3802-mahendrawathi-managing variety si jmtm

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Managing product variety inmultinational corporation

supply chainsA simulation study

Mahendrawathi Er Information System Department, Sepuluh Nopember

Institute of Technology, Surabaya, Indonesia, and

Bart MacCarthy Division of Operations Management, Business School,

University of Nottingham, Nottingham, UK

Abstract

Purpose – For manufacturing enterprises, today’s business environment is characterisedby globally dispersed supply and manufacturing networks. In addition, the level of varietyin products continues to increase in almost all sectors. Greater understanding of the management of product variety in international operations is required. Aims to discuss this issue.

Design/methodology/approach – A generic simulation model representing a multinationalcorporation (MNC) supply chain is used to investigate the impact on supply chain performance of increasing product variety in combination with supply lead-time and demand uncertainty in aninternational setting. The simulation focuses on the upstream activities of production planning,inbound supply and manufacturing. The structure and logic of the simulation model are based on

insights obtained from an empirical study of real MNC supply networks.

Findings – The study shows that increasing the level of product variety has a detrimentalimpact on supply chain performance. In the presence of supply lead-time and demanduncertainty, high levels of variety result in much longer flow times and much higher systeminventory relative to more stable conditions. The impact is greatest when variety involvescritical materials which are required early in the production process and that entail long set-uptimes.

Research limitations/implications – The study could be extended to incorporate more advancedinventory control models, the inclusion of downstream activities, multiple manufacturing sites andmultiple potential supply routes.

Practical implications – Implications for the selection of suppliers and for inventory controlpolicies are discussed in the context of international operations. The potential value of postponementstrategies and the need in some cases for fundamental product and process redesign to mitigate the

negative impacts of variety are highlighted.

Originality/value – Managing product variety in the context of international operationshas received very little attention to date in the research literature. This study quantifies thepotential impact of increasing product variety on supply chain performance in an internationalsetting.

Keywords Supply chain management, Multinational companies, Simulation, Product variants,Supply and demand

Paper type Research paper

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1741-038X.htm

Managingproduct variety

1117

Received October 2005Reviewed June 2006Accepted July 2006

Journal of Manufacturing Technology

Management

Vol. 17 No. 8, 2006

pp. 1117-1138

q Emerald Group Publishing Limited

1741-038X

DOI 10.1108/17410380610707410



1. IntroductionMore and more companies are involved in international supply chains in various ways,ranging from simple import and export activities to the development of subsidiaries inforeign countries. International issues can be considered a common characteristic of

today’s supply chains (Akkermans et al., 1999; Wisner et al., 2004). In tandem with theinternationalisation of supply chains, product variety has been growing in almost allsectors (Cox and Alm, 1998; Bils and Klenow, 2001).

In any operational context, high levels of product variety may result in set-up andchangeover delays and may require more complex procurement and demandmanagement approaches (Fisher et al., 1994; Randall and Ulrich, 2001). When thesupply network is dispersed around the world, the challenge is even greater withpotentially longer and more uncertain delivery times, as well as inflexibilities inplanning, procurement and ordering systems (Levy, 1995; Lowson, 2001). Managingproduct variety in an international setting is a challenging and important and task.The combined literature on international business, supply chain management andproduct variety is extensive. However, the impact of product variety in relation tofactors usually found in the context of international operations has not been addressed.

This paper investigates the issue of product variety in typical multinationalcompany (MNC) supply chains. The main objective is to examine the impact of varietyin conjunction with other issues of importance in the international context, particularlydemand uncertainty and supply lead-time uncertainty. The approach used is toinvestigate through simulation the performance of an MNC supply chain in orderto identify key issues and to indicate potential strategies to address them. The focus ison the upstream activities of production planning, inbound supply and manufacturingrather than downstream activities of distribution to markets and customers.

In the next section, relevant literature is reviewed. Empirical fieldwork thatprovided the basis for the development of a simulation model is then described briefly

in Section 3. This is followed by a detailed description of the simulation work, whichincludes the simulation environment, assumptions, simulation experimentation issuesand performance measures. Results from the simulation experiments are discussed inSection 5. Section 6 describes the implications of the findings for practice. Brief concluding remarks and directions for further research are provided in the finalsection.

2. Literature reviewThis work builds upon several streams of literature including international operations,supply chain and international supply chain management and product variety.

Companies are driven to develop dispersed manufacturing networks for manyreasons, including achieving lower costs, accessing new markets, seeking strategic

assets such as a skilled workforce, special technologies, etc. (Ferdows, 1989; MacCarthyand Atthirawong, 2003). MNCs are an important part of today’s business environment.Ghoshal and Bartlett (1990) note that an MNC consists of a group of geographicallydispersed and goal-disparate organizations that includes its headquarters and thedifferent national subsidiaries.

According to Porter (1986), the distinctive issues in international, as opposed topurely domestic operations, can be summarised in two dimensions – configuration andco-ordination. Configuration is concerned with location and structure – where

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activities are performed and in how many places. Co-ordination is concerned with thelinkages between different activities of companies operating internationally, such asplanning and scheduling (Oliff et al., 1989). Effective configuration and co-ordinationstrategies may enable an MNC to achieve strategic advantages over its competitors in

terms of costs and market responsiveness.One of the key issues found in management of MNC is the choice between global or

local sourcing for raw materials, parts, components or products. The availability of low-priced commodities in the world market may force MNCs to source globallyinstead of domestically (McGrath and Bequillard, 1989). Some MNCs decide to sourcefrom trusted suppliers, despite their location, in order to procure high quality materials.However, sourcing from global suppliers located far from manufacturing units maylead to long and highly uncertain delivery times (Levy, 1995). Based on his study onNorth American and UK retailers, Lowson (2001) indicates that domestic suppliers aremore flexible and responsive to accommodate volume and mix changes compared tointernational suppliers.

Product variety has received attention in a separate stream of literature (Ramdas,2002). Several authors investigate the impact of product variety on manufacturingperformance. Anderson’s (1995) study in three textile weaving plants of a single firmindicates that manufacturing overhead costs increase with the number and severity of set-ups. MacDuffie et al. (1996) studied 70 assembly plants world wide and foundvarying impacts of different types of product variety on labour productivity. Anempirical analysis by Fisher and Ittner (1999) in the automotive industry indicates thatin mixed model assembly operations, variability in product mix may be a betterindicator of variety than measures such as the number of products or number of parts,commonly used in research studies and activity-based costing systems.

More recently, some studies have investigated the issue of product variety in thecontext of supply chains. Randall and Ulrich (2001) suggest that there is a coherent

way to match product variety with supply chain structure and that firms that correctlymatch the types of variety offered with the supply chain structure perform bettercompared to those that fail to do so. Thonemann and Bradley (2002) presented amathematical model to analyse the effect of product variety on the performance of a supply chain with a single manufacturer and multiple retailers. They demonstratethat disregarding the effect of product variety on lead time can result in poor decisionsand lead companies to offer a level of product variety that is greater than optimal.

Product variety may result from differences in materials and or productionprocesses at various stages of the supply chain. Several authors have noted thepotential effect of product variety on supply systems (MacDuffie et al., 1996; Milgate,2001). More variety potentially adds complexity to the configuration and co-ordinationof supply networks (Milgate, 2001).

High product variety also creates uncertainty in demand (Randall and Ulrich, 2001).Fisher et al. (1994) argue that having a wider range of product variants means that it ismore difficult to predict demand at the product level. In the presence of demanduncertainty, it is difficult to precisely match supply with demand. Mismatches causedisruptions to production, particularly when demand exceeds supply. Variety alsogenerates costs associated with holding inventory and product markdowns whensupply exceeds demand and the costs of lost sales when demand exceeds supply(Randall and Ulrich, 2001).


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The challenge of managing product variety is amplified further whenmanufacturing of the products are dispersed across national boundaries.Geographical distance between elements in international supply networks may beassociated with long lead-times and greater delivery uncertainty (Levy, 1995). As a

consequence, some activities, particularly procurement of materials, must beconducted some time in advance of real demand signals. This exposes the supplynetwork to greater risks of a mismatch between the supply of materials procured basedon forecasts and the actual demand that ensues. It may also limit the supply network’sability to respond to demand changes in the marketplace.

Simulation is a tool commonly used to address a range of issues in operationsmanagement and has gained popularity in supply chain analysis due to its strengthin predicting system variation and interdependencies (Wyland et al., 2000). It has beenused to investigate the impact of demand and supply uncertainty on supply chainperformance. In addition, it has also been used to investigate the impact of productvariety on manufacturing performance. Several important studies that used simulationto address these issues are summarized in Table I.

The review of the various streams of literature indicates that investigating productvariety in a supply chain operating internationally is a timely issue that needs to bepursued. The issues relating to product variety and international operations have notbeen addressed simultaneously in the literature to date. This study aims to fill part of this gap by addressing the following research question:

RQ1. What is the impact of product variety on the performance of a supply chainoperating internationally in the presence of [0] supply and demanduncertainty?

To address the research question, both an empirical and a simulation study have beenconducted. The purpose of the empirical study was to provide insights on:

.

the characteristics of companies operating internationally in terms of planningand control; and

. the issues facing these companies with respect to product variety, supply anddemand uncertainty.

A simulation study was then conducted to investigate more generally the impact of product variety in the context of an enterprise operating internationally. The resultsfrom the simulation study are the focus of this paper. The empirical study is describedvery briefly first.

3. Empirical studyIn the initial part of this work, we conducted a fieldwork study involving

11 manufacturing companies in Indonesia and one company in the UK belonging tointernational supply networks (Er, 2004; Er and MacCarthy, 2002). Semi-structuredinterviews were conducted to gather information on the network characteristics andrelevant issues in managing international supply chains. Four of the companiesprovided the most relevant insights specifically for MNC supply chains. The four weresubsidiaries of MNCs producing leather shoes, ladies underwear and light bulbs(two companies). Analysis of the empirical data from the four companies highlightedthe following common characteristics:

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. Configuration. The major elements in the four MNC supply chains weresuppliers, a manufacturer, sales office, wholesalers and corporate headquarters.Sales offices, wholesalers or distributors and in some cases corporateheadquarters were in charge of demand management activities. We refer tothese elements as “internal customers” that transform demand information fromend customers into production demand for the manufacturer:

Literature Focus of the study Description of study

Fisher and Ittner (1999) Product variety Indicates that the use of directlabour slack is an optimal

response to increased productvariety in terms of optionvariability

Levy (1995) Demand and supplyuncertainty

Demand instability raises theproportion of unfulfilleddemand for differentinternational supply chainconfigurations. Disruption insupplier deliveries reduceddemand fulfilment andincreased inventory levels

Towill (1996) Supply chain dynamicbehaviour

Used system dynamics insupply chain redesign to

generate added insights of asystem’s dynamic behaviourand the underlying causalrelationships

Ovalle and Marques (2003) E-collaboration tools Develop system dynamicsmodel to assess the impact of using e-collaboration tools onthe supply chain performance

Spengler and Schroter (2003) Integrated production andrecovery system

Used system dynamics tomodel an integrated productionand recovery system forsupplying spare parts

Persson and Olhager (2002) Supply chain design Developed a discrete eventsimulation model to evaluate

alternative supply chaindesigns

Al-Zubaidi and Tyler (2004) Retailing and supplyprocedures

Developed a discrete simulationmodel to investigate the effectsof improved retailing andsupply procedures on financialand other performancemeasures

Tiger and Simpson (2003) Material flow Developed a discrete-eventsimulation to assist a MNC inunderstanding the impact of material flow from the US tothe Asia Pacific region

Table I.Examples of simulationstudies in the literature


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. Co-ordination. Production is triggered by demand from the internal customer.Planning and forecasting is typically done with centralised control fromheadquarters.

. Products. Products are not technologically complex, typically consumer products.

. Manufacturing process. Manufacturing can be classified as primarily discreteprocessing. In general, the manufacturing processes observed in the four casescan be classified into three sequential stages of production. The first stage istypically associated with the main material (MM) preparation involvingfabrication or manufacturing operations on main (raw) materials. These arefollowed by assembly processes. The final stage typically consists of finishingand packaging of finished products. Manufacturing of products is typicallycarried out in batches.

. Product variety. Variety is determined by the use of different types of materials atdifferent stages of the production process.

The study also highlighted the problems of supply and demand uncertainty as majorchallenges in international supply chain management. Each company procuresmaterials from a combination of global and local suppliers and faces supplier lead-timeuncertainty. It was clear that global sourcing entailed longer lead-times and higherdelivery uncertainty compared to local sourcing. Each manufacturing company alsofaces demand uncertainty as production requirements and forecast information fromtheir internal customers are updated periodically to reflect the most recent conditionsin the marketplace. We refer to this periodic updating of demand and forecasts as a“rolling forecasting system”.

Findings from the empirical study provided important qualitative insights on howMNCs manage product variety in international supply chains. However, such a studycan provide only limited information on the magnitude of impact of product variety

and related factors on overall performance. Any empirical study can give only“point samples” of the complete “operational space”. A generic simulation modelallows us the opportunity to explore, in an informed way, the range of potentialbehaviour and performance that may be observed or may be possible. Thus, asimulation study was conducted to give greater insights. The characteristics of theMNC supply chain described above have been used as a platform to develop a genericsimulation model for experimentation.

4. Simulation studyInsights and information gained from the case companies on configuration have beenused in developing the simulation model structure. Knowledge gained on co-ordination

has provided the “logic” for the information and material flows. Important and difficultproblems facing the case companies, including demand uncertainty, supply lead-timeuncertainty and increasing levels of product variety, are treated as factors to beinvestigated in the simulation. In particular, the simulation environment reflectsthe international MNC context in two ways:

(1) by adopting centralised planning and forecasting resulting in long andinflexible planning lead times, particularly to ensure materials supply, and

(2) in the selection of parameter values and ranges used in experimentation.

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The simulation model uses a discrete-event approach and has been developed usingGeneral Purpose System Simulator (GPSS) WorldTM (Minuteman Software, 2005). Fulltechnical details of the model implementation are provided in Er (2004).

4.1 Simulation environment The simulation environment is shown in Figure 1. It captures a three-stage MNCsupply network consisting of:

(1) suppliers;

(2) a manufacturer; and

(3) internal customers.

At the beginning of a planning period, the internal customer generates productiondemand and forecast information for the manufacturer. This information is updatedfrom one period to the next reflecting a rolling forecast system, enabling demanduncertainty to be captured.

The manufacturer uses the information from internal customers to plan itsproduction and procure materials. Figure 1 shows that the manufacturing processdivided into three typical stages – Process 1, Process 2, and Process 3. Production of aspecific product variant requires different types of materials, classified here as:

. MM representing raw material fabricated in Process 1;

. unique material (UM) representing auxiliary materials, parts or sub-assemblies,assembled in Process 2; and

. packaging material (PM) representing packaging and general finishing itemsused in Process 3.

Each type of material has a number of options designated by different numbers, e.g.

MM 1, UM 2, etc. The system produces different product variants through differentoptions of MM, UM and PM in manufacturing processes. For example, product variant121 is a product requiring PM 1, MM 2 and UM 1. Processing different materials atdifferent stages of manufacturing requires set-up activities.

Each type of material can be obtained from a local supplier located close to themanufacturer or a global supplier located in a different country. The length of timerequired by a supplier to deliver the material is subject to uncertainty, referred to hereas supply lead-time uncertainty. Procuring materials from global suppliers requires arelatively long lead-time compared to local suppliers. Thus, the manufacturer has toplace orders further ahead of production if the materials are bought from globalsuppliers. Different types of material received from suppliers are stored in differentinventory storage buffers as shown in Figure 1.

The production of an order is divided into batches. It is assumed that the size of transfer batch is equal to the process batch. We assume a relatively efficient andflexible plant and use the smallest feasible batch size. The production sequence of batches is determined using a scheduling rule that minimises set-up time. Before aproduction schedule is executed, the availability of materials to produce each batch ischecked. If sufficient materials for a batch are not available, the materials are notassigned to the batch. If materials are available, they are assigned to (pegged to) aspecific batch and sent to the queue for the appropriate process.


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Figure 1.The simulationenvironment

I N T E R N A L C U S T O M E R

W h o l e s a l e r s 1 /

D i s t r i b u t o r s 1 /

S a l e s

O f f i c e s 1

W h o l e s a l

e r s 2 /


S a l e s O f f i c e s 2

F l o

w o f

I n f o r m a t i o n

…

…

W h o l e s a l e r s 3 /


S a l e s O f f i c e s 3

C o r p o r a t e /

R e g i o n a l

H e a d q u a r t e r s

A c t u a l d e m a n d

F o r e c a s t p e r i o d e + 1 … n

( A g g r e g a t e & p r o d u c t -

l e v e l ) u p d a t e d e v e r y

m o n t h

S u p p l i e r

M a i n M a t e r i a l

G l o b a l o r L o c a l

S u p p l i e r

U n i q u e M a t e r i a l

G l o b a l o r l o c a l

S u p p l i e r

P a c k a g i n g

M a t e r i a l

G l o b a l o r l o c a l

E l e m e n t o f S u p p l y

C h a i n

U n i q u e M a t e r i a l

I n v e n t o r y

1 , 2 , 3 , 4 , 5

P a c k a g i n g M a t e r i a l

I n v e n t o r y

1 , 2 , 3 , 4 , 5

P r o c e s s 1

P r o c e s s 2

P r o c e s s 3

S u p p l y

L e a d - t i m e

Q u e u

e

P r o c e

s s

1

S U P P L I E R S

M A N U F A C T U R E R

F i n i s h e d

P r o d u c t s

P r o d u c t 1 1 1

P r o d u c t 1 1 2

E t c .

B a t c h e s

s c h e d u l e d f o r

p r o d u c t i o n

M o n t h l y

P r o d u c t i o n

P l a n

O r d e r

M a t e r i a l

M a n u f a c t u r e r

B a t c h e s o f U n i q u e

M

a t e r i a l

B a t c h e s o f P a c k a g i n g M a t e r i a l

I n v e n t o r y

M a i n M a t e r i a l

I n v e n t o r y

1 , 2 , 3 , 4 , 5

F l o w o f

M a t e r i a l

B a t c h e s o f

M a i n m a t e r i a l

Q u e u e

P r o c e s s

2

Q u e u e

P r o c e s s 3

Q u e u e w a i t

M M

Q u e u e w a i t

U M

Q u e u e w a i t

P M

Q u e u e f o r

m a t e r i a l s

Q u e u e f o r

p r o c e s s

P r o c e s s

I n v e n t o r y

L e v e l

D o c u m e n t o r

i n f o r m a t i o n

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At each processing stage, there is a queue to enter the process. As soon as Process 1 isavailable, MMs are processed. The output of Process 1 is then assembled with UMsin Process 2. As Process 2 is completed, its output is packed and finished withPMs in Process 3. At any stage of production, a batch is delayed if the correct materials

are not available. Delayed batches wait in the corresponding queue for materials(e.g. queue wait MM) until sufficient materials are available. Processing differentmaterial options at each stage of production introduces a delay due to set-up activities.Finally, finished products are allocated to satisfy the internal customer demand.

4.2 AssumptionsIn developing the simulation model, several assumptions are made:

. The system works with an operational month of 20 working days. The capacityper month is assumed to be constant.

. No planned level of safety stock of materials and finished goods is assumed.This is an aspect highlighted for further work.

. The entire production process is conducted in the same plant and there is no timedelay associated with transferring batches between production processes.

. A scheduling rule that minimises set-up time in production and ultimatelyminimises the average lead-time is applied. The rule first takes into account thetime a batch was generated to ensure that batches generated earlier are givenpriority to be processed first. Then, the rule ensures that batches with the samematerials are done sequentially in order to reduce the need for set-up (Er, 2004).Set-up time, which occurs when different material options are processed insequence in a certain process, is assumed to be constant for all processes andsequence independent.

. In the results reported here, each type of material has either none or five different

options, representing no variety or a high level of variety.. In the presence of product variety, all product variants have equal proportions

over the total demand.

4.3 Simulation factorsThree main factors are investigated in the simulation study. The first is product variety. The level of product variety produced by the manufacturer is determined bythe number of options for MMs, UMs and PMs.

The second factor investigated is supply lead-time uncertainty, determined by thelocation and the reliability of suppliers. While supply lead time uncertainty may alsoapply companies operating in a purely national setting, the extent of supplier lead timeuncertainty used in the model captures the MNC context. In MNC supply chain,

transport of goods internationally is commonly done by sea, which is typically longerthan local sourcing. Therefore, local suppliers are expected to deliver more quicklycompared to global suppliers located in a different country. Ideally, the supplierdelivers just in time. However, in reality, suppliers may deliver earlier or later than thescheduled time. The supply lead-time distribution is expected to be less variable if thesupplier is local. With global suppliers, the supply lead-time distribution is affected notonly by transportation times, but also potentially by additional delays due to problemsassociated with production, transportation schedules, customs and communication or


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unexpected problems due to weather, strikes, etc (Levy, 1995). As a result, buying fromglobal suppliers entails greater supply lead-time uncertainty, reflected in greaterpotential variability in supply lead-time.

The third factor to be investigated in this study is demand uncertainty, which is

captured from the changes in the total volume of demand from one period to the next.Volume uncertainty is reflected in the deviation of demand from an average value.Demand uncertainty due to continuous updating of forecasts combined with longprocurement time results in system nervousness, a problem that applies morecommonly to MNCs than national companies (Houlihan, 1987). This is anothercharacteristics of the MNC environment captured in the simulation.

4.4 Design of simulation experimentsThe general research question is divided into four specific questions to be investigatedin the simulation study:

(1) What is the impact on supply chain performance of increasing product variety

when both supply and demand are constant?(2) What is the impact on supply chain performance of supply lead-time

uncertainty in a high variety and constant demand situation?

(3) What is the impact on supply chain performance of demand uncertainty in ahigh variety and constant supply lead-time situation?

(4) What is the impact on supply chain performance of demand and supplyuncertainty in a high variety situation?

First, the simulation is run under static conditions with the system producing only oneproduct variant and both supply lead-time and demand remaining constant. Then, thefirst set of experiments investigates the situation using different options of material for

product variety whilst supply lead-time and demand are constant. Comparing resultsfrom this set of experiments with static conditions will reveal the magnitude of impact of product variety on supply chain performance. Results from the first set of experimentsare used as a base case to compare the results of subsequent sets of experiments. Thenext three sets of experiments investigate the situation with five material options underdifferent demand and supply uncertainty situations. The second set of experimentsinvestigates the situation when supply lead-time is uncertain but demand is constant.The third set of experiments investigates the situation when demand is uncertain andsuppliers deliver materials with a constant lead-time. The final set of the experimentsinvestigates the situation when both supply and demand are subject to uncertainty.

4.5 Performance measures

For this study, we are particularly interested in investigating the impact of productvariety on the amount of time that a product spends in the system, i.e. flow time, and onthe level of inventory.Flow time is measured as the period of time a product spends in thesystem from the time the demand for the product is generated by the internal customeruntil the product has completed manufacture. The levels of inventory for specific typesof material (MMs, UMs and PMs) are measured, as well as the total inventory, referred toas system inventory, before the materials are pegged to a specific batch. Both flow timeand inventory are measured in terms of their average values.

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4.6 Input data and parametersThe simulation model strives to capture generic characteristics of MNC supply chainsto give insights on the potential impact of different factors on their performance. It doesnot try to mimic any specific real system. Findings from the case companies are used to

inform the simulation but specific quantitative data from companies has not been used,as it would not be consistent with the objectives of the study. Instead, reference sets(Kritchanchai and MacCarthy, 2002) of input data and parameters were establishedthat are representative of the generic observations in the empirical study. For example,three of the case companies provided some information regarding supplier’slead time. These companies generally do not know the exact form of supplier leadtime distribution, but they could estimate the minimum, maximum and most likelyvalues. This indicated that sourcing from a local supplier on average required 20 dayswhile global suppliers on average were expected to deliver in 60 days in suchsituations. A triangular distribution is commonly used in such circumstances (Keltonet al., 2003). A Normal distribution is not used to avoid the possibility of meaninglesszero or negative values. The full reference sets of input data and parameters used in the

simulation experiments are summarised in Tables II and III. The determination of these data is discussed in Er (2004). In the interests of brevity they are not discussedfurther here.

4.7 Tactical planning for the simulation experimentsSimulation experiments require consideration of transient, dynamic and randomeffects. Before starting the experiments, it is important to ensure that the results aretaken when the simulation has reached a steady state (Law and Kelton, 2000). Visualexamination of the results indicated that the system had reached a steady-statecondition after 5,000 warm up periods. Therefore, each experiment is run for 5,000warm up periods, followed by 5,000 measurement periods. In addition, each experimentunder uncertain conditions has been replicated five times.

5. Results from the simulation experiments5.1 Impact of variety under constant demand and supply conditionsResults from the experiments under constant demand and supply conditions are shownin Table IV. In the static situation when the system produces only one product andboth supply and demand are constant, the average flow time is 7.78 days per product.

Parameters Values

Capacity 1 month ¼ 20 days ¼ 160h ¼ 9,600 minAverage demand 16875 unit/month

Batch size 135 unit of productProcessing time 0.0018 day/product ¼ 0.864 min/productProcess 1 0.0009 day/product ¼ 0.432 min/productProcess 2 0.00072 day/product ¼ 0.3456 min/productProcess 3 0.00018 day/product ¼ 0.0864 min/productSet-up timeSet-up process 1 0.75 day ¼ 360minSet-up process 2 0.25 day ¼ 120minSet-up process 3 0.0625 day ¼ 30 min

Table II.The reference input data

and parameters used inthe simulation model


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F a c t o r s

L e v e l

D e t e r m i n a n t o f p r o d u c t v a r i e t y

L e v e l 1 ( l o w v a r i e t y )

L e v e l 2 ( h i g h v a r i e t y )

N o . o f d i f f e r e n t M M

s

1

5

N o . o f d i f f e r e n t U M s

1

5

N o . o f d i f f e r e n t P M

s

1

5

S u p p l y l e a d - t

i m e

M M

s u p p l y l e a d - t i m e

L o c a l : t r i a n g u l a r ( 5 ,

2 0 ,

3 5 ) d a y s

G l o b a l : t r i a n g u l a r ( 1 5 ,

6 0 ,

1 0 5 ) d a y

s

U M



2 0 ,

3 5 ) d a y s


6 0 ,

1 0 5 ) d a y

s

P M



2 0 ,

3 5 ) d a y s


6 0 ,

1 0 5 ) d a y

s

D e m a n d u n c e r t a i n t y

D e m a n d

C o n s t a n t ¼

1 6 , 8

7 5 u n i t s p e r m o n t h

V a r i a b l e :

D e m a n d ¼

f o r e c a s t ( i 2

1 , 1

) þ e r r o r N ( 0 , 3

3 7 5 )

F o r e c a s t ( i þ

1 ) ¼

1 3 , 5

0 0 þ

0 . 2

£

d e m a n d


2 ) ¼

1 0 , 1

2 5 þ

0 . 4

£

d e m a n d


3 ) ¼

6 , 7

5 0 þ

0 . 6

£

d e m a n d


4 ) ¼

3 , 3

7 5 þ

0 . 8

£

d e m a n d

Table III.Factors and parametersused in the simulationmodel

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The average flow time increases by 29 per cent when the number of MM options isincreased from one to five, which results in five different product variants. This is dueto set-up activities that occur when different options of MM are processedconsecutively in Process 1. Increasing the number of unique and PMs to five, when

there is one option of MM, does not result in a significant increase in the average flowtime (less than 1 per cent). These results are expected because set-up time associatedwith MM is significantly longer than the set-up time associated with unique and PMs.

The differential impacts between MM, UM and PM variety are also influenced bythe fact that the effect occurs at different stages in production. Variety in MMs occursat the beginning of the production process and the process itself takes a relatively longtime (50 per cent of total processing time), making MM availability critical toproduction. Thus, extended lead-times associated with MM variety will affect theentire production time of a batch and also subsequent batches. In contrast, PM varietytakes place at the last stage of the production process. Thus, production of otherbatches in Process 1 and 2 are not blocked by extended lead-times due to PM variety.

Table IV also shows that the average system inventory remains constant despite anincrease in the amount of material variety. Again this is expected as we measure onlyinventory before it is pegged to a batch. Thus, there are no uncertainties that can leadto a higher inventory level in constant demand and constant supply situations.Although a wider variety of materials needs to be procured, the manufacturer canpredict the exact amount and type of materials in these situations. Furthermore,materials always arrive at the expected time. The above results provide the base casefor the remaining experimental conditions.

The average values for each metric for the second, third and fourth set of experiments are summarised in Tables V and VI. In comparing the results with thebase case, the percentage change is given in the adjacent columns.

5.2 Impact of supply lead-time uncertaintyIn the second set of experiments, materials as determinants of product variety arebought either from local or global suppliers with different ranges of lead-time.Uncertainty in supply lead-time means that some materials may be delivered earlierthan the expected time. Materials then have to be held in stock for longer, whicheventually leads to significant increases in inventory compared to the base case.Supply lead-time uncertainty also means that a certain amount of material might bedelivered later than expected. Tardiness in material arrival causes delays inproduction, and eventually leads to longer flow time.

Figure 2 shows the impact on average system inventory and average flow time of supply lead-time uncertainty in the presence of product variety. The average system

ExperimentsFlow time

(day/product)Percentageof change

Inventory(units)

Percentageof change

Static conditions (one product,constant demand and supply) 7.78 2,537MM variety 10.03 28.92 2,537 0UM variety 7.83 0.64 2,537 0PM variety 7.78 0.00 2,537 0

Table IVResults from experiments

for a constant demandand supply situation


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S u p p l y u n c e r t a

i n t y

D e m a n d

u n c e r t a i n t y

S u p p l y a n d d e m a n d u n c e r t a i n t y

D e t e r m i n a n t o f

p r o d u c t v a r i e t y

B a s e c a s e

L o c a l

P e r c e n t

G l o

b a l

P e r c e n t

P e r c e

n t

L o c a l

P e r c e n t

G l o b a l

P e r c e n t

M M

1 0

. 0 3

1 0 . 6

2

6

1 5 . 1

0

5 1

2 4 . 5

8

1 4 5

2 6 . 8

6

1 6 8

3 2 . 9

2 2 8

U M

7

. 8 3

9 . 1

7

1 7

1 1 . 3

5

4 5

1 2 . 4

7

5 9

1 2 . 6

6

6 2

1 5 . 6 6

1 0 0

P M

7

. 7 8

8 . 2

7

6

1 0 . 1

8

3 1

1 1 . 3

6

4 6

1 1 . 7

4

5 1

1 4 . 0 9

8 1

Table V.The impact of supply anddemand uncertainties onaverage flow time

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S u p p

l y u n c e r t a i n t y

D e m a

n d

u n c e r t a

i n t y

S u p p l y a n d d e m a n d u n

c e r t a i n t y

D e t e r m i n a n t o f p r o d u c t v

a r i e t y

B a s e c a s e

L o c a l

P e r c e n t

G l o b a l

P e r c e n t

P

e r c e n t

L o c a l

P e r c e n t

G l o b a

l

P e r c e n t

M M

2 , 5

3 7

4 , 2

3 6

6 7

6 , 6

2 8

1 6 1

8 , 9

8 2

2 5 4

1 0 , 2

2 3

3 0 3

1 3 , 2 6

2

4 2 3

U M

2 , 5

3 7

4 , 2

4 5

6 7

6 , 6

4 3

1 6 2

8 , 9

7 5

2 5 4

1 0 , 2

2 5

3 0 3

1 3 , 1 8

4

4 2 0

P M

2 , 5

3 7

4 , 2

3 4

6 7

6 , 6

1 8

1 6 1

8 , 9

8 3

2 5 4

1 0 , 2

0 4

3 0 2

1 3 , 1 4

7

4 1 8

Table VI.The impact of supply anddemand uncertainties on

average system inventory


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inventory increases approximately 67 per cent from the base case when materials arebought from local suppliers. Figure 2(b) shows increases in the average flow time dueto supply lead-time uncertainty. Most notably, the average flow time increases by17 per cent when UMs are bought from local suppliers with uncertain lead times.Recalling the result from the base case experiment when materials are delivered ontime, UM variety contributes less than 1 per cent to the increase in average flow time.This indicates that the presence of supply lead time uncertainty amplifies the negativeimpact of UM variety on flow time.

Buying from global suppliers with greater lead-time uncertainty means that thereare even greater possibilities of materials being delivered earlier or later than expectedtime, resulting in a significant increase in flow time and system inventory as shown

in Figure 2(a) and (b). The increase in average system inventory is in the order of 160 per cent when material that generates product variety is bought from globalsuppliers. Using global suppliers for MMs results in an increase in average flow time inthe order of 50 per cent. The average flow time increases by 45 and 31 per cent whenunique and PMs are bought from global suppliers, respectively.

5.3 Impact of demand uncertaintyDemand uncertainty results in a degree of forecast error and eventually leads todisparity between material ordered and actual production requirements. On someoccasions forecasts will overestimate actual demand. Thus, materials ordered by themanufacturer are greater than the actual production requirement. This means there areexcess materials that have to be held for longer. Material shortages will occur when

forecasts underestimate real demand. In the situation when current on-hand inventoryis insufficient, the production of a batch will be delayed. On-hand inventory will beheld until sufficient material to start production of that batch is available.These situations eventually lead to a higher average system inventory.

Figure 2(a) and (b) show that demand uncertainty results in higher average systeminventory and longer average flow time relative to the base case. The system inventoryincreases by approximately 250 per cent compared to the base case when demand isuncertain. Forecast errors caused by demand uncertainty also affect flow time

Figure 2.The impact of productvariety under uncertainsupply or uncertaindemand on: (a) averagesystem inventory; and(b) average flow time

0

2000

4000

6000

8000

10000

Main material Unique

material

Packaging

materialMain material

A v e r a g e f l o w t i m e (

d a y / u n i t )

Unique

material

Packaging

material

Determinant of product variety Determinant of product variety

A v e r a g e s y s t e m i n v e n t o r y ( u n i t s )

Base Case Local suppliers

Global suppliers Demand uncertainty

0

5

10

15

20

25

30

Local suppliers

Demand uncertainty

(a) (b)

Base Case

Global suppliers

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significantly. Similar to the trends found in supply uncertainty experiments, Figure 2(b)clearly shows that MM variety has the greatest impact on average flow time amongdifferent determinants of product variety in uncertain demand situations. MM varietyleads to an increase in average flow time in the order of 150 per cent when demand is

uncertain. Variety in unique and PM s increases the average flow time by 59 and 46 percent, respectively.

5.4 Impact of supply and demand uncertaintyFigure 3 shows that increasing product variety under both uncertain demand anduncertain supply situations, simultaneously, has a large detrimental impact onperformance. Both system inventory and flow time increase significantly compared tothe base case. The average inventory increases in the order of 300 per cent compared tothe base case when local suppliers are used for main, unique and PMs. Changing fromlocal to global suppliers worsens the performance. The average inventory increases inthe order of 400 per cent. These numbers are far greater than the impact caused bymaterial variety and supply uncertainty under constant demand situations.

In terms of the flow time, similarly damaging impacts are found, although todifferent extents. Again, MM variety has the most significant impact with an increasein average flow time of the order of 160 per cent relative to the base case, due to thecombination of demand uncertainty and local supplier’s lead-time uncertainty. Thisrises to 200 per cent when global suppliers are used. The impact of UM and PM varietyis less severe. The average flow time increases by 62 per cent and 51 per cent,respectively, when local suppliers are used. The numbers rise to 100 per cent and81 per cent when suppliers are global. These impacts are significantly greatercompared to the results when demand are constant.

6. Practical implications

Results from the simulation experiments have several practical implications.As expected, significant levels of product variety result in longer flow times.This supports the findings of Anderson (1995) that severity of setup increases

Figure 3.The impact of producing

product variety underuncertain supply and

demand on: (a) averagesystem inventory; and

(b) average flow time

0

2000

4000

6000

8000

10000

12000

14000

(a) (b)

Main material Unique

material

Packaging

material

Determinant of product variety

Determinant of product variety

A v e

r a g e s y s t e m i n v e n t o r y ( u n i t s )

0

5

10

15

20

25

30

35

Main material

A v e r a g e f l o w t i m e ( d a y / u n i t )

Unique

material

Packaging

material

Base Case

Demand uncertain & local suppliers

Demand uncertain & global suppliers

Base Case

Demand uncertain & local suppliers

Demand uncertain & global suppliers


1133



advantages from cheap production might be negated by “hidden costs” such as expressexpedition, co-ordinating complex networks, customer cancellations due to latedeliveries, etc.

Findings from the simulation study confirm the value of the concept of

postponement, widely advocated in the literature (Lee et al., 1993; Van Hoek, 1999).If variety proliferation can be postponed until later stages in production, disruption of production due to material tardiness can be minimized. Late product differentiationalso allows more precise information on demand to be obtained, so mismatchesbetween supply based on forecasts and actual production requirements can be reduced.

In designing or redesigning their international manufacturing networks, companiesshould also recognise that product variety does not necessarily mean introducingcomplexity across the entire network. Companies may reduce the negative impacts of product variety on supply network operations by choosing and applying strategiesrelevant to their product and process. Product-based strategies (Fisher et al., 1999) thatinclude the use of modular design, standardisation of materials and componentsharing, may allow companies to offer high levels of product variety in the marketplacewhile maintaining a relatively low level of component variety and assemblycomplexity in production. Process-based strategies through the use of flexibletechnology and plant configuration based on the principles of cellular manufacturingmay allow firms to accommodate a high level of variety at a reasonable cost. However,in order to successfully re-design products or processes in the supply chain, the impacton all functions needs to be considered and good co-ordination has to be maintained.

7. Concluding remarksThis paper has presented findings from a simulation study investigating the impact of product variety, supply lead-time and demand uncertainty on MNC supply chainperformance. Clearly, managing product variety that requires different types of

materials in an international setting is very challenging. Results from the experimentsshow that increasing the number of materials options when such materials are criticalfor production results in an increase in the average flow time in the order of 30 per centrelative to static conditions. However, product variety involving materials that arerequired in later stages of production and requiring short set-ups do not have asignificant impact on performance. Offering product variety that requires sourcing of critical materials under demand and supply lead-time uncertainty worsens theperformance of a supply chain relative to a static situation. Sourcing critical materialsfrom suppliers with high delivery uncertainty, results in increases in flow time andsystem inventory in the order of 50 and 160 per cent, respectively. Under demanduncertainty situations, variety in critical materials increases flow time and systeminventory in the order of 140 and 250 per cent, respectively. The worst performance is

found when the system has to handle critical material variety under both demand andsupply uncertainty. Increases in flow time and system inventory are in the order of 200and 400 per cent, respectively. These results highlight the need for careful managementof variety in international operations. They confirm the value of the postponementconcept.

The findings from the study provide important insights on the magnitude of theimpact of product variety in the context of international operations. Opportunities existfor further studies. Incorporating inventory policies into the model may provide further


1135



insights on behaviour. Further, work might also investigate the “shape” of the leadtime distributions in addition to the level variability. In this study, the internationaldimension has concentrated on upstream activities. Incorporating downstreamactivities may add to our understanding of international supply chain management.

Supply networks with multiple manufacturing sites and multiple potential supplyroutes are also of increasing importance. Although simulation in this wider context ischallenging it may provide understanding on how flexibility in production networkscan be exploited to produce higher levels of variety.

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Corresponding authorBart MacCarthy can be contacted at: [email protected]

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