THE IMPACTS OF DATA INACCURACY ON RETAILER S PERISHABLE INVENTORY

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1 THE IMPACTS OF DATA INACCURACY ON RETAILER S PERISHABLE INVENTORY Mert Bal (a), Alp Ustundag (b) (a) Yldz Techncal Unversty, Mathematcal Engneerng Dept., Istanbul, Turkey (b) Istanbul Techncal Unversty, Industral Engneerng Dept., Istanbul, Turkey (a) mert.bal@gmal.com, (b) ustundaga@tu.edu.tr ABSTRACT Snce pershable products such as fresh produce, dary and meat have a fnte usable lfetme and consttute more than a thrd of the worldwde sales at grocery retalers, controllng the nventores of these products s ncreasngly mportant. Wthout algnment of physcal nventory and nformaton system nventory, nventory nformaton becomes naccurate due to product spolage and the retaler cannot take effcent nventory decsons. The purpose of ths research s to nvestgate how the data naccuracy affects the retaler s nventory performance for dfferent levels of product lfetme, uncertanty of demand and lead tme. Usng a smulaton model, the lost sales, product spolage, average nventory and order quanttes are calculated to nvestgate the retaler s performance over a 365 day perod and ANOVA analyss s performed to examne the results. Keywords: Pershable, Product lfetme, Inventory, Smulaton, Data naccuracy. 1. INTRODUCTION Pershable products such as fresh produce, dary and meat consttute more than a thrd of the worldwde sales at grocery retalers (Broekmeulen and Donselaar, 2009). The lfe of pershable product s a functon of the product s characterstcs and the envronment n whch the product s stored. An effcent cold chan s very essental to ensure that the product remans fresh for the expected duraton. Besde the safety, tmely producton and delvery of pershable goods are very mportant. The rapd and contnuous decayng characterstc of pershables makes the supplers postpone the tme to produce and delver the product as fast as possble. However, a lot of pershable tems exceed the expraton date n retaler s nventory. If the retaler doesn t track the tems n real tme, nventory data wll become naccurate. Therefore, nventory decsons wll be taken wth naccurate data and the lost sales wll ncrease. Accordng to Kendall and Lee (1980), the crtcal objectves of pershable product nventory management are () satsfyng demand by carryng suffcent nventores () holdng down nventory carryng costs () keepng the amount of product spolage (outdatng) at an acceptable level, and (v) mantanng qualty by usng the product whle t s stll fresh, and (v) keepng the cost rotaton low. Effcent nventory management s very crtcal for the retaler to ncrease the proftablty. However, due to the dscrepancy between physcal nventory and nformaton system nventory, the retaler cannot take effcent nventory decsons. Ths study focuses on data naccuracy of the retaler s pershable nventory and nvestgates the mpacts of ths naccuracy on retaler s pershable nventory for dfferent levels of product lfetme, demand and lead tme uncertanty. Usng a smulaton model, the lost sales, product spolage, average nventory and order quanttes are calculated to nvestgate the retaler s performance. Ths paper starts wth the revew of the relevant lterature. Secton 3 and 4 descrbe the smulaton model and the expermental desgn respectvely. Secton 5 analyzes and evaluates the smulatons results. Fnally, Secton 6 presents the conclusons and outlnes further research. 2. LITERATURE REVIEW Most of the exstng nventory models n the lterature assume that tems can be stored ndefntely to meet the future demands. However, certan types of commodtes deterorate n the course of tme and hence are unstable. Inventory problems for deteroratng tems have been studed extensvely by many researchers from tme to tme. There are several lterature revews on pershable nventory system (Nahmas, 1982; Raafat, 1991; Goyal and Gr, 2001). Most publcatons focus on prcng and lot szng models (Ferguson et al., 2007; L et al., 2007; Dye, 2007; Teng et al., 2007; Elmaghraby and Kesknocak, 2003; Chu et al., 1998), or wth 2 echelon systems (Wee et al., 2007, Zanon and Zavanella, 2007; Ferguson and Ketzenberg, 2006; Sarker et al. 2000). Research n ths area started wth the work of Wthn (1957) who consdered fashon goods deteroratng at the end of prescrbed storage perod. One of the focuses of the research on pershable products s nteracton and coordnaton n supply chans (Song et al., 2005). For example, Goyal and Gunasekaran (1995) developed an Proceedngs of the European Modelng and Smulaton Symposum, ; Bretenecker, Bruzzone, Jmenez, Longo, Merkuryev, Sokolov Eds. 241

2 ntegrated producton- nventory-marketng model for determnng the economc producton quantty and economc order quantty for raw materals n a multstage producton system. Yan and Cheng (1998) studed a producton-nventory model for pershable products, where they assumed that the producton, demand and deteroraton rate are all tme-dependent. They gave the condtons for a feasble pont to be optmal. Arcelus et al. (2003) modeled a proft maxmzng retal promoton strategy for a retaler confronted wth a vendor s trade promoton offer of credt and/or prce dscount on the purchase of regular or pershable products. Kanchanasuntorn and Techantsawad (2006) nvestgated the effect of product deteroraton and retalers stockout polces on system total cost, net proft, servce level, and average nventory level n a two-echelon nventory-dstrbuton system, and developed an approxmate nventory model to evaluate system performance. L, Cheng and Wang (2007) developed economc-order-quantty model (EOQ)- based models wth pershable tems to evaluate the mpact of a form postponement strategy on the retaler n a supply chan. Owng to the fact that ths study nvestgates the mpact of data naccuracy on pershable nventory, t wll be meanngful to menton about the past studes about nventory naccuracy. Several researchers focusng on nventory management have dealt wth the effect of nventory errors on supply chan performance, and nvestgated the effect on performance factors of decreasng nventory naccuracy. In ths context, Delanuay et al. (2007) classfed the errors causng nventory dscrepances n supply chans and defned four types of errors. The frst s permanent shrnkage n the physcal stock due to theft, obsolescence, or breakage. The second s msplacement, whch s temporary shrnkage n the physcal stock that can be replaced after every countng or after every perod. The others are the randomness of the suppler yeld and the transacton type. The random yeld of the suppler s a permanent loss or surplus n the physcal nventory due to suppler errors, and transacton type errors affect the nformaton system dfferently than the frst three errors, whch modfy the physcal nventory. Most studes n ths area have used the smulaton method to measure the effects of nventory naccuracy on supply chan performance. Kang and Gerschwn (2005) used smulatons to emphasze the problem of shrnkage n physcal nventory that may ncrease lost sales because of tems beng unexpectedly out of stock. They found that wth an error rate of theft as small as 1% of the average demand, the dsturbance led to 17% of demand lost. Flesch and Tellkamp (2005) examned the relatonshp between nventory naccuracy and performance n a retal supply chan, consderng more error types than the prevous study. They smulated a three echelon supply chan wth one product n whch end-customer demand s exchanged between the echelons, and studed how ncorrect delveres, msplacement, theft, and unsaleable goods affect nventory naccuracy, the out-of-stock level, and the costs related to nventory naccuracy. The results of the study showed that naccuraces caused by theft appear to have the bggest mpact on supply chan performance compared to naccuraces caused by unsaleable goods or low process qualty. Lee et al. (2004) used smulatons to study the effects of nventory accuracy on nventory reducton and level mprovement n a three echelon supply chan consderng the (s,s) polcy decsons, unlke other studes. They found that usng automatc dentfcaton technology, average nventory held decreased by 16%, and total back orders decreased by 22% when the (s,s) polcy decsons are made wth accurate nventory nformaton. Rekk et al. (2008) optmzed an nventory management model by consderng a sngle perod Newsvendor problem wth naccuraces n the nventory. They compared three approaches. In the frst approach, the retaler s unaware of errors n the store. In the second approach, the retaler s aware of the errors and optmzes ts operatons by takng ths ssue nto account. The thrd approach deals wth the case where the retaler deploys an advanced automatc dentfcaton technology to elmnate errors. They concluded that gettng nformaton on msplacement errors and takng them nto account when optmzng the orderng decson can lead to mportant savngs and does not necesstate the deployment of a partcular system, the retaler can beneft from ths mprovement by adjustng hs orderng quantty There are many papers addressng the pershable nventory systems. To the best of our knowledge, there exsts no paper studyng the mpacts of data naccuracy on pershable nventory of whch product lfetme s fnte. In ths paper, we wll nvestgate the mpacts of data naccuracy on retaler s pershable nventory for dfferent levels of product lfetme, demand and lead tme uncertanty to fll ths gap n the lterature. Usng a smulaton model, the lost sales, product spolage, average nventory and order quanttes are calculated to nvestgate the retaler s performance. 3. SIMULATION MODEL In our smulaton model, we worked on a twoechelon supply chan whch s composed of a retaler and manufacturer. Dependng on customer demand, the retaler places orders wth the manufacturer. The manufacturer delvers the product wth fxed product lfetme (t) as soon as possble and doesn t hold any nventory. The Economc Order Quantty (EOQ) method s used as the nventory model at the retaler (Eq 1). The nventory replenshment pont (s) s determned dependng on the average lead tme (L ) and average demand (D ) (Eq 2). Products at the retaler are placed on the shelves based on FIFO prncple. Proceedngs of the European Modelng and Smulaton Symposum, ; Bretenecker, Bruzzone, Jmenez, Longo, Merkuryev, Sokolov Eds. 242

3 2 D c order cnventory EOQ / s (1) L D (2) In our smulaton model, the retaler fulflls the end customer demand daly bass and counts the nventory for dfferent perods (p). It s expected that a gap wll occur between physcal and system nventory snce the retaler s unaware of the outdated products (Y) untl next countng date. Daly closng system nventory (I) s calculated by addng the dfference between the daly openng system nventory and customer demand (D) to daly ncomng tems (Q) from the manufacturer (Eq. 3). Physcal nventory s calculated n the same way, consderng the quantty of outdated products (Eq. 4). As orders are placed dependng on the system nventory level, lost sales occur dependng on the physcal nventory level. At the end of a perod (p), nventory s counted and the data are corrected; thus, the system and physcal nventores become equvalent (Flesch and Tellkamp, 2005): I system I 1 D Q (3) I physcal I 1 D Q Y (4) The model calculates daly lost sales (LS), outdated products (PS) and nventory level (I) for dfferent values of the nput varables whch are nventory countng perod (p), product lfetme (t), standard devaton of lead tme (sl) and demand (sd). At the end of the each 365 days (a year), total lost sales (TLS) as a percentage of total customer demand, total quantty of outdated products (TPS), yearly average nventory (AI) and total order quantty (NQ) are reported annually. The process flow of the smulaton model s llustrated n Fg. 1. Fgure 1. Smulaton model process flow 4. EXPERIMENTAL DESIGN In ths study, the experments are conducted for the products havng very short shelf lves. And n order to nvestgate dfferent levels of nventory naccuracy, the smulaton s run for the cases of that the nventory s counted each day, once a week and once every two weeks (p). The longer the perod of nventory countng, the hgher the data naccuracy occurs n system nventory. The demand and lead tme uncertanty (sd,sl) are determned as 25%, 50% and 75%. The am of ths study s to show how dfferent levels of data naccuracy affect the nventory performance of the retaler under uncertanty of the demand and lead tme. Addtonally, t s also nvestgated how data naccuracy affect the nventory performance when the products have dfferent levels of lfetme. In ths model, end-customer demand s ndependently and dentcally normally dstrbuted, wth a daly average (D ) of 500 tems. The average lead tme of the orders (L ), order cost (c order ) and nventory holdng cost (c nventory ) are chosen to be 2 days, $20 and $ respectvely. Usng the smulaton model, the lost sales, outdated products, average nventory and order quanttes are calculated on Proceedngs of the European Modelng and Smulaton Symposum, ; Bretenecker, Bruzzone, Jmenez, Longo, Merkuryev, Sokolov Eds. 243

4 yearly bass to nvestgate the retaler s performance. Dfferent levels of the ndependent varables of nventory countng perod (p), product lfetme (t), standard devaton of lead tme (sl) and demand (sd) are gven n Table 1. Table 2. ANOVA Tukey Test for nventory countng perod for the retaler Table 1. Levels of ndependent varables Independent varables Levels Inventory countng perod (days) Product lfetme (days) Demand uncertanty (st, %) Lead tme uncertanty (sl, %) EVALUATION OF THE SIMULATION RESULTS The smulatons to examne the mpacts of data naccuracy on retaler s pershable nventory are carred out usng the commercal software Crystal Ball Verson The ndependent factors of nventory countng perod, product lfetme, standard devaton of lead tme and demand were used n 81 (3x3x3x3) combnatons. Addtonally, the smulatons are run 100 tmes for each combnaton to mnmze the possble errors arsng from the random varables. In the smulaton model, the lost sales, product spolage, average nventory and order quanttes are calculated to nvestgate the retaler s performance over a 365 day perod. ANOVA analyss s performed to examne the results of the smulaton model usng SPSS. The rsng level of nventory countng perod ncreases the data naccuracy (Flesch and Tellkamp, 2005). Accordng to the results of a Tukey test of ANOVA post-hoc analyss shown n Table 2, the rsng level of data naccuracy ncreases the dscrepancy between system and physcal nventory and thus the lost sales. Snce the level of retaler s physcal nventory s lower than the level of the system nventory, the retaler fulflls the customer demand at a lower rate. Addtonally the rsng data naccuracy decreases the yearly number of orders placed by the retaler and average nventory level. And the decreasng level of average nventory decreases the yearly quantty of product spolage. When we nvestgate the mpact of data naccuracy for dfferent levels of product lfetme, we notce that the rsng level of data naccuracy ncreases the lost sales. And the ncrease rate n lost sales becomes hgher for pershables wth lower product lfetme, as the trend lnes show (Fg 2). However, the rsng value of data naccuracy decreases the number of orders and consequently the yearly average nventory so decreases. And the decreasng yearly average nventory reduces the quantty of product spolage. Fgure 2 also ndcates that the pershables wth hgher product lfetme have hgher nventory level and consequently less lost sales Proceedngs of the European Modelng and Smulaton Symposum, ; Bretenecker, Bruzzone, Jmenez, Longo, Merkuryev, Sokolov Eds. 244

5 and product spolage. spolage. And Fgure 4 also ndcates that the ncrease rates n lost sales become slghtly greater wth ncreasng lead tme uncertanty, as the trend lnes show. Fgure 2. The mpact of data naccuracy for dfferent values of product lfetme As seen n Fgure 3, hgh level of demand uncertanty ncreases the number of orders and thus the level of nventory. Therefore hgh level of demand uncertanty decreases the lost sales but ncreases the quantty of product spolage due to hgher nventory levels. In our model, when the demand uncertanty exceeds the level of 50%, the number of orders and the average nventory level become much more hgher, and therefore the product spolage ncreases to hgher value. As shown n Fgure 3, the rsng level of data naccuracy ncreases the lost sales and decreases the level of product spolage. Addtonally, wth ncreasng data naccuracy, the ncrease rate n lost sales and decrease rate n product spolage become greater wth hgher demand uncertanty, as the trend lnes show. Fgure 4. The mpact of data naccuracy for dfferent values of lead tme uncertanty In summary, the results of the smulaton study show that the nventory naccuracy s much more crtcal for pershables wth lower product lfetme under hgher demand and lead tme uncertanty. Sellng pershables wth lower product lfetme, the retaler has hgher lost sales and product spolage. The lower the product lfetme, the greater the mpact of data naccuracy s on retaler s nventory performance. Under hgher demand uncertanty, the lost sales become lower but the product spolage hgher. And the data naccuracy affects the retaler s nventory performance much more wth ncreasng demand uncertanty. Under hgher lead tme uncertanty, the retaler becomes hgher lost sales and product spolage. And the ncreasng data naccuracy affects the nventory performance much more wth ncreasng lead tme uncertanty. Fgure 3.The mpact of data naccuracy for dfferent values of demand uncertanty As seen n Fgure 4, hgh level of lead tme uncertanty ncreases the quantty of product spolage. However t decreases the number of orders and thus the level of nventory. Therefore the lost sales are greater n hgher level of lead tme uncertanty. As shown n Fgure 4, the rsng level of data naccuracy ncreases the lost sales and decreases the level of product 6. CONCLUSION Persh ablty of ether raw materals or fnshed products s a major problem for companes. Due to the lmted product lfetme, an neffectve nventory management at each stage n the supply chan from producton to consumers can lead to hgh system costs ncludng orderng costs, shortage costs, nventory handlng costs, and outdatng costs. Wthout algnment of physcal nventory and nformaton system nventory, nventory nformaton becomes naccurate due to product spolage and the supply chan performance decreases. However, the power of nformaton technology can be harnessed to help supply chan members ncrease ther operaton performance (Yu et al., 2001). Advanced Auto-ID technologes can be used to decrease the data naccuracy across the supply chan. Proceedngs of the European Modelng and Smulaton Symposum, ; Bretenecker, Bruzzone, Jmenez, Longo, Merkuryev, Sokolov Eds. 245

6 The purpose of ths research s to contrbute to the lterature on pershable nventory systems by nvestgatng how the data naccuracy affects the retaler s nventory performance for dfferent levels of product lfetme, demand and lead tme uncertanty. Usng a smulaton model, the lost sales, product spolage, average nventory and order quanttes are calculated to nvestgate the retaler s performance. It s shown here that the mpact of data naccuracy on retaler s nventory performance becomes greater wth lower product lfetme. And the data naccuracy affects the retaler s nventory performance much hgher wth ncreasng demand and lead tme uncertanty. In concluson, the results show that decreasng data naccuracy s much more mportant for pershables wth lower product lfetme under hgher demand and lead tme uncertanty to ncrease the nventory performance. In a future study, the mpact of data naccuracy on pershable nventory systems wll be nvestgated on a three echelon supply chan. REFERENCES Arcelus, F.J., Shah, N.H. Srnvasan, G., Retaler s prcng, credt and nventory polces for deteroratng tems n response to temporary prce/credt ncentves, Internatonal Journal of Producton Economcs, Vol (11), Broekmeulen, R.A.C.M. Donselaar, K.H., A heurstc to manage pershable nventory wth batch orderng, postve lead-tmes, and tme-varyng demand, Computers and Operatons Research, Vol. 36 (11), Chu, P.,Chung, K.J., Lan, S.P., Economc order quantty of deteroratng tems under permssble delay n payments, Computers&OperatonsResearch, Vol. 25 (10), Delanuay, C., Sahn, E. and Dallery Y., 2007, A lterature revew on nvestgatons dealng wth nventory management wth data naccuraces, Proceedngs of the 1st RFID Eurasa Conference pp ,İstanbul, (İstanbul,Turkey). Dye, C. Y., Jont prcng and orderng polcy for a deteroratng nventory wth partal backloggng, Omega, Vol. 35 (2), Elmaghraby, W., Kesknocak, P., Dynamc prcng n the presence of nventory consderatons: research overvew, current practces, and future drectons, Management Scence, Vol. 49 (10), Ferguson, M., Ketzenberg, M.E., Informaton sharng to mprove retal product freshness of pershables, Producton and Operatons Management, Vol. 15 (1), Ferguson, M., Jayaraman, V. Souza, G.C., Note: an applcaton of the EOQ model wth nonlnear holdng cost to nventory management of pershables, European Journal of Operatonal Research, Vol. 180 (1), Flesch, E., Tellkamp C., Inventory naccuracy and supply chan performance: a smulaton study of a retal supply chan, Internatonal Journal of Producton Economcs, Vol.95, Goyal, S.K., Gunasekaran, A., An ntegrated producton nventory- marketng model for deteroratng tems, Computers & Industral Engneerng, Vol. 28 (4), Goyal, S.K., Gr, B.C., Recent trends n modelng of deteroratng nventory, European Journal of Operatonal Research, Vol. 134, Kanchanasuntorn, K., Techantsawad, A., An approxmate perodc model for fxed-lfe pershable products n a two-echelon nventorydstrbuton system, Internatonal Journal of Producton Economcs, Vol. 100 (1), Kang, Y., Gershwn, S., Informaton naccuracy n nventory systems: stock loss and stockout, IIE Transactons, Vol. 37, Kendall, K.E., Lee, S.M., Improvng pershable nventory management usng goal programmng, Journal of Operatons Management, Vol. 1 (2), Lee, Y.M., Cheng, F., Leung, Y.T., 2004, Explorng the mpact of RFID on supply chan dynamcs, Proceedngs of the 36th conference on Wnter smulaton pp (Washngton, D.C,USA). L, J., Cheng, T.C. and Wang, S., Analyss of postponement strategy for pershable tems by EOQ-based models, Internatonal Journal of Producton Economcs, Vol. 107 (1), L, J., Wang, S.Y., Cheng, T.C.E., Analyss of postponement strategy by EPQ-based models wth planned backorders, Omega, Vol. 36 (5), Nahmas S., Pershable nventory theory: a revew, Operatons Research, Vol. 30, Raafat, F., Survey of lterature on contnuously deteroratng nventory models, Journal of the Operatonal Research Socety, Vol. 42, Rekk, Y., Sahn, E., Dallery, Y., Analyss of the mpact of the RFID technology on reducng product msplacement errors at retal stores, Internatonal Journal of Producton Economcs, Vol. 112, Sarker, B.R., Jamal, A.M.M., Wang, S Supply chan models for pershable products under nflaton and permssble delay n payment, Computers&Operatons Research, Vol. 27(1), Song, X.P., Ca, X.Q., Chen, J., 2005, Studes on nteracton and coordnaton n supply chans wth pershable products: A revew, Chan, C.K., Lee, H.W.J. (Eds.), Successful Strateges n Supply Chan Management. Idea Group Publshng, Hershey, PA, USA, pp Teng, J.T., Ouyang L.Y., Chen, L.H., A comparson between two prcng and lot-szng models wth partal back loggng and deterorated Proceedngs of the European Modelng and Smulaton Symposum, ; Bretenecker, Bruzzone, Jmenez, Longo, Merkuryev, Sokolov Eds. 246

7 tems, Internatonal Journal of Producton Economcs, Vol. 105 (1), Wee, H.M., Jong, J.F., Jang, J.C., A note on a sngle-vendor and multple-buyers productonnventory polcy for a deteroratng tem, European Journal of Operatonal Research, Vol. 180 (3), Whtn, T.M., 1957, Theory of Inventory Management, Prnceton Unversty Press, Prnceton, NJ. Yan, H., Cheng, T.C.E., Optmal producton stoppng and restartng tmes for an EOQ model wth deteroratng tems, Journal of Operatonal Research Socety, Vol. 49, Yu, Z., Yan, H., Cheng T.C.E., Beneft of nformaton sharng wth supply chan partnershps, Industral Management & Data Systems, Vol. 101 (3), Zanon, S., Zavanella, L., Sngle-vendor snglebuyer wth ntegrated transport- nventory system: models and heurstcs n the case of pershable goods, Computers &Industral Engneerng, Vol. 52 (1), AUTHORS BIOGRAPHY Mert Bal Dr. Mert Bal s a research assstant at the Mathematcal Engneerng Department of Yldz Techncal Unversty. He receved hs B.Sc., M.Sc. and PhD degrees from Mathematcal Engneerng Department of Yldz Techncal Unversty 1999, 2001 and 2008, respectvely. Hs areas of nterest nclude: machne learnng, uncertanty reasonng, artfcal ntellgence, soft computng, rough set theory, formal concept analyss, decson support systems, and smulaton. Alp Üstündağ Assoc. Prof. Dr. Alp Üstündağ s a lecturer at Industral Engneerng Department n Istanbul Techncal Unversty and also the head of RFID Research and Test Lab n the same unversty. He has also worked n IT and fnance ndustry from 2000 to Hs current research nterests nclude RFID, supply chan and logstcs management, rsk management, IT/IS systems, soft computng. He has publshed many papers n nternatonal journals and presented varous studes at natonal and nternatonal conferences and semnars. He has conducted a lot of research and consultng projects n reengneerng, logstcs management and supply chan management. Proceedngs of the European Modelng and Smulaton Symposum, ; Bretenecker, Bruzzone, Jmenez, Longo, Merkuryev, Sokolov Eds. 247