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Int. J. Production Economics 113 (2008) 575–586

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On the benefits of CPFR and VMI: A comparativesimulation study

Kazim Sari�

Department of International Logistics and Transportation, Faculty of Economics and Administrative Sciences, Beykent University,

Sisli Ayazaga Mah., Hadimkoru Yolu Mevkii, 34396 Sisli, Istanbul, Turkey

Received 1 December 2006; accepted 4 October 2007

Available online 10 March 2008

Abstract

This paper aims to help managers of a supply chain to determine an appropriate level of collaboration according to their

specific business conditions. For this purpose, a comprehensive simulation model representing two popular supply chain

initiatives, that are collaborative planning, forecasting and replenishment (CPFR) and vendor-managed inventory (VMI),

is constructed. In addition, a traditionally managed supply chain (TSS) is also included in the model as a benchmark. The

results indicate that benefits of CPFR are always higher than VMI. However, we also realize that under certain conditions,

the gap between the performances of CPFR and VMI does not rationalize the additional resources required for CPFR.

Especially, when the lead time is short and/or when available manufacturing capacity is tight, a careful consideration has

to be given on the selection of an appropriate collaboration mode.

r 2008 Elsevier B.V. All rights reserved.

Keywords: Supply chain collaboration; CPFR; VMI; Simulation

1. Introduction

A supply chain, consisting of several organiza-tions with different and sometimes conflicting ob-jectives, is a complex network of facilities designedto produce and distribute products according tocustomers’ demands. By coordinating differententerprises along the logistics network or establish-ing business partnerships, supply chain management(SCM) is concerned with finding the best strategyfor the whole supply chain (Simchi-Levi et al., 2003,p. 2). Nevertheless, finding the best strategy in thiscomplex network of facilities is not an easy task. It

front matter r 2008 Elsevier B.V. All rights reserved

e.2007.10.021

2 444 1997; fax: +90 212 867 5060.

ss: kazimsari@msn.com

requires intensive communication and coordinationamong trading partners so that material flow alongthe supply chain is optimized as well as informationflow. Fortunately, with the emergence of newmanagement paradigms at the beginning of 1980s,e.g. Lean Thinking, Total Quality Management andPartnership Sourcing Programme, much progresshas been made in the coordination of material flow(Mason-Jones and Towill, 2000; Simchi-Levi et al.,2003, p. 5). However, an equal attention has notbeen paid to the optimization of information flow.This ignorance of the information flow has con-tributed to one important problem in supply chainliterature, which is called ‘‘bullwhip effect’’ (Leeet al., 1997a, b). The bullwhip effect represents thephenomenon where orders to supplier tend to have

.

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a larger variance than sales to the buyer (Lee et al.,1997a, b). In return, high inventory levels and poorcustomer service rates are typical symptoms of thebullwhip effect (Metters, 1997; Chopra and Meindl,2001, p. 1363). Today, SCM researchers indicatethat elimination the bullwhip effect plays a vital rolefor supply chain enterprises to gain competitiveadvantage.

Most of the researchers focusing on remedies forcoping with the bullwhip effect dictate that sharingretail-level information (i.e. point of sales (pos)data) between supply chain members is a prerequi-site for elimination of the bullwhip effect, see e.g.Lee et al. (1997a), Chen et al. (2000a, b), McCullenand Towill (2002), Dejonckheere et al. (2004),Ouyang (2006) and Li et al. (2006). Nevertheless,retailers, most of the time, do not desire to engage ininformation sharing because it provides ignorablelevels of benefits for them, see e.g. Lee et al. (2000),Yu et al. (2001, 2002), Zhao et al. (2002a, b).Therefore, this requires upstream members (e.g.suppliers or manufacturers) to offer incentives forretailers in return for information sharing. Vendor-managed inventory (VMI) and collaborative plan-ning, forecasting and replenishment (CPFR) are thepartnership programs primarily developed to en-courage retailers to share information, see e.g. Leeet al. (1997b) and Disney and Towill (2003a, b).

VMI, also known as continuous replenishment orsupplier-managed inventory, is one of the mostwidely discussed partnering initiatives for encoura-ging collaboration and information sharing amongtrading partners (Angulo et al., 2004). Popularizedin the late 1980s by Wal-Mart and Procter &Gamble (Waller et al., 1999), it was subsequentlyimplemented by many other leading companiesfrom different industries, such as Glaxosmithkline(Danese, 2004), Electrolux Italia (De Toni andZamolo, 2005), Nestle and Tesco (Watson, 2005),Boeing and Alcoa (Micheau, 2005), etc. It is asupply chain initiative where the vendor decides onthe appropriate inventory levels of each of theproducts and the appropriate inventory policies tomaintain those levels. The retailer provides thevendor with access to its real-time inventory level.In this partnership program, the retailer may setcertain service level and/or self-space requirements,which are then taken into consideration by thevendor. That is, in a VMI system, the retailer’s roleshifts from managing inventory to simply rentingretailing space (Simchi-Levi et al., 2003, p. 154;Mishra and Raghunathan, 2004).

VMI offers a competitive advantage for retailersbecause it results in higher product availability andservice level as well as lower inventory monitoringand ordering cost (Waller et al., 1999; Achabal et al.,2000). For vendors, on the other hand, it results inreduced bullwhip effect (Lee et al., 1997b; Disneyand Towill, 2003a, b) and better utilization ofmanufacturing capacity (Waller et al., 1999), as wellas better synchronization of replenishment planning(Waller et al., 1999; C- etinkaya and Lee, 2000).

While many benefits have been identified in theliterature, there are also a number of challenges thatmay exist in practice and that can potentially reducethe benefits obtained from VMI or lead to failures inVMI programs. For instance, Spartan Stores, agrocery chain, shut down its VMI effort about 1year after due in part VMI vendors’ inability todeal with product promotions (Simchi-Levi et al.,2003, p. 161). Similarly, Kmart cut a substantialamount of VMI contracts because Kmart is notsatisfied with the forecasting ability of VMI vendors(Fiddis, 1997). Consequently, many studies have beencarried out to investigate the effectiveness of VMIprograms under different conditions. For instance,Kuk (2004) empirically tested the acclaimed benefitsof VMI programs in electronics industry. Similarly,Sari (2007) used a simulation model to evaluate thebenefits of VMI under different market conditions.Dong and Xu (2002), on the other hand, evaluatedthe value of VMI programs both for suppliers andbuyers. Most of these studies show that ineffectiveusage of retail-level information is one major limita-tion of VMI programs (see e.g. Aviv, 2002; Ovalleand Marquez, 2003; Angulo et al., 2004; Yao et al.,2007). That is, since retailers are closer to themarketplace, they may have better knowledge aboutcustomer behaviors, products and marketplace.However, in most, if not all, VMI programs, thisunique knowledge of the retailers cannot be joinedinto inventory decisions. This is because in a typicalVMI program, retailers are excluded from demandforecasting process. Indeed, in a VMI system, theresponsibilities of the retailers are noting more thansharing sales and inventory data.

CPFR, on the other hand, can solve majority ofthe problems that are encountered in adaptation ofVMI because it requires all members of a supplychain to jointly develop demand forecasts, produc-tion and purchasing plans, and inventory replenish-ments (Aviv, 2002). It is a business practice thatcombines the intelligence of multiple trading part-ners in the planning and fulfilment of customer

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demand (CPFR Workgroup, 2002). CPFR addsvalue to the supply chain in the form of reducedinventory and increased customer service level byachieving better match of demand and supply(Foote and Krishnamurthi, 2001; Aghazadeh,2003; Aichlmayr, 2003; Fliedner, 2003). Nonethe-less, successful implementation of CPFR is not aneasy task. It requires more intensive organizationalresources than VMI as well as mutual trust ofmultiple trading partners (Barratt and Oliveria,2001; Fliedner, 2003). Furthermore, dramaticchanges are also required in usual ways of doingbusiness for CPFR implementation.

Consequently, examination of the previous litera-ture reveals the fact that adaptation of higher levelsof collaboration among members of a supply chaincreates greater benefits for the supply chain. On theother hand, we also see that development andoperational costs of a highly integrated collabora-tion is also higher. That is, while CPFR eliminatesmost of the problems encountered in VMI pro-grams; investment and operation costs of CPFR aresubstantially higher along with greater implementa-tion difficulties. Indeed, these difficulties mightexplain why many of the CPFR programs havenot moved beyond a limited number of productcategories or a small set of trading partners (see e.g.Baird, 2003; Program may build CPFR momentum,2005). Therefore, this trade-off between benefits andcosts of supply chain collaborations creates anurgent need for SCM practitioners to determine theright collaboration level for their supply chains.Today, many SCM practitioners try to determinethe appropriate level of collaboration for theirsupply chains. Here, the following two questionsplay a critical role in determining the rightcollaboration level:

�

Does it is required to invest in CPFR if an earliersupply chain initiative such as VMI, had alreadybeen adopted? In other words, does the gapbetween the performances of CPFR and VMIcompensate the cost of investing in CPFR? � Which factors are influential in answering the

question described above? Do capacity of themanufacturing facility, lead times, or uncertaintyin customer demand influence the desire forCPFR?

To the best of our knowledge, there have beenvery few research studies aiming to explore thesequestions. That is, a few research studies e.g.

Raghunathan (1999), Aviv (2001, 2002, 2007),Ovalle and Marquez (2003) and Disney et al.(2004) represent most of the developments in thisarea. Our paper is different from these previousstudies in three ways. First, most of the models havetended to mainly analytical with some very restric-tive assumptions (e.g. two-stage supply chain,normally distributed or correlated market demands)for the sake of mathematical tractability (e.g.Raghunathan, 1999; Aviv, 2001, 2002, 2007; Disneyet al., 2004). Second, some of the models have beendeveloped so far are only concentrated on forecast-ing part of CPFR (e.g. Aviv, 2001, 2007). Third, asfar as we know, none of the models has exploredCPFR and VMI comparatively in capacitatedmulti-stage supply chains under both stationaryand non-stationary customer demands (e.g. Ovalleand Marquez, 2003) as we have done in this paper.Therefore, this paper contributes to the currentliterature by extending the results of previousresearch studies in a way that managers in a supplychain enterprise can determine an appropriate levelof collaboration for their supply chains.

Unlike many prior analytical studies which havevery restrictive assumptions for the sake of math-ematical tractability (e.g. Mishra and Raghunathan,2004; Lee and Chu, 2005; Yao et al., 2007), we haveused a simulation model in this study to investigatethe benefits of CPFR and VMI under more realisticcircumstances. The simulation approach has beenused extensively in the literature for analyzingsupply chain systems (e.g. Waller et al., 1999; Zhaoet al., 2002a, b; Angulo et al., 2004; Lau et al., 2004;Sari, 2007; Zhang and Zhang, 2007). In this study,we considered a four-stage supply chain, whichconsists of four echelons: a manufacturing plant, awarehouse, a distributor and a retailer. The planthas limited manufacturing capacity and produces asingle product. Each enterprise replenishes itsinventory from its immediate upstream enterprise.

The remainder of this study is organized asfollows. Section 2 clarifies the methodology anddevelopment of the simulation model. Setting ofexperimental design is identified in Section 3,followed by simulation output analysis in Section 4.Conclusions are presented in Section 5.

2. The simulation model

At the initial stages of this research, we intendedto use Microsoft Excel in constructing the simula-tion model; however, research conducted by Keeling

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and Pavur (2004) indicated that it might possible forerrors to occur in the random numbers generated byMicrosoft Excel. Therefore, in order to eliminatethis potential problem, we have used Crystal Ball,an Excel add-in published by Decisioneering. It is apopular risk analysis and forecasting program thatuses Monte Carlo simulation in a spreadsheetenvironment.

According to different situations of informationsharing and ordering information coordination, thepartnership between supply chain members can bedescribed as one of the following integration levels(see Fig. 1). The first structure is a supply chainoperated under traditional ways of doing business(TSS) and the second structure is a supply chainmodel operated under a VMI program. Finally, thethird structure is the supply chain operated under aCPFR program.

In all three supply chain structures, an (R, S)inventory control policy is used for replenishmentdecisions. Here, R indicates the review interval andS indicates the order-up-to level. R is chosen as 1week. Order-up-to level, however, is updated at thebeginning of each week to reflect changes in demandpatterns.

Under TSS, each member strives to develop localstrategies for optimizing his own organizationwithout considering the impact of his strategies onthe performance of other members. Moreover, since

Fig. 1. Three supply c

no information is shared between members, up-stream stages are unaware of actual demandinformation at the market place. That is, whilecreating demand forecasts and inventory plans,supply chain members use only replenishmentorders placed by their immediate downstreammember. Therefore, each member of the supplychain replenishes his own inventory by following aninstallation-based (R, S) policy. Under an installa-tion-based (R, S) policy, each member considers hislocal inventory position. The sequence of eventsfollowed by a supply chain member under TSS isoutlined as follows:

(i)

hain

The member receives the delivery from itsimmediate upstream member, which was or-dered L periods ago (the lead time is L periods).If the member is the plant, L is the productionlead time.

(ii)

The member observes the order placed by itsimmediate downstream member. If the memberis the retailer, the order is the market demand.

(iii)

The member fulfils the customer orders (plusbackorders if there are any) by on-handinventory, and any unfulfilled customer ordersare backordered. The member analyzes thehistorical replenishment orders placed by itsimmediate downstream member for forecast-ing. Based on this demand forecast, the member

structures.

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updates its order-up-to point. If the member isthe retailer, historical market demand data areanalyzed. The order-up-to point of the memberat stage k, Sk, estimated from the observeddemand is as follows (Nahmias, 1997, p. 278):

Sk ¼ F�1k

bk

bk þ hk

� �(1)

where Fk (.) is the distribution function of thedemand realized by the member at stage k.Similarly, bk and hk are backorder and holdingcosts of the member at stage k, respectively.Here, parameters of the demand distribution,Fk (.), are updated at the beginning of eachweek by using the exponential smoothingmethod (see e.g. Nahmias, 1997, p. 74) toreflect changes in demand patterns.

(iv)

The member decides how many units to orderfrom its immediate upstream member. Thequantity of the order is equal to the differencebetween the order-up-to level and inventoryposition. If the member is the plant, a produc-tion order is placed. Here, the plant, because ofits limited manufacturing capacity, cannotalways produce enough to bring its inventoryposition up to the updated value of S. In thesecases, the plant makes full capacity productionby backordering the remaining requirement.This modification of order-up-to policy for thecase of limited production capacity provides anoptimal solution for uncertain demands (seee.g. Gavirneni et al., 1999; Federgruen andZipkin, 1986a, b).

Under VMI, on the other hand, the retailerprovides the distributor with access to its real-timeinventory level as well as its pos data (Fig. 1b). Inreturn, the distributor takes the responsibility ofmanaging the inventories at the retailer. That is,under VMI, the distributor does not only need totake its own inventories into account while makinginventory plans, but also the inventories of theretailer. Therefore, under this structure, the dis-tributor follows an echelon-based policy in hisreplenishment planning. Under the echelon-basedpolicy, the distributor looks at its own inventoryposition plus the inventory position of the retailer,instead of his local inventory position only. For adiscussion of installation and echelon policies, seeClark and Scarf (1960), Axsater and Rosling (1993).All other echelons of the supply chain (the plant andthe warehouse), on the other hand, are operated in

the same way as in TSS. Here, in order to computethe echelon order-up-to levels of the retailer and thedistributor, the heuristic developed by Shang andSong (2003) is used. Again, in this supply chain, theexponential smoothing method is used to update theorder-up-to level at each week.

Finally, under CPFR, inventory levels, pos data,promotion plans, sales forecasts and all otherinformation that may be influential on the marketdemand are shared between supply chain members(Fig. 1c). Consequently, a single joint demandforecast is created by the contribution of eachmember. Here, there is no doubt that demandforecasts created with the joint contributions of allsupply chain members are more accurate than theones created by the individual organizations (e.g.demand forecasts created by the supply chainsoperated under TSS or VMI). Indeed, it is verypossible that the parameters of the demand dis-tribution can be predicted under a CPFR program.Therefore, in the simulation model, it is assumedthat distribution parameters of the market demandare predicted under CPFR by contribution of eachmember. This assumption does not mean that at theend of the collaborative forecasting process, themembers can know exactly what the customerdemand is, but rather they can know what theparameters of the underlying demand distributionare (i.e. if the customer demand is normallydistributed, mean and standard deviation of thecustomer demand is known only). Indeed, thisassumption makes it sure that the promise of CPFRis realized. That is, the large amounts of informa-tion available with CPFR are effectively used tominimize the uncertainty along the supply chain. Ofcourse, in practice, as the model of Disney et al.(2004) also indicates, it might possible that bulk ofinformation available with CPFR result in confu-sion of supply chain managers, which leading lowlevels of supply chain performance. Therefore, thebenefits of CPFR obtained in this study are validonly if CPFR is properly implemented. Moreover,under this collaboration mode, an echelon-based(R, S) inventory policy is used for entire supplychain. That is, inventory positions and inventorycosts of all four supply chain members are takeninto account in replenishment decisions.

The cost structures for the supply chain membersin the simulation model are assumed to be asfollows; the unit backorder costs per week forthe plant, the warehouse, the distributor andthe retailer are $5, $11, $18 and $25, respectively.

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Fig. 2. Histogram of the market demand when a ¼ 15 and

b(t) ¼ 20.

K. Sari / Int. J. Production Economics 113 (2008) 575–586580

The unit inventory costs per week for the plant, thewarehouse, the distributor and the retailer are $0.25,$0.50, $0.75 and $1.00, respectively.

2.1. Retailer’s demand structure

Although normal distribution is more widely usedin supply chain research studies, the g-distribution isused here to represent the customer demand realizedby the retailer. This is due to the fact that there aresome limitations of normal distribution in repre-senting demand structures. For example, normaldistribution allows the occurrence of negativecustomer demand. Therefore, in order to avoid thisunrealistic situation, some restrictive assumptionshave to be included in the model (e.g. Waller et al.,1999; Zhao et al. 2002a, b; Lau et al., 2004). Theg-distribution, on the other hand, does not havesuch problems because it allows only non-negativevalues. Moreover, the g-distribution is flexible inthat it can represent a wide variety of demandstructures. Keaton (1995), for instance, states thatchoosing g-distribution is an effective choice torepresent the demand patterns.

There are two parameters of the g-distribution.These are shape (a) and scale (b) parameters. Themean and the variance of the distribution canbe expressed as ab and ab2, respectively. In thesimulation model, we assume that the shape para-meter of the demand distribution is 15 (a ¼ 15). Thescale parameter (b), on the other hand, is assumedto be a stochastic variable in the form of Eq. (2).

bðtÞ ¼ 20þ season� sin2p52� t

� �(2)

In Eq. (2), b(t) is the scale parameter of theg-distribution in week t. The variability in the scaleparameter of the demand distribution allows us togenerate both seasonal and non-seasonal customerdemands. For example, while assigning zero to theseason constant produces non-seasonal demandpattern, assigning non-zero values results in season-ality in customer demand. A representative histo-gram of the market demand for the selectedparameters is generated in Fig. 2 to clarify thedistribution of the market demand to the readers.

Three demand structures representing differentcombinations of seasonality are used in this study.These are customer demand with no seasonality(SDV), customer demand with medium level ofseasonality (MDV) and customer demand with highlevel of seasonality (HDV). The values of the season

constant for each demand structure are determinedas 0, 2 and 4, respectively. The values of the season

constant are selected in such a way that both non-seasonal and seasonal customer demands withdifferent strengths are generated. For example,while SDV represent the non-seasonal customerdemand, MDV and HDV represent the demandstructures with seasonal swings of the size ofapproximately 10% and 20% of average demand,respectively.

2.2. Verification and validation of the simulation

In order to verify that the simulation programperforms as intended, the conceptual model isdivided into three parts: demand generation anddetermination of total manufacturing capacity,forecasting and production/inventory planning,and order fulfillment and reporting. Each part isdesigned separately so that more efficient andeffective debugging is made possible. Moreover,the combined simulation model is also traced andtested with the results calculated manually.

Later, in order to validate the simulation output,the random demand variables generated in thesimulation model are plotted on a scatter diagram.Then, it is validated that the intended demandstructure is generated. The supply chain modelabove is simulated for 1128 weeks. The initialparameters of the forecasting model are estimatedwith the first 400 weeks of simulation run, which areremoved later from the output analysis to eliminatethe worm-up period effect. Therefore, the rest of thedata are used for effective simulation outputanalysis. In order to reduce the impact of randomvariations, the same random numbers are used tosimulate all three systems. That is, same customerdemand is generated for all types of supply chainsystems. In addition to this variance reduction

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technique, 15 replications for each combination ofthe independent variables are conducted.

3. Experimental design

Four independent factors are considered in theexperimental design. These are; type of supplychain (SCTYPE), available production capacity ofplant (CAP), uncertainty in customer demand (DV)and replenishment lead times (L). The number oflevels of these variables and their values are listed inTable 1.

Factor SCTYPE refers to the way the supplychain is operated. Specifically, this factor indicateswhether the supply chain is operated under TSS,VMI, or CPFR. The factor CAP is expressed as theratio of the plant’s total capacity to the total marketdemand. Total capacity of the plant is distributedto each week, equally. The factor L denotes thereplenishment lead times between each member ofthe supply chain. Finally, the factor DV indicatesthe level of uncertainty seen in market demand.

Two factors are used as dependent variables inthe experimental design in order to evaluate benefitsof CPFR and VMI. These factors are total cost forthe entire supply chain (TSC) and customer servicelevel of the retailer (CSL). TSC is the sum of theinventory holding costs of all members in the supplychain and backorder cost of the retailer. Here, weinclude the backorder cost of the retailer only,because all other backorder costs are internal costswithin the entire supply chain and they are notactually incurred. Factor CSL is the percentage ofcustomer demand satisfied by the retailer throughthe available inventory.

4. Simulation output analysis

The output from the simulation experiments areanalyzed using MANOVA procedure of the SPSS.MANOVA analysis is chosen because it is more

Table 1

Independent factors of the experimental design

Independent factors Levels

1 2 3

SCTYPE TSS VMI CPFR

CAP 1.10 1.30 1.50

DV SDV MDV HDV

L 1 4

appropriate for our model because MANOVAconsiders the correlations between the dependentvariables in the experimental design, see Hair et al.(1998, p. 331). Selected MANOVA results arepresented in Table 2.

MANOVA results in Table 2 show that at 5%significance level, SCTYPE has significant impactson both performance factors, which indicates thatCPFR and VMI have substantial influences on theperformance of the supply chain. The performanceof each type of supply chain is presented in Table 3.

Examination of Table 3 reveals that the reductionin total supply chain cost derived from CPFRsignificantly higher than the reduction derived fromVMI. For example, while CPFR provides 33.90%cost savings, VMI provides 17.34% on the average.Similarly, the results also indicate that CPFRprovides higher level of increase in the customerservice level than VMI does. For example, whileCPFR leads to an increase of 3.84% in customerservice level on the average, VMI results in 1.54%increase in customer service level. Therefore, theseresults lead us to conclude that CPFR producessubstantially higher benefits than VMI in terms oftotal supply chain cost and customer service level.Actually, these findings are simple and intuitivelyexpected for us, so we will not concentrate on themfurther. Instead, we will concentrate on how theperformance increase gained from CPFR and VMIchange in parallel to changes in specific conditionsof supply chains. For this purpose, performance ofCPFR and VMI under various capacity levels(CAP), demand uncertainty (DV) and lead times(L) are produced in Fig. 3.

4.1. Impact of manufacturing capacity (CAP) on the

supply chain collaboration

MANOVA results in Table 2 shows that at 5%significance level, the interaction effect betweenCAP and SCTYPE has significant impacts on bothdependent variables. This means that manufactur-ing capacity has a significant influence on theperformance of CPFR and VMI for all performancemeasures.

Examination of Fig. 3 reveals that both supplychain initiatives better off operating in the environ-ments where larger manufacturing capacities areavailable. That is, we see that contribution of CPFRand VMI is in its lowest level when the plant has itssmallest manufacturing capacity (i.e. CAP ¼ 1.10).For example, when CAP is 1.10, the reductions in

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Table 3

Performances of each type of supply chain

Performance

measures

SCTYPE Average 95% confidence

interval

Lower

bound

Upper

bound

CSL (%) TSS 94.37 94.25 94.50

VMI 95.91 95.78 96.04

CPFR 98.22 98.09 98.35

TSC ($) TSS 699,869 677,720 722,019

VMI 578,451 556,301 600,600

CPFR 462,737 440,588 484,886

Table 2

Selected MANOVA results

Source Dependent variables

CSL TSC (a)

F value Pr4F F value Pr4F

SCTYPE 881.2724 0.0000 424.7569 0.0000

CAP 153.6741 0.0000 24.7766 0.0000

L 508.1819 0.0000 3491.4777 0.0000

DV 627.2433 0.0000 205.2806 0.0000

SCTYPE�CAP 13.7673 0.0000 12.4347 0.0000

SCTYPE�L 50.8457 0.0000 104.2851 0.0000

CAP�L 13.1954 0.0000 3.8058 0.0227

SCTYPE�CAP�L 12.4123 0.0000 8.9866 0.0000

SCTYPE�DV 18.3525 0.0000 5.0148 0.0005

CAP�DV 59.6543 0.0000 27.2560 0.0000

SCTYPE�CAP�DV 9.5052 0.0000 6.2094 0.0000

L�DV 106.4525 0.0000 10.0771 0.0000

SCTYPE�L�DV 6.1199 0.0001 2.6900 0.0302

CAP�L�DV 4.3597 0.0017 5.0067 0.0005

SCTYPE�CAP�L�DV 13.3417 0.0000 6.3501 0.0000

aBased on residual analysis, log transformation of TSC was made to satisfy the assumptions of MANOVA.

K. Sari / Int. J. Production Economics 113 (2008) 575–586582

total supply chain cost for VMI and CPFR arerealized as 14.6% and 19.2%, respectively. On theother hand, when CAP is increased to 1.50, we seethat the reductions for VMI and CPFR dramati-cally jump to 21.0 and 42.3, respectively. This result,consistently with previous studies (e.g. Gavirneniet al., 1999; Lee et al., 2000; Gavirneni, 2002;Simchi-Levi and Zhao, 2003; Lau et al., 2004),shows that the contributions of supply chaincollaborations are significantly higher when largerlevels of manufacturing capacity is available.

In addition, examination of Fig. 3 also reveals thatthe distinction between CPFR and VMI reaches itsmaximum level when the capacity ratio is in its

highest level (i.e. CAP ¼ 1.50). For example, whilethe difference between the percentage of costreductions provided by CPFR and VMI is around22% when CAP is 1.50, it is realized around 4% onlywhen CAP is 1.10. Therefore, this result clearlyshows that choosing to implement a CPFR programrather than a VMI program provides substantiallyhigher benefits for the supply chain where largerlevels of manufacturing capacity are available. Inother words, this shows us that under the conditionswhere available manufacturing capacity is tight, acareful consideration has to be given on the selectionof an appropriate collaboration mode. That is, underthe conditions where the manufacturing capacity istoo tight, additional performance increase providedby CPFR over VMI may not justify the higherimplementation and operational cost of CPFR.

4.2. Impact of demand uncertainty (DV) on the

supply chain collaboration

MANOVA results in Table 2 show that at 5%significance level, the interaction effect between DVand SCTYPE has significant impacts on bothdemand variables. This means that uncertainty inmarket demand has a significant influence on CPFRand VMI for all performance measures.

Examination of Fig. 3 reveals that higher levels ofdemand uncertainty results in substantial decreases

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Fig. 3. Overall performance of CPFR and VMI.

K. Sari / Int. J. Production Economics 113 (2008) 575–586 583

in the cost savings derived from VMI. For example,the reduction in total supply chain cost decreasesfrom 24% to 15% when the uncertainty in thecustomer demand (DV) changes from low level(LDV) to high level (HDV). On the other hand,same level of increase in the demand uncertaintydoes not produce that much performance reductionin CPFR. That is, it results in approximately 3% ofreduction in total supply chain cost. In addition tothis, Fig. 3 also shows that the supply chainoperated under CPFR produces higher customerservice level under all levels of the demanduncertainty. Similarly, the gap between the custo-mer service levels of VMI and CPFR is in its highestlevel when the uncertainty in market demand is inits highest level. For example, we see that, whileCPFR produces 1.52% of higher customer servicelevel under low level of demand uncertainty(DV ¼ SDV), it is increased to 2.90% when thereis high level of uncertainty in the market demand(DV ¼ HDV).

The results obtained here lead us to conclude thatcompared with a VMI system, the value of CPFR issubstantially greater under the market conditions wheredemand variability is high. Therefore, SCM practi-tioners have to be more eager to implement CPFRprograms under more volatile market conditions.

4.3. Impact of lead times (L) on the supply chain

collaboration

MANOVA results in Table 2 show that at 5%significance level, the interaction effect between L

and SCTYPE has significant impacts on bothperformance factors. This means that lead timeshave a significant influence on CPFR and VMI forall performance measures.

Examination of Fig. 3 reveals that CPFR andVMI exhibit different performance levels underdifferent replenishment lead times. That is, whilethe reduction amount in total supply chain costunder CPFR substantially increases as the replenish-ment lead times increase, the supply chain costsavings gained from VMI do not change signifi-cantly. For example, when the supply chain operatedunder CPFR is considered, it is seen that thereduction in total supply chain cost dramaticallyincreases from 20% to 40% as lead times increasefrom 1 to 4 weeks. On the other hand, in case ofVMI, it is apparent that any level of increase ordecrease in replenishment lead times does not resultin significant changes in the reduction of total supplychain cost. Furthermore, when the customer servicelevel is considered, we see that under all levels of leadtimes, CPFR produces higher level of fill rate thanVMI does. There is, in addition, one further point tomake that the gap between the service levels achievedby CPFR and VMI substantially increase as thereplenishment lead times increase.

The results obtained here imply that comparedwith a VMI system, the value of CPFR issubstantially greater under the conditions wherereplenishment lead times are longer. Therefore,SCM practitioners have to be more eager to investin CPFR instead of VMI in supply chains wherelead times are long.

ARTICLE IN PRESSK. Sari / Int. J. Production Economics 113 (2008) 575–586584

5. Conclusion

This paper comparatively investigates the perfor-mance increase obtained from VMI and CPFR in afour-stage supply chain under both stationary andnon-stationary customer demands viva a compre-hensive simulation study. Through comprehensivesimulation experiments and subsequent statisticalanalysis of the simulation outputs, we make thefollowing three important observations.

First, we observe that the benefits gained fromCPFR are always higher than that of VMI under allconditions considered in this study. That is,compared with VMI, CPFR produces lower totalsupply chain cost as well as higher customer servicelevels. Therefore, from this study, we may sure andclear about the fact that the managers of a supplychain enterprise better off investing in CPFR.

Second, through simulation output analysis, weobserve that the performance increase gained fromCPFR and VMI significantly depends on threefactors. These are capacity tightness of the plant,replenishment lead times and uncertainty in marketdemand. As these factors get different levels, thebenefits obtained from both initiatives also changesubstantially. Moreover, the gap between theperformance improvements produced by CPFRand VMI also changes significantly. For example,we observe that when the lead times are short and/or where available manufacturing capacity is tight,the benefits of switching from VMI to CPFR are atits lowest value. That is, contribution of switchingto CPFR is almost ignorable when we consider theadditional resources required for CPFR adaptation.The managerial implication of this finding is greatbecause nowadays, adaptation of every businesspractice, which is in fashion, is popular withoutanalyzing the suitability of it for specific businessconditions. Therefore, this research indicates that itis of highly importance to make careful benefit/costanalysis to invest in CPFR under the conditionswhere lead time is short and/or where availablemanufacturing capacity is very tight.

Finally, we recognize that there are substantialdecreases in the performance of VMI as the un-certainty in customer demand increases. On theother hand, we also recognize that there is only aslight decrease in the performance of CPFR underhigher variable customer demands. Thus, indicatingthat highly variable customer demand results inwidening the gap between the performances ofCPFR and VMI. This is because of the fact that

supply chain members may better manage theuncertainties through joint forecasting and inven-tory planning under CPFR. Therefore, one othermanagerial implication that can be drawn from thisfinding is that the practitioners have to invest inCPFR as soon as possible in the industries in whichdemand uncertainty is highly variable. The compu-ter industry, for instance, with its very short productlife cycle and highly variable customer demand, is agood example to industries where the adaptation ofCPFR is an urgent need.

Although this study provides important insightsinto CPFR and its relationship with VMI, we haveto state that there are some limitations of this study.First, we consider a serial supply chain structurewith one member at each echelon. This supply chainstructure is only a simplified case and in futureresearch studies, modeling more realistic supplychain structures may better explain and extend theresults obtained from this research. Second, weassume that the members in the supply chain applyorder-up to policies to make their production/inventory decisions; however, there are other typesof inventory/production policies that can be in-cluded in the model. Third, the cost structure usedin the simulation model only represents one specialcase.

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