QUANTIFYING THE BULLWHIP EFFECT IN THE SUPPLY CHAIN OF SMALL-SIZED COMPANIES

Transcription

1 Sixth LACCEI International Latin American and Caribbean Conference for Engineering and Technology (LACCEI 2008) Partnering to Succe: Engineering, Education, Reearch and Development June 4 June , Tegucigalpa, Hondura. QUANTIFYING THE BULLWHIP EFFECT IN THE SUPPLY CHAIN OF SMALL-SIZED COMPANIES Martha A. Centeno Florida International Univerity, Miami, USA, [email protected] Jaime E. Pérez UNITEC, Tegucigalpa, Hondura, [email protected] ABSTRACT Thi reearch addreed the Bullwhip Effect (BWE) in the upply chain of mall companie. Identifying way to control the BWE require that it be undertood in the context of mall companie. Thi reearch analyzed quantitatively the impact of the interaction between demand management trategie and forecating method on BWE. Experiment were performed with four management trategie and three forecating technique for different level of eaonality, lead time, and order batching. Cot and an overall BWE index were ued a performance meaure. Reult indicate that no demand management trategy dominate under all condition. However, the collaborative demand management trategie are bet under low eaonality regardle of the forecating method and lead time nature. Higher level of communication do not reult in the ame level of improvement; it depend on the eaonality of the demand. Stochatic lead time do not exacerbate BWE when the item are batched. A quantification methodology i propoed. Keyword: Bullwhip Effect, Small companie upply chain, forecating method, demand management. 1. INTRODUCTION The upply chain may lack tability for everal reaon, including incompetence, miinformation, and doubt. A tangible and obervable phenomenon that indicate the level of tability i the Bullwhip Effect (BWE) which i a metaphor that decribe demand variability amplification baed on mall change of demand at the end-uer point. Several reearcher have concluded that information ditortion i at the center of the root caue of the BWE (Lee et al.,1997), which lead to inefficiencie all over: exceive inventory, poor cutomer ervice, lot revenue, and o forth. Large companie, uch a Walmart and Procter & Gamble, have advanced tremendouly in their effort to reduce BWE. However, globalization ha affected companie of all ize. Large corporation have decentralized and ubcontracted their production need all over the world, creating numerou opportunitie for the BWE to creep in. Thee corporation have been able to urvive the BWE becaue they have ufficient reource. However, mall buinee do not have the ame trength to withtand the BWE, yet, thee firm repreent 99.9 percent of the 25.8 million US buinee reported in 2005 by the Office of Advocacy etimate (SBA, 2006a). In Latin America, Pyramid Reearch etimate the ratio of mall buinee to large corporation i 13 to 1, with more mall buinee to come (Pawling (2005)). Undertanding how BWE affect thee mall companie, and how it can be analyzed and controlled, hould reult in great economic benefit at the micro and macro level. Mot of the previou reearch on the BWE ha focued on demontrating it exitence, identifying poible caue, and providing qualitative method for reducing it impact. In the early 2000, the field aw a hift to how to quantify the effect. In fact, Lee et al. (2004) tate that although the Bullwhip Effect eem well known among practitioner, it i not clear if companie have completely ucceeded in taming it. Further, they tell u that it i neceary to conduct empirical reearch to etimate the magnitude of the BWE in different indutry egment, and identify bet practice. WE1-1

2 Thi reearch ha addreed the BWE in the upply chain of mall companie and ha developed a methodology to detect and quantify it, a well a to evaluate different mitigation trategie. Specific demand management trategie that enhance collaboration were conidered along with ome well known forecating method. The impact of the interaction between the demand management trategy and the forecating method on BWE wa analyzed quantitatively. Thi paper ha been divided into eight ection. Section 2 provide a comprehenive review of the literature on the ubject of thi reearch. Section 3 provide the problem decription. Section 4 decribe the reearch goal and methodology. Section 5 decribe the experimental framework including the cae tudy ued to invetigate the BWE and the experimental model ued to ae the BWE, along with a decription of the input to the model. Section 6 preent the reult of the variou experiment and in-depth dicuion. Section 7 decribe a quantification methodology for BWE propoed for mall company owner. Latly, Section 8 ummarize the main contribution concluion of thi effort. It alo preent poible reearch extenion. 2. LITERATURE REVIEW The BWE i a phenomenon of the upply chain that ha been tudied for many year. The firt academic decription wa given by Forreter (1958) who tried to demontrate it exitence through Sytem Dynamic. A a ucceor to Forreter, Sterman (1989) argued that the main caue for BWE wa the miperception of the feedback. Lee et al. (1997) modeled the operational caue of BWE analytically. They documented the effect of it in a number of pecific buinee (e.g. P&G and HP), identified caue, and propoed cure. In a more recent paper, Lee et al. (2004) dealt with undertanding the caue and managerial implication of the BWE. Unlike Forreter (1958) or Sterman (1989), they developed imple mathematical model of upply chain that reflect the eential apect of the organizational tructure and optimizing behavior of the member. Previou work had uggeted that the effect could be mitigated through modification in behavioral practice (Forreter, 1958) or individual education (Sterman, 1989). However, they demontrated that the BWE i an outcome of the trategic interaction among rational upply chain member. Their concluion i that companie would obtain great benefit by attacking the intitutional and inter-organizational infratructure and their procee. In recent year many author have attempted to quantify the BWE. Metter (1997) expreed the ignificance of the BWE in monetary term. He created a dynamic programming model to minimize total cot in a productioninventory ytem. Hi reult howed that eliminating the eaonal BWE alone (caued by demand updating) can increae the product profitability by 10-20%. Mitigation of demand updating in particular had a greater impact in profit. Chen et al. (2000) tried to quantify the BWE for a upply chain coniting of a ingle retailer and a ingle manufacturer focuing on two caue: demand forecating and order lead time. They concluded that centralizing the demand information can ignificantly reduce the BWE, but they howed that it i impoible for the BWE to be completely eliminated. Lee et al. (2000) developed a model of a two-tage upply chain and attempted to quantify the benefit of information haring. Their reult howed that a manufacturer could reduce inventory level and cot by haring information. More recently, Warburton (2004) developed and olved analytical model, ued numerical integration to repreent inventory behavior, and derived a meaure for the BWE. He alo defined the total BWE from conumer to upplier a the product of the BWE of each tage in the upply chain. Other paper have ued more imulation analyi than analytical modeling to quantify the BWE. For example, Merkuryev et al. (2002) and Merkuryev et al. (2004) preented both tatitical and imulation-baed analyi of the BWE in upply chain. Their imulation reult howed that with centralized information demand ditortion uptream a upply chain i le ignificant than in a upply chain with decentralized demand information. However, they acknowledge that a more thorough tudy i needed to include other factor uch a lead time, forecating technique, etc. Chatfield et al. (2004) ued imulation to perform a erie of experiment that employ factor related to lead-time variation, information quality, and information haring. The reult from the experiment were verified by comparing the oberved variability amplification level to thoe from the analytical work of Chen et al. (2000). Thee reult how the impact of information haring, lead time variation, and information quality on BWE. Recent tudie have focued on analyzing the effect of different trategie to mitigate the BWE. Towill and Diney (2003) compared traditional and Vendor Managed Inventory upply chain, and howed the benefit of VMI in reducing or eliminating ome of the caue of BWE. Aviv (2001) tudied the interaction between inventory and forecating in a two tage upply chain of a ingle product with tochatic demand. Zhang (2004) WE1-2

3 derived expreion for forecat and forecat error etimation for three forecating method and analyzed the cot impact of each method on BWE. Sun and Ren (2005) reviewed literature related to the impact of forecating method on the BWE. They provided management principle for the adequate ue of MMSE, MA and ES. Mot of the reearch to date (2007) ha focued on well integrated upply chain, mainly of large companie. Only recently Govindarajulu (2006) made a tudy on the application of demand chain initiative to mall buinee from the Indian context. He preent a review of two mall buinee in India attempting to explore the potential of applying demand chain initiative. However hi tudy provide only a qualitative approach. Many definition of BWE were found in literature. In thi effort, we ue the definition by Chatfield et al. (2004): BWE i the amplification of demand (or order) variance up the upply chain from cutomer to factory, a demand information pae back through the upply chain. 3. PROBLEM DESCRIPTION The problem that thi reearch effort ha addreed i the order variability amplification, alo known a Bullwhip Effect, in the upply chain of mall companie. The demand amplification that occur during the ordering proce caue unnecearily high inventory level, which may repreent high cot and low liquidity in a buine. Small companie, in many cae, do not have ufficient reource and technology infratructure to urvive attack of the BWE. Faced with thi problem, thee companie need to find way to control it caue becaue it impact may directly affect not only their operation, but at ome point, if not relieved, their bottom line. Identifying way to control the BWE require that it be undertood in the context of mall companie. Thee companie have characteritic that we believe render the form and conequence of the BWE different from thoe of large companie: Low level cah flow buffer, little acce to advanced buine integration tool, and hort-to-medium term relationhip with many upplier. However, a Srinivaan (2004) tate, mall companie have everal deirable characteritic, which may act a a philoophical framework to conider a different approach to the BWE in mall companie, including the following: Ability to rapidly react to market and demand change, agile deciion making due to it imple organization tructure, deciion are made with awarene of their impact on the enterprie, deciion are made with everyone input (mall team), and they can be communicated quickly throughout the enterprie. Many reearcher have tudied the Bullwhip and have dicued many poible caue, mot of which are of behavioral (qualitative) nature. Some of thee effort have been theoretical, ome have been experimental, and yet other have been of analytical nature. Mot tudie related to BWE make reference to large companie (e.g. Procter & Gamble, Walmart, Hewlett-Packard). So they model imple and well integrated upply chain (i.e. two-tage or three-tage upply chain with one or few retailer, manufacturer and upplier), and their propoed mitigation olution are affordable to large companie. 4. RESEARCH GOAL AND METHODOLOGY The goal of thi reearch ha been to quantify and analyze the impact of different demand management trategie on the BWE experienced by the upply chain of mall companie. The impact ha been meaured in monetary term. From thi goal the following tak were performed: A mall buine cae tudy wa defined. A meaure for the BWE from literature wa defined. Model of the mall buine upply chain in the cae tudy were built. Experiment were deigned and performed to tet thi impact. Reult were analyzed and concluion were drawn on the impact of the different demand management trategie on BWE reduction and on a mall buine profit. A general methodology wa developed which can be ued by mall buine owner. The general reearch quetion wa: Which Demand Management Strategy i bet under different condition? Three demand management trategie have been conidered: Independent Forecating Management (IFM), Collaborative Demand Management (CDM), and Flexible Collaborative demand Management (FCM). To anwer the reearch quetion, each trategy wa compared againt an unmanaged upply chain cenario, the Random Forecating Selection (RAN) trategy. WE1-3

4 The goal of the demand management trategie i to have a poitive effect on BWE, i.e. lead to a lower BWE index than that of the unmanaged upply chain cenario. Alo, three forecating method (imple exponential moothing, moving average, and exponential moothing conidering eaonality) were teted to anwer the additional quetion: Which forecating method i bet under different condition? 5. EXPERIMENTAL FRAMEWORK: A cae tudy wa ued tet the reearch hypothee derived from the reearch quetion. The cae tudy repreent a mall company with characteritic to reemble typical real life cenario. Information wa collected from a real life mall company and wa ued a the bai to contruct the cae tudy. The mall company of the cae tudy i referred to a Churro Magic (CM). Churro Magic belong to an international franchie chain, with operation in everal Latin American countrie a well a in the United State. For the purpoe of thi tudy, only one tore wa conidered. The company i dedicated to the ale of gourmet churro with different flavor topping, erved in tray or in bag. In thi reearch, three tage of Churro Magic upply chain have been conidered with one retailer, one ditributor, but multiple (three) manufacturer. The manufacturer produce the main raw material needed for production of churro, namely: wheat flour, cardboard tray, and topping. Thi effort ha ued the index for BWE propoed by Chen et al. (2000): V ( qit ) B = (1) V ( dit ) Where V q ) = Variance of order placed by tage i to tage i+1 at time t. ( it V ( d it ) = Variance of demand experienced by tage i at time t. Traditionally it ha been etablihed that BWE i preent when B > 1. In thi reearch BWE wa meaured uing equation 1, but the interpretation of the numeric value ha been changed. B i a random variable that follow an F 2 2 ditribution. Thu, BWE exit if σ > at a ignificance level α. The deciion i baed on the comparion of the tatitic 2 q 2 d Q σ D B 0 = to a critical value of the F ditribution. If B0 > Fα, n1 1, n2 1, then BWE exit. EXPERIMENTAL DESIGN Simulation wa ued to model the upply chain operation of the cae tudy mall company and to quantify the level of BWE uing different demand management trategie. A full factorial deign wa ued (Table 1). The five factor conidered in the experiment were: 1) demand management trategy (DMS), 2) forecating method (FM), 3) Lead Time nature, 4) eaonality level, and 5) product ordering policy. Their combination generated four different experimental cenario. The repone variable were the overall BWE index (equation 2) and total cot (equation 3). The total upply chain cot for a period t, C Total,, i the um of the cot of placing order, cot of material, cot of holding t inventory, and delivery cot at on all tage for period t. WE1-4

5 Factor Demand Management Strategy (DMS) Forecating Method (FM) Table 1: Factor and level conidered in experimental deign RAN = IFM = CDM = FCM = ESS = SES = MA = Level Random election of forecating method at each tage of the SC Independent Forecating Management Collaborative Demand Management Flexible Collaborative Management Exponential Smoothing Adjuted with Seaonality Simple Exponential Smoothing Moving Average Seaonality Level L = Low H = High Lead Time Nature D = Determinitic S = Stochatic Product Ordering Policy B = Batched N = Non batched B n n B i i= (2) overall = 1 C Total,t = k= 1 C P, t, k + k= 1 C M, t, k + k= 1 C H, t, k + k= 1 C D, t, k Where: C Total,t = Total upply chain cot for period t. C P,t, k = Total cot of placing order in t at tage k. C M,t, k = Total cot of material in period t at tage k C H,t, k = Total holding cot in period t at tage k. C D,t, k = Total delivery cot in period t at tage k. EXPERIMENTAL MODELS Experiment required an operational model and a planning model (Figure 1). Four imulation model were built (Table 2) under the aumption in Table 3. The operational model wa built uing ARENA, and it repreent the flow of order information and product in the upply chain. The planning model wa built uing Excel and help manager at each tage decide how much and when to order uing different forecating, demand planning, and cheduling technique. The period of analyi i one quarter of a year (16 week). Initial inventory level were r, S inventory policy. aumed. All companie in the upply chain ue an ( ) (3) Figure 1: Interaction between experimental ub-model Table 2: Decription of four experimental model Model RAN IFM CDM FCM Decription It repreent the unmanaged upply chain. Retailer ue exponential moothing with trend and eaonality (ESTS), imple exponential moothing (SES), or moving average (MA). The other tage ue either (ESS), SES, or MA. IFM trategy. Each tage make it own forecat of demand but ue the ame forecating method (ESS, SES, or MA). For the retailer ESS extend to ESTS. Low level of communication. CDM trategy. Retailer ue a forecat independent demand. Demand planning i ued to determine order quantitie. Medium level of communication. FCM trategy. Retailer ue a forecating method to predict independent demand while demand planning i ued to determine order quantitie. Flexibility i incorporated by allowing order quantitie to change. Highet level of communication. WE1-5

6 Table 3: Aumption of experimental model 1. Demand pattern how eaonality and trend. 2. There are four eaon (quarter) preent in the yearly ale. 3. Within each eaon, eaonality i alo preent. 4. Phyical Inventory inpection i made at the end of each week. 5. CM order to DC are made at the beginning of every week. 6. All companie in the Supply Chain ue an (r,s) inventory policy. 7. Backordering i not allowed. 8. Material travel from one tage to the other uing the original packaging unit. 9. Each tage j tart week one with an initial inventory level ( I 0 j ). 10. Ued an arbitrary additional 10% in the forecated value at the retail level, a 7% for DC, and a 5% for all manufacturer. Aumption 1) lead to uing Exponential Smoothing with trend and eaonality a the forecating method. Aumption 2) and 3) are baed on empirical obervation (data) and experience with the real company on which the cae tudy i baed. Figure 2 how the input needed by the model. Raw data were analyzed to etablih probabilitic model for thee input of the imulation model. Figure 2: Input needed for the model SELECTION OF FORECASTING METHOD Demand pattern were analyzed to chooe an appropriate forecating method. The regreion analyi performed uggeted that there could be ome eaonality embedded in the data. Thi wa confirmed by the experience of the manager of the cae tudy company who tated that ale vary depending on the week of the month. For the lat and middle week of each month ale increae, uually coinciding with the pay day of mot people who viit our tore. According to management, from experience of previou year there i alo a tendency of ale to decreae from the beginning week of the third quarter to the lat week. Baed on the eaonality analyi, exponential moothing with adjutment for eaonality and trend wa elected a the forecating method for the firt part of experimentation. However, ince both trend and eaonality were not very trong, imple exponential moothing and moving average were alo conidered. 6. EXPERIMENTATION AND DISCUSSION Recall that the general reearch quetion were 1) Which demand management trategy i better under different condition?, and 2) Which forecating method work better for the bet demand management trategy? The experimental model were built to reduce the number of cenario to four, while till have the full factorial experiment: 1) Low Seaonality Determinitic Lead Time, 2) Low Seaonality Stochatic Lead Time, 3) High Seaonality Determinitic Lead Time, and 4) High Seaonality Stochatic Lead Time. WE1-6

7 All experiment ued a ignificance level of 0.05, and when needed, the confidence interval were built at 95% confidence level. The mot relevant reult were hown in the interaction of the demand management trategy and the forecating method. Table 4 ummarize the ranking for thi interaction according to the BWE analyi, wherea Table 5 ummarize the ranking according to cot. Table 4: Ranking for Interaction between DMS and FM (BWE analyi) DMS FCM CDM IFM RAN Condition Low Seaonality High Seaonality FM Determinitic LT Stochatic LT Determinitic LT Stochatic LT Batched Non-Batched Batched Non-Batched Batched Non-Batched Batched Non-Batched ESS MA SES ESS MA SES ESS MA SES ESS MA SES Table 5: Ranking for Interaction between DMS and FM (Cot analyi) DMS FCM CDM IFM RAN Condition FM Low Seaonality High Seaonality Determinitic LT Stochatic LT Determinitic LT Stochatic LT ESS MA SES ESS MA SES ESS MA SES ESS MA SES From the BWE perpective the following concluion can be made: 1. For batched product, the bet demand management trategy under all cenario i FCM combined with MA a the forecating method. 2. For non batched product, there wa a greater interaction between the demand management trategy and forecating method ued. CDM i bet for non batched product under all condition only when MA i ued. But if there are tochatic lead time, then FCM combined with either SES or ESS i better. Further, if there i high eaonality with contant lead time, then IFM. 3. A expected, the trategie that correpond to the highet level of communication and collaboration between companie yield the lowet BWE level. We know two new thing: 3.1. Thee trategie interact better with MA, a together they adjut better to change in demand and variation in lead time The level of the improvement a communication increae i le when dealing with batched product. From a cot perpective, the following concluion can be made: 1. For low eaonality demand, the choice of an appropriate demand management trategy i more important than the forecating method. FCM combined with SES or MA offer the bet BWE and cot. 2. For high eaonality demand, the ituation i revered: the choice of forecating method i more critical than the demand management trategy. ESS howed the lowet cot regardle of the demand management trategy, with RAN combined with ESS yielding the lowet, followed by FCM- ESS. WE1-7

8 In regard to lead time nature (tochatic v. determinitic) and product ordering nature (batched v. non-batched), the following may be concluded: 1. The ranking of the combination are not affected by the nature of lead time. 2. The BWE i tatitically the ame under both tochatic lead time and determinitic lead time. 3. A previou reearch ha aerted, batched product have a ignificantly higher BWE than nonbatched product. 4. The BWE level i le enitive to the random behavior of tochatic lead time when the item under conideration i ordered in batche. The reult of thi effort are conitent with the finding of other reearcher, epecially in regard to the level of communication and collaboration in the upply chain. Higher level of communication lead to lower overall BWE level; hence, upply chain cot are reduced (Lee et al. (2000) and Chatfield et al. (2004)). However, thee work conider only two level of information haring (with and without information haring), wherea we conidered three level. Further, they were not pecific with repect to the type of trategy ued to hare information, wherea we conidered four pecific trategie. None of the previou tudie looked into what would happen to the forecating method performance when combined with a pecific demand management trategy a it wa done in thi effort. Reult alo upport and expand the idea that uing commonly known time erie forecating method, uch a SES and MA, may reult in ignificant reduction in BWE level, a the work by Chen et al. (2000) and Zhang (2004) have aerted. Our reult expand thi idea by howing that for relatively contant (low eaonality) demand, the forecating method i overhadowed by the demand management trategy for batched product. In uch cae, an effective demand management trategy render the forecating method irrelevant. On the other hand, for high eaonality demand, the choice of an appropriate forecating method i more critical and may overhadow the effect of the demand management trategy. From a cot perpective, flexible collaborative trategie that apply Lean Thinking and TOC principle, a the FCM trategy, would ignificantly reduce the total upply chain cot. Flexible collaborative trategie for demand management enable mall companie to react to their dynamic market, and it give them a competitive advantage. 7. BWE QUANTIFICATION METHODOLOGY A BWE Quantification Methodology (BQM) ha been developed to erve mall company manager a a mean of 1) quantifying the BWE, and 2) evaluating different demand management trategie to mitigate BWE. It i intended a a guideline. At the heart of the methodology i data collection. The methodology call for the mall buine manager to be organized and keep record of cutomer demand and material conumption. It alo call for having material decompoition diagram for all product under conideration. It alo call for the retailer to trategically etablih a long term relationhip with the ditribution center, and for the DC to do o with it upplier. The methodology conit of four phae (Error! Reference ource not found.): 1) Set up, 2) Data collection, 3) Analyi framework, and 4) Analyi. 1. The et up phae i baically focued on etablihing the framework for the long term and continuou tracking of BWE. 2. The data collection phae focue on tracking the behavior of demand and conumption and repreenting uch behavior via a probabilitic model. 3. The analyi framework phae focue on deciding which forecating method to u and which demand management trategie to conider. 4. The analyi phae i the evaluation for the variou forecating-demand management trategy combination, both from BWE and cot perpective. The lat tep in it i to chooe the bet combination for the company. If data i collected for more than one period, then ue following performance meaure: the average of the average overall BWE indice and average total upply chain cot for all period conidered. Compare the performance meaure of all alternative trategie againt the performance meaure of the current trategy uing pair-wie WE1-8

9 comparion. Conolidate data for each alternative trategy to perform paired-t tet (analyi Tool-Pack in Excel) and determine the bet alternative. For the purpoe of implementing the methodology, it uffice that the mall buine keep track of the collected data either in a preadheet (e.g. Excel) or in a databae (e.g. Acce). The tracking and deciion making calculation hould be programmed by omeone knowledgeable on baic preadheet formula. 8 CONTRIBUTIONS AND FURTHER RESEARCH The following are the main contribution of thi reearch: 1) Specific demand management trategie that enhance collaboration have been conidered. Thi tudy ha pecifically analyzed the performance of three demand management trategie (DMS) on the BWE. Particularly the impact of flexible collaborative trategie on BWE ha being conidered. 2) More importantly, the impact of the interaction of thee trategie and pecific forecating method on BWE ha been analyzed quantitatively. 3) Analyi wa made in the context of mall companie and the impact of the trategie wa meaured under different level of demand eaonality, nature of lead time, and product ordering policie, making our analyi more robut. 4) Thi effort ha expanded the conceptual tate of the art by providing new knowledge about the BWE behavior: a) Although demand management ha a ignificant effect in reducing BWE beyond jut the forecating method, and the one with higher collaboration in general tend to reduce BWE, no trategy i dominant. b) From a cot perpective, for low eaonality demand, the choice of demand management trategy i more ignificant, regardle of the forecating method ued. c) For mitigating BWE of batched product, FCM i bet under all condition regardle of the forecating method ued. However, the bet performance i achieved with MA. d) For mitigating BWE of non-batched product, CDM along with MA i the bet trategy under all condition. The flexibility in the ordering policy i not a ignificant a in batched product. e) The interaction between the demand management trategy and the forecating method i ignificant in the cae of non batched product and under high eaonality demand pattern. f) The improvement of having a higher level of communication by applying FCM or CDM are higher under low eaonality than under high eaonality demand pattern. 5) A methodology ha been provided to detect, quantify, and mitigate BWE. The BWE Quantification Methodology (BQM) wa deigned a et of guideline to ait mall company manager in detecting and quantifying BWE in the upply chain of their companie a well a in evaluating different mitigation trategie. 6) The BQM provide a more accurate way of detecting the exitence of BWE and a mean of meauring how critical it i, if it i preent. It alo provide a way of teting different demand management trategie to help mitigate it effect and meauring their performance in monetary term. Thi way, manager would be able to jutify invetment made to implement uch trategie in their companie. According to Metter (1997) if the impact of inefficiencie can be meaured and hown to be ignificant they are far more likely to be recipient of managerial attention. The following reearch quetion provide line of thought for poible extenion: How could mall company manager continuouly ae BWE while operating? What other diagnotic tool could be ued to ae the everity of BWE impact in mall companie? What other meaure could be ued to properly ae overall BWE in upply chain? What would happen if each tage ue different inventory policie? If price varied, what further impact, if any, would thi variability have on BWE? What i the correlation of applying demand management trategie and trategie related to the other caue of BWE? What alternative flexible collaborative trategie could help mitigate BWE? WE1-9

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