful resources to managers for designing manufacturingsystems (Farhadi, 1994). Simulation enables those systems to work before theyare build (Koelsch, 1992). Simulation has been used in most of the studiescomparing JS and CM layouts (i.e. Albino and Garavelli, 1999; Shafer and

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Charnes, 1993,1995; Shafer and Meredith, 1993; Shambu and Suresh, 2000). Inthis study, we conducted simulation experiments to compare the performanceof CM and FCM layouts. Thus far, we have discussed a total of six independentvariables which affect the performance of the layouts simulated in theexperiments. These variables are also commonly used performance measuresestimated by simulation (Shafer and Meredith, 1993, Law and McComas 1998,1999). The effect of each of the independent variables needs to be individuallyinvestigated under different operating scenarios. Each variable has two orthree levels to be tested, as discussed earlier and summarized in Table IV.

Simulation languages have become easier to use and apply in manufacturingenvironments (Law and McComas, 1992). Examples of general-purposesimulation languages include Arena, AweSim, EXTEND, GASP, GPSS/H,Micro Saint, MODSIM III, Q-GERT, SIMPLE++, SIMSCRIPT II, SIMUL8,SLAM II and Taylor Enterprise Dynamics Developer (Diamond, 1989; Ekere

and Hannam, 1989; Law and McComas, 1999). With EXTEND, it is possible tocreate a block diagram of a process where each block describes one part of theprocess. The simulation model used in this research was designed to representthe general practice used by past researchers.

Interarrival rates for ordersThe average number of components per end-item that were generated at eachinterval of time was dependent upon the end-item demand distribution. Tomaintain comparable shop capacity levels for all simulation models theinterarrival rates were changed to overcome changes in the demanddistributions. The arrival rates were drawn from an exponential distribution

with pre-set mean interarrival rates.The average number of parts per product that is generated at each intervalof time is different from one model to another, depending on the demanddistribution used, which is shown in Table V.

For example, when each product has an equal probability of being ordered(EPD), the average number of parts per product is four. When products with alarger number of parts have a higher probability of being ordered (LPD), theaverage number of parts is 4.4 per product. The third case is when productswith a smaller number of parts have a higher probability of being ordered(SPD); the average number of parts per product is then 3.6.

Number Independent variable Number of levels Levels

1 Shop layout 2 CM, FCM

2 Demand distribution 3 SPD, EPD, LPD

3 Batch size 3 Small, medium, large

4 Set-up time 2 Small, large

5 Product structure 2 Single level, multi-level

6 Priority rule 2 FIFO, FISFO

Table IV.Independent

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Simulation runsEach simulation run accounted for 750 days. The initial 250 days for each runwas found to stabilize the system and data was collected during the remaining500 days. Each data collection period was broken into ten 50 day subperiodswhich accounted for ten replications per simulation run.

If a full factorial experiment (all possible combinations) is designed, the numberof runs needed would be 144 (2(3 3 2 2 2)). Each run would provide anumber of performance measures. When the number of runs is multiplied by thenumber of performance criteria, the resulting information becomes prohibitivelylarge. Moreover, it would be difficult to comprehend how each systems behaviorwould compare to the other one under different operating environments. Inprevious research, such complex situations are avoided by limiting the number ofindependent variables and/or reducing the number of levels.

To achieve both the inclusion of all variables and their levels and produce a

reasonable set of results, pilot studies were first run to produce somepreliminary results. These preliminary results helped to define a two-phasedexperiment so as to achieve many insights on the performance of layouts withthe changes in the independent variables. The independent variables aregrouped in sets and are run together. This will reduce the number of runs,while providing the needed information.

In the first phase, the two layouts are run using the three demanddistributions and all levels of batch sizes. This produced 18 runs (2(3 3)). Inthese runs, set-up time is set at the small level, product structure is fixed at asingle level and the priority rule used is FIFO.

In phase two, the other three independent variables (set-up time, product

structure and priority rule) are varied individually in different sets, while thedemand distribution and batch size are held constant at EPD and mediumbatch, respectively. This resulted in 16 additional runs (2(2 2 2)).

VerificationThe verification process included de-bugging simulation programs from anytopographic or logical mistakes. Models were tested as a whole and as

Product typeParts SPD EPD LPD

N p p*N p p*N p p*N

1 5 0.1 0.5 0.2 1.0 0.3 1.52 3 0.3 0.9 0.2 0.6 0.1 0.33 4 0.2 0.8 0.2 0.8 0.2 0.84 3 0.3 0.9 0.2 0.6 0.1 0.35 5 0.1 0.5 0.2 1.0 0.3 1.5

Average part/product 4 3.6 4.0 4.4Interarrival 1.1 1.0 0.9

Table V.Average numberof parts

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subsections. For example, a cell was completely evaluated individually fromthe arrival of a batch until its departure. The use of the priority rule in amachines queue was verified by monitoring the entering and exiting parts overtime. Check points were positioned at every stage of the process to monitor andreport on all parts passing through the system. The above inspection processwas used to verify the condition of both layout models. Through de-bugging,refining and redesigning, both models were completely verified.

The random number generator used in the simulation program was alsotested. The Kolmogorov-Smirnov goodness-of-fit test was applied, and the nullhypothesis (no difference between the sample frequency and the theoreticaluniform distribution) was not rejected for any sample tested. Four other testswere also applied: number of runs, number of runs above and below the mean,length of runs and length of runs above and below the mean. In no case was thenull hypothesis rejected.

The performance measure values collected over every period must beindependent in order for them to be statistically reliable. The batch meansmethod (Fishman, 1978) is used to test the independence between each adjacentbatch. A batch here is a period of time over which statistics are collected.Independence of batches refers to the degree of correlation between thecriterion values of adjacent periods.

ValidationValidation is the process of establishing that desired accuracy orcorrespondence exists between the simulation model and the real systembeing simulated. Deterministic values are used as input to every layout model

in order to test the validity of the results. For example, in the stage of randomlygenerating product orders, a specific number and type of orders was generatedat a specific time interval. This ensures that these products are the only inputsand the only expected outputs of the system. Output results are validated bycomparing them to independently manually calculated results. This meansthat, not only is the model being executed as intended, but also the systemresults correspond to the logically and intuitively calculated results.

The correspondence between different results of a specific run was alsocompared to verify their validity. It was established that there is a linearrelationship between the average completion time and the work-in-processlevel.

Results and analysisAn inspection of all data produced in all of the simulation runs revealed noapparent unjustifiable irregularities. The mean batch method was applied to allmajor performance values. At a 5 percent level of significance, the correlationcoefficients were not statistically significantly different from 0. Thisindependence of observations makes the data statistically reliable. A


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graphical method (Tukey, 1977) was used to test the normality of the data. Nogross violation of normality was detected in any performance measure.

Comparison of layoutsSimilar to the methodology used by Shafer and Charnes (1993) whencomparing JS to CM-based layouts, a method pairs t-test is used to test fordifferences between the various levels of the independent variables. Thismethod was also used in a similar study by Penlesky et al. (1989). First, wedetermined the differences between observations in each layout. To determinethe t-value, the sample mean differences, and the standard deviation of thesample mean paired differences are calculated. Table VI shows the meandifferences, percent change, t-value, and corresponding probability for eachmanufacturing scenario. The percent change is determined as follows:

Percent change Value of CM layout Value of FCM layout

Value of CM layout

100:

Information from this analysis is the basis for determining whether there is adifference in the performance of the layouts and, when found, in whichdirection. Calculations shown in Table VI were performed for each layout forall three performance criteria.

Demand distribution and batch size analysisThe independent variables manipulated in the first 18 experiments are demanddistributions and batch sizes. Our objective is to compare the performance ofFCM-based layouts to CM-based layouts. FCM concept is introduced in order to

improve the performance of manufacturing systems, and the percentageimprovement of FCM over CM, if any, is an important finding in this research.

Average component flow time CM FCM D(CM 2 FCM)

1 6.73 6.45 0.282 7.66 6.94 0.723 7.16 7.85 20.694 7.4 5.49 1.915 7.44 7.4 0.046 6.95 6.57 0.387 7.59 6.08 1.518 6.88 7.17

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9 7.5 7.55 20.0510 7.03 6.68 0.35Mean 7.23 6.82 0.42% change 3Standard deviation 0.79t-value 1.66

p 0.070

Table VI.Pair-wisecomparisons

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Average component flow time. Average component flow time (ACFT) is thetime it takes a part to be processed, beginning with the time at which the batchcontaining the part is released to the system and ending at the time when allparts comprising the parent batch are finished. This includes four majorcomponents:

(1) waiting in machine queues (if any);

(2) set-up time;

(3) operation processing; and

(4) waiting for other parts of the same batch to finish processing.

An examination of Table VII reveals that the ACFT is significantly differentfor both layouts and for most of the manufacturing scenarios compared. Theresults show that, in the EPD case, there is no significant difference in ACFT

for all operating scenarios corresponding to small batch sizes. Furthermore, theprobability that there is no difference between ACFT for CM and FCM modelsis 7 percent when the batch size is medium and demand is based on EPD. Thisprobability, when compared with a 5 percent level of significance, indicatesthat there is no significant difference between the two layouts. A similar resultcan be seen when the batch size is medium and demand is based on LPD. Theremaining cases in Table VII show significant differences between pairs ofACFTs for the two layouts. The percentage change ranges from as low as 3percent to as high as 211 percent.

For small batch sizes, the ACFT in the EPD cases are almost identical in bothlayouts. However, for LPD and SPD, ACFT for FCM is relatively higher. This is

due to the fact that, when batch sizes are small, it means ordering one product.Therefore, each focused cell receives all parts of a product order. In the CM layoutcases, parts for different products are sent to different departments and cells. Theeffects that the three levels of demand distributions had on the averagecomponent flow times for the two layouts are shown in Figure 2. The ACFT forEPD, LPD, and SPD are averaged over each batch size for each layout. Thegeneral trend is that ACFT increases as batch size increases in both layouts.

Average end item completion time. Table VIII shows the results andstatistical analyses of comparisons for average end-item completion time(AEICT). This table shows that product average completion times of the

two layouts are significantly different. When products of different sizeshave an equal probability of being ordered (EPD), the AEICT increases asbatch sizes increase. The only exception is in the SPD case, where theAEICT for small batches in the FCM model is relatively higher than thatof CM model. This is a result of a compounded effect of longer ACFTs forFCM. Figure 3 shows the effects that the three levels of demanddistributions had on the average component flow times for the two layouts.The AEICT for EPD, LPD, and SPD are averaged over each batch size for


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each layout. The general trend is that AEICT increases as batch size

increases in both layouts.Average work-in-process inventory. Table IX shows that average work inprocess inventory (AWIP) is significantly different in every operating scenariowhere different demand distributions and batch sizes are used. A generalobservation that can be made is that AWIP inventory increases as batch sizesincrease. As batch sizes increase, items wait longer for each other to be movedfrom one stage to another. This causes the number of parts in the system, atany time, to increase in a proportional manner.

Demand distribution Batch Size Statistic CM FCM Difference

EPD Small Mean 5.81 6.30

% change 2

13t-value 21.7p 0.065

Medium Mean 7.23 6.82% change 3t-value 1.66

p 0.070Large Mean 12 9

% change 26t-value 6.55

p 0.000LPD Small Mean 5.3 8.7

% change 258

t-value 2

6.96p 0.000Medium Mean 6.9 6.8


p 0.998Large Mean 11 8.2


p 0.000SPD Small Mean 5.4 17.6


p 0.000Medium Mean 9.6 6.8% change 28t-value 15.20



p 0.000

Table VII.ACFT for demanddistribution andbatch size models

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Figure 4 shows the AWIP inventory averaged over all three demanddistributions for each batch size. When batch sizes are small, the AWIP is notsignificantly different in each layout. However, as batch sizes increase, AWIPincreases for both layouts.

The effect of changes in demand distributions. As demand distributionschange, the performance of each model changes to some degree. For example,Figure 5 shows how ACFT increases as product mix changes for the FCMmodels. The product average completion time as shown in Figure 6, increasedrelatively when the demand distribution changed from EPD to LPD. This isdue to the fact that larger products (i.e. products with more components) are

ordered more frequently than smaller products. This causes batches to waitlonger to reach a pre-set batch size. This waiting causes AEICT to increaserelatively. In the case of SPD, the AEICT increases even more, because partshave to wait even longer as larger products are being ordered less frequentlythan smaller products.

The AWIP inventory, as shown in Figure 7, increases for all layouts as thedemand distribution changes from EPD. FCM models do not accumulate moreAWIP than CM models. This is because batches in FCM models are made up ofparts of the same products and do not have to wait as long as others to bebatched or assembled.

Multi-level product models. Two manufacturing scenarios are compared inthis section. In the first case, both systems process single level products. In thesecond case, the process is for multi-level products. Table X shows how ACFTvaries with BOM structure, giving comparisons for each of the two productstructures. The percentage change shows that ACFTs in FCM models are lowerthan those in the corresponding CM models. Since the way ACFT is determinedis not affected by the way parts are assembled, the results of single and multi-level models for each layout are similar.

Figure 2.ACFT averaged over all

demand distributions


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Average end-item completion time as shown in Table X, is higher in multi-levelmodels than in single level models. The AEICT is high in the CM layout and

low in FCM models. This indicates that processing multi-level products has thesame effect on both layouts.The AWIP inventory as shown in Table X, for all models processing single

and multi-level products is significantly different from one layout to another.AWIP is high in the CM and low in the FCM models. This is attributed to themanner in which each model processes and assembles parts in the system. It isworth noting that the differences between layouts in the multi-level system aresimilar to those in the single level production system.

Demand distribution Batch size Statistic CM FCM Difference

EPD Small Mean 10 7

% change 22t-value 7.65p 0.000

Medium Mean 14 11% change 25t-value 13.21

p 0.000Large Mean 25.2 20


p 0.000LPD Small Mean 9.3 10.6

% change 220

t-value 2

2.40p 0.027Medium Mean 14 12




p 0.016SPD Small Mean 9 20.7


p 0.000Medium Mean 16 11% change 31t-value 17.67



p 0.000

Table VIII.AEICT for demanddistribution andbatch size models

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The effect of increased set-up time. The set-up time was tripled in order to testthe effect of larger set-up times on all performance criteria for the two layouts.Parts with larger set-up times produced higher average component flow times,as shown in Table X. This can be confirmed logically; as set-up time increases,the part spends more time in the system. It has to wait, not only for its large set-up time, but also for parts ahead of it, which also have large set-up times. It isworth mentioning that the FCM model is penalized more by the larger set-uptime than the CM model. This shows the amount of set-up time saving that CMprovides by grouping similar parts into families.

The AWIP inventory was not significantly affected by an increase in set-uptime in the JS models. However, in the FCM model, the AWIP doubled, from 28to 57 parts. Most of the increases in the large set-up time models, were in themachine queues. As can be seen in Table X, the FCM models produce lowerAWIP when set-up time is small, while the reverse is the case for this criterionwhen set-up time is larger.

Priority rule effect analysis. Priority rules control the sequence by whichparts are dispatched as machines become available. Using different priorityrules should affect ACFT directly and AEICT and AWIP indirectly. The twopriority rules which are compared in this experiment are FIFO and FISFO. It isbelieved that both layout models should benefit from the FISFO rule (i.e. reduceACFT and AEICT). Table X shows ACFT, AEICT and AWIP for both models.Generally, there is no significant improvement from using FISFO. The lack ofimprovement in these performance measures is due to the limited effect ofpriority rules in these systems. The average queue length was low in most ofthe situations. Therefore, the priority rule did not have an effective role in theproduction system. The average queue lengths are so short that they limit theeffectiveness of the dispatching rule applied.

Figure 3.AEICT averaged over all

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Overall, the results are consistent with the expectations in most cases. In many

cases, the part mean flow times are relatively shorter in the CM models thanthose in the FCM models, specially as the ratio of set-up time to processing timeincreases. On the other hand, product average completion times are shortest inthe FCM models. Table XI shows the average criteria values across all demanddistributions and batch sizes. It is clear that the FCM outperformed the CMmodel in the manufacturing scenarios described. The average work in processinventory is always high in the CM model, especially as batch size increases.The AWIP is always low in FCM models, even as batch size increases. In

Demand distribution Batch size Statistic CM FCM Difference

EPD Small Mean 24 14

% change 40t-value 81.23p 0.000

Medium Mean 40 28% change 28t-value 36.60



p 0.000LPD Small Mean 26 34

% change 231

t-value 2

20.47p 0.000Medium Mean 41 30




p 0.017SPD Small Mean 20 40


p 0.000Medium Mean 41 27% change 35t-value 25.37



p 0.004

Table IX.AWIP for demanddistribution andbatch size models

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previous studies (Shafer and Meredith, 1993; Morris and Tersine, 1994), whereprocess layouts are compared to CM layouts, the completion time of jobs in theCM layouts were found to be longer than they are in the process layouts. As aresult of longer completion times, AWIP levels are higher. As shown inTable XI, the lower AWIP in FCM can be attributed to the lower completion

times for the end-items in FCM than they are in CM.

ConclusionsThis paper presented the results of a study aimed at comparing CM with FCM.In addition, it included batching and assembly times in its criteria which fewresearchers in this area have done. The FCM layout schemes major advantagewas minor batching delays before assemble. This accounted for its superior

Figure 4.Average WIP over alldemand distributions

Figure 5.ACFT averaged over all

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performance in the average end-item completion times and the AWIP inventorylevels. The CM layout schemes major advantage was from set-up reductiontimes which resulted in better performance when batch sizes were small or

when set-up time magnitudes were large.

Further research into the behavior of FCM layouts is needed. The ultimate goalof this stream of research would be the development of guidelines pertaining tothe appropriate environment for its use and in determining how best to manage it.

For example, this study allowed only one end-item per cell and this may not bepractical in some industrial settings. Future studies should evaluate this schemewhen more than one end-item is assigned to each cell. Research such as this is

clearly relevant and timely to a large number of manufacturing organizations.

Figure 6.AEICT averaged over allbatch sizes

Figure 7.AWIP averaged over allbatch sizes

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CriteriaACFT AEICT AWIP

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FCM modelSingle 6.82 11.0 28.36Multi 6.90 12.40 28.50

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FCM modelFIFO 6.82 12.0 28.0FISFO 6.8 11.0 26.0

Notes: ACFT average component flow time; AEICT average end-item completion time;AWIP average work-in-process inventory

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