Uncertain Supply Chain Management

Uncerain Supply Chain Manaemen 4 (2016) 1 16 Conens liss available a GrowinScience Uncerain Supply Chain Manaemen homepae: www.growinscience.com/uscm Effec of cusomer demand informaion sharin on a four-sae supply chain performance: an experimenal sudy T. Chinna Pamuley a and V. Madhusudanan Pillai b* a Research Scholar, Deparmen of Mechanical Enineerin, Naional Insiue of Technoloy Calicu, Calicu 673 601, Kerala, India b Associae Professor, Deparmen of Mechanical Enineerin, Naional Insiue of Technoloy Calicu, Calicu 673 601, Kerala, India C H R O N I C L E A B S T R A C T Aricle hisory: Received April 20, 2015 Received in revised forma June 10, 2015 Acceped Sepember 31 2015 Available online Ocober 7 2015 Keywords: Supply chain Bullwhip effec Performance Cusomer demand informaion sharin Cusomer Demand Informaion (CDI) sharin plays a vial role in reducin he bullwhip effec as well as in improvin he performance of a supply chain. The objecive of he presen research is o idenify he bes form of CDI sharin experimenally for a four-sae supply chain under los sales business environmen. A supply chain role play ame sofware packae is developed for conducin suiable experimens. Differen forms of CDI sharin esed in his research are periodic CDI, hisory of CDI and CDI in he form of disribuion. I is found ha all forms of CDI sharin have sinifican impac on he reducion of bullwhip effec compared o non-sharin of informaion and he upsream saes in he supply chain are benefied he mos under CDI sharin. The saisical analysis also confirms ha sharin CDI in he form of disribuion is he mos effecive amon he various forms of informaion sharin sudied. The percenae reducions in maniude of order variance under he mos benefied informaion sharin a disribuor and facory saes are 64.43 and 66.04, respecively. I is also found ha he performance of a supply chain depends on he deree of cusomer demand informaion shared amon he saes in he supply chain. 2016 Growin Science Ld. All rihs reserved. 1. Inroducion Supply chain consiss of all paries involved, direcly or indirecly, in fulfillin a cusomer reques (Chopra e al. 2007). Amplificaion of demand variabiliy from downsream sae o upsream sae in a supply chain is referred as bullwhip effec (Lee e al., 1997). Forreser (1958) is he firs o repor on his phenomenon. Bullwhip effec (BWE) has been observed in he commercial operaions of many indusries and a few of hem are: Texile indusry (Zymelman, 1965), Hewle-Packard (HP) (Lee e al., 1997), Procer & Gamble (P&G) (Lee e al., 1997), Auomoive componen supply chain in UK (Taylor, 2000), Machine ool indusry (Anderson e al., 2000), Clohin supply chain (Disney & Towill 2003), Grocery reailer in Unied Kindom (Ge e al., 2004), Phillip Elecronics (Kok e al., 2005), Semiconducor equipmen indusry (Terwiesch e al., 2005), Campbell Soup (O donnell e al., 2006) and Wal-Mar (Bhaacharya & Bandyopadhyay, 2011). This phenomenon is also ermed as whip- * Correspondin auhor Tel. : 09895367804 E-mail address: vmp@nic.ac.in (V. Madhusudanan Pillai) 2016 Growin Science Ld. All rihs reserved. doi: 10.5267/j.uscm.2015.10.001

2 lash or whip-saw effec (Lee e al., 1997) and insabiliy problem (Ouyan & Li, 2010) by some indusries. Generally, increase in variance of orders creaes excessive invenories or shoraes, poor cusomer services due o unavailabiliy of producs or lon backorders, insufficien or excessive capaciies, or unsable or uncerain producion plannin. Thus, he presence of bullwhip effec in a supply chain is cosly (Meers, 1997), harmful (Wrih & Yuan, 2008) and decreases he efficiency of he supply chain (Sucky, 2009). Reducion in bullwhip effec showed sinifican improvemen in he profiabiliy of he whole supply chain (Meers, 1997; Caloiero e al., 2008; Boani e al., 2010). The causes of he bullwhip effec are: (i) Lack of cusomer demand informaion (Serman, 1989), (ii) Flaws in demand forecas updain, order bachin, variaion in prices, raionin and shorae amin (Lee e al., 1997), (iii) Lead ime (Chen e al., 2000; Simchi-Levi e al., 2008; Wan e al., 2008), (iv) Replenishmen rule (Dejonckheere e al., 2003; Disney & Towill 2006), (v) Behavioural aspecs of manaers (Croson & Donohue, 2006; Nienhaus e al., 2006), (vi) Overesimaion (Sucky 2009), (vii) Capaciy limi and number of levels in a supply chain (Paik & Bachi 2007), and (viii) Free reurn policies, inflaed orders, no communicaion and no coordinaion up and down in a supply chain, overreacion o backlos, and produc promoions (Buchmeiser e al., 2008). Lack of cusomer demand informaion leads o demand/informaion disorion as one move from downsream o upsream saes and as a resul he requiremens a upsream saes are difficul o esimae (Serman, 1989). So he sharin of Cusomer Demand Informaion (CDI), which is he informaion relaed wih he demand ha he reailer faces, can ive proecion from he uncerainy and is essenial for reducin he bullwhip effec (Chen e al., 2000). Differen forms of CDI sharin include sharin each period cusomer demand, hisory of cusomer demand and disribuion of cusomer demand. Informaion reardin fuure cusomer demand is called Advance Demand Informaion (ADI). If i is wih cerainy, i is perfec ADI oherwise ermed as imperfec ADI (Tan e al., 2007). In his sudy, sharin he disribuion of cusomer demand in advance is called imperfec ADI. Performance of a supply chain can be invesiaed by analyical, experimenal or simulaion mehods. Beer disribuion ame is used o evaluae he performance of a supply chain experimenally in which each sae is manaed by a human bein. The beer disribuion ame is a simulaion of he supply chains wih four saes viz. reailer, wholesaler, disribuor and facory; he deails of which are repored by Serman (1989 and 2009). The experimenal sudies reviewed in he presen sudy are iven in Table 1. The deails such as he srucure of supply chain, lead ime, demand disribuion, business environmen, performance measure(s) used and he key findins of each sudy are summarized in Table 1. There are a ood number of repors on he analyical sudies in describin he effec of lead ime on bullwhip effec. Meers (1997) repored lead ime as he overridin cause of bullwhip effec. Lon lead ime is one of he causes of bullwhip effec (Lee e al., 1997) and i also increases he complexiy of decision makin (Wu & Kaok, 2006). Bullwhip effec was found hih under lon lead ime (Simchi- Levi e al., 2008). Sudy by Wan e al., (2008) on he impac of lead ime on bullwhip effec and oal invenory showed ha flucuaion in orders are more if he lead ime is lon, and hus he bullwhip effec is eviden. Lon lead ime in he saes of a supply chain increases he oal invenory. Reducion in lead ime reduces he bullwhip effec more han he informaion sharin (Arawal e al., 2009). The above lieraure survey shows ha lead ime is one of he major causes of bullwhip effec. If he ime beween order and delivery is lare, i can ac as a major reason for confusion a he mind of decision maker while placin he order. Order placed a one poin, enerally, mees he demand of some disan fuure period(s). So, lead ime can conribue considerably o variaion in orders. Similarly, backorders are a major conribuor of variaion in order sizes. Due o backorders, he replenishmen quaniy varies considerably and as a resul he order placed may also vary. Mos of he previous researchers in heir experimenal sudies have considered lon lead ime (4 periods) and uniform disribuion wih a rane of 0 o 8 or sep-up demand paern for he cusomer demand (Serman, 1989; Croson & Donohue, 2003, Croson & Donohue, 2006, Wu & Kaok, 2006); assumin a uniform

T. Chinna Pamuley and V. Madhusudanan Pillai /Uncerain Supply Chain Manaemen 4 (2016) 3 cusomer demand is unusual (Seckel e al., 2004) and normal disribuion is he bes choice for he same (Chan & Chan, 2010). Table 1 Experimenal sudies reviewed in he presen sudy Auhor and year Serman (1989) Croson and Donohue (2003) Seckel e al. (2004) Croson and Donohue (2005a) Croson e al. (2014) Croson and Donohue (2006) Wu and Kaok (2006) Nienhaus e al. (2006) Canor and Kaok (2012) Type of informaion sharin No sharin Imperfec ADI Poin of Sale (PoS) wih imperfec ADI Performance measure used BWE, Toal Cos of he Supply Chain (TCSC) Cusomer Demand Sep-up Lead ime Four weeks for reailer, wholesaler and disribuor Three weeks for facory BWE U (0,8) Same as in Serman (1989) PoS TCSC Sep-up S-shaped wih saionary disurbances Errorless S- shaped paern No informaion Downsream invenory Upsream invenory Coordinaion sock Common knowlede Imperfec ADI Invenory informaion Learnin, rainin and communicaion Informaion sharin (sock in all saes and allowed o cha wih players) Non-sharin of informaion Aen based sraey Under four weeks and wo weeks BWE U (0,8) Same as in Serman (1989) BWE and TCSC Consan demand of 4 unis per week Same as in Serman (1989) BWE U (0,8) Same as in Serman (1989) BWE U (0,8) Same as in Serman (1989) TCSC Sep-up Three weeks Imperfec ADI BWE U(0,8) Two weeks for reailer, one-week for facory Business environmen Supply chain Foursae Foursae Threesae Foursae Foursae Foursae Foursae Foursae Twosae Key resuls Mispercepion in feedback leads o BWE Lare variaion in orders is due o lack of cusomer demand informaion PoS wih imperfec ADI was no shown sinifican impac on BWE han imperfec ADI sharin Reducion in lead ime more beneficial han sharin PoS daa Impac of downsream invenory informaion sharin is sinifican Coordinaion risk is anoher behavioural cause of bullwhip effec Invenory informaion sharin sinificanly reduced he BWE Trainin and communicaion sinificanly reduced he BWE Informaion sharin improves he performance of he supply chain Safe-harbour and panic sraey of human players are he causes of BWE Simplifyin he srucure of he supply chain leads o producion smoohin Carer e al. (2007), Bendoly e al. (2010), Tokar (2010), and Canor and Kaok (2012) hihlihed he need for behavioural research in loisics and supply chain manaemen in which human beins are used for conducin experimens. They concluded ha he behavioural research in loisics and supply chain manaemen can sinificanly advance boh heory and pracice in he loisics and supply chain manaemen. Hence, he auhors of his paper are moivaed o evaluae he performance of supply chain by conducin he experimens usin human beins a a small lead ime of one period under los sales business environmen. This experimenal work is differen from he research works repored in he lieraure in erms of lead ime, demand disribuion and is maniude and variabiliy, and business environmen. One of he objecives of his work is o es he presence of bullwhip effec experimenally under CDI sharin in a small lead ime (one period) and no backorders environmen. No backorders

4 are assumed because cusomers do no wish o wai for heir needs in he presen compeiive world and backorder is also one of he causes of he bullwhip effec (Pillai & Pamuley, 2013). The second objecive is o measure he performance of he supply chain usin various performance measures viz. variance of orders placed by each sae, oal invenory a each sae and oal invenory of he supply chain. The hird objecive is o idenify he mos effecive ype of CDI sharin. Here, hree ypes of informaion sharin are considered such as: (i) periodic CDI (ii) hisory of CDI (iii) CDI in he form of a disribuion. Furher, he sudy is aimed o idenify he benefied saes in he supply chain under differen forms of CDI sharin. Suiable hypohesis are formulaed for esin he above objecives. This paper is oranized as follows: Feaures of supply chain role play ame sofware packae developed are discussed in Secion 2. Secion 3 ives he deails of experimenal seins in he packae for he presen sudy. Assumpions and experimenal procedure are described in Secion 4. Resuls are iven in Secion 5 and Saisical ess conduced are explained in Secion 6. Secion 7 provides discussion and conclusions of he sudy are iven in Secion 8. 2. Feaures of supply chain role play ame sofware packae developed Generally, Beer disribuion ame is used for evaluain he performance of a supply chain experimenally under backorder cases. For meein he objecives of presen sudy, flexibiliy in sein he parameers of a supply chain is required and hus a cusomized supply chain role play ame sofware packae is developed. The deails and feaures of his packae are explained below. The suiabiliy of he sofware packae for experimens in supply chain manaemen is analysed and validaed wih he resuls available in he lieraure and are available in Pillai and Pamuley (2013). In a four-sae supply chain, four paricipans are required o form a eam or a supply chain and each paricipan acs as manaer of a sae. The four saes in he supply chain are reailer, wholesaler, disribuor and facory. This ame is played in neworked compuers where each paricipan has a clien compuer and manaes his/her role in he supply chain. A maximum of 10 eams can paricipae in he role play a a ime. An insrucor or admin se he parameers of he supply chain and assin roles o each paricipan. Various parameers of he supply chain o be se are: (i) number of supply chains (In he packae, i is iven as number of ames), (ii) ype of informaion sharin beween he saes (ame ype), (iii) cusomer demand (The packae has demand eneraors followin normal or uniform disribuions. Desired demand daa manually also can be se in he packae.), (iv) maximum number of periods for he ame play, (v) ime required o reach he order from lower sae o he nex hiher sae (order lead ime), (vi) ime required for he shipmen quaniy o reach he immediae downsream sae (delivery lead ime), (vii) iniial invenory, (viii) performance evaluaion period and (ix) holdin cos per uni per period a each sae. The screensho showin he inerfaces for sein he above parameers are iven in Fi. 1 and Fi. 2. This web based sofware packae uses Hyper Tex Markup Lanuae (HTML), Cascadin Syle Shees (CSS) and Java Scrip and MySQL. HTML, CSS and Java Scrip are used as fron-end for his sofware and MySQL is used as back-end. Afer sein he parameers, paricipans can loin ino he respecive supply chain sae wih password eneraed by he sofware packae. Reailer places order o wholesaler, wholesaler o disribuor, disribuor o facory and facory issues producion orders. A reailer sae window a week 1 is shown in Fi. 3 which conains periodic deails such as (i) cusomer order (demand), (ii) backorder quaniy (his quaniy has sinificance, if he ame is played under backorder environmen), (iii) Reailer invenory (on-hand invenory), (iv) replenishmen quaniy and (v) ousandin orders which will be useful for akin decision on he size of order o be placed. The order decision is recorded alon wih invenory saus for each period. This play run for several periods and he recorded daa is used o evaluae he performance of he supply chain.

T. Chinna Pamuley and V. Madhusudanan Pillai /Uncerain Supply Chain Manaemen 4 (2016) 5 Fi.1. Inerface for sein he supply chain parameers - pae 1 Fi. 2. Inerface for sein he supply chain parameers - pae 2 3. Deails of experimenal seins in he packae for he presen sudy In his sudy, he experimens are conduced by sein he supply chain under differen informaion sharin seins such as: (i) non-sharin informaion, (ii) sharin periodic CDI, (iii) sharin hisory of CDI and (iv) sharin imperfec ADI. The screensho of a reailer sae under non-sharin of informaion sein is shown in Fi. 3 and screensho for oher informaion sharin seins of a wholesaler sae are provided in Fi. 4, Fi. 5 and Fi. 6 respecively. The iniial parameer sein such as order lead ime, delivery lead ime, cusomer demand disribuion, iniial invenory, ec. is he same for all experimens bu he ype of CDI shared differs. Fi. 3. Screensho of a reailer sae of a supply chain (nonsharin of informaion sein) Fi. 4. Cusomer demand per period sharin Fi. 5. Hisory of cusomer demand informaion sharin Fi. 6. Imperfec advance demand informaion sharin The ineracion beween wo consecuive saes is in erms of orders placed under non-sharin of informaion whereas in all oher seins appropriae CDI is shared in addiion o order informaion (see Fis. 4 6). Cusomer demand occurred a he reailer a each period is shared wih all oher saes in he periodic CDI sein. Cusomer demand a he reailer is abulaed and is updaed a each period which is shared wih all oher saes in he hisory of CDI sharin. The shared informaion is updaed and is displayed a each period in he above informaion sharin seins. The name and parameers of cusomer demand disribuion is shared in he imperfec ADI sein experimen. The shared informaion can be used for akin he decision on order size. The CDI was shared insead of Poin of Sale (PoS) as he PoS may conain only he demand me from sock. (When he experimen is conduced under los sales, where demand is reaer han invenory, he PoS canno represen he acual cusomer demand). In he e-commerce marke, i is possible o obain CDI easily. Under each sein, 9 supply chains are evaluaed and 144 members paricipaed in his experimenal sudy. These members are

6 under-raduae and pos-raduae sudens, and research scholars of Indusrial Enineerin and Manaemen specializaion. 4. Assumpions and experimenal procedure The followin assumpions and procedure are applicable o all supply chains (9 supply chains in each experimenal sein). Cusomer demand for a sinle produc is eneraed randomly a each period which follows normal disribuion, N (20, 5). When he reailer receives cusomer order, a decision reardin he size of he order o be placed o is nex hiher level is aken. I is assumed ha he order decision a a sae is made a he end of a period as his decision is aken afer shippin he demand quaniy for he period. The order size decision is aken wih he objecive of meein he demand and minimizin he invenory. A any sae he shipmen quaniy o is downsream is based on he availabiliy of sock. Similar ype of decision is aken in every period a each sae bu, he facory sae issues he producion order based on he order received and on-hand invenory. The order placed by a sae (i = 1, 2, 3) a he end of ime period reaches is supplier a he beinnin of period (+1) and he shipmen made by supplier a he beinnin of (+1) reaches is cusomer a he beinnin of (+2). I is assumed ha he facory (i = 4) has unlimied producion capaciy and resources for producion. Hence, he producion quaniy aains he producion order issued by facory a he end of period is available wih he facory for disribuion a he beinnin of (+1). Fi. 7 shows he shipmen and order flows in a supply chain. A each sae, a period beins wih he arrival of shipmen from is upsream sae and hen receives orders from is downsream sae. Iniial invenory a each sae is se so ha i could saisfy he demand expeced ill i receives firs replenishmen order from is supplier. Iniial invenory a each sae in he supply chain a he beinnin of he ame is 40 unis. The orders received are me from he available invenory and he remainin invenory is carried over o he nex period. The quaniy ha is no me is considered as los sales. A period ends when each paricipan places an order wih his/her upsream sae. The duraion of he experimen was no revealed o he paricipans and i was conduced for 55 periods. The daa from period 7 o 48 are considered for performance evaluaion as in Seckel e al. (2004). The supply chain parameers under which he experimens conduced are iven in Table 2. The performance measures considered in his sudy are variance of orders (BWE), oal invenory a each sae and Toal Invenory of he Supply Chain (TISC). TISC is he sum of he invenory a all saes of he supply chain. The variance of orders, oal invenory a each sae and he TISC are calculaed by he sofware packae usin he equaion 1, 2 and 3, respecively. Noaions used in he equaions are iven below. Noaions: i Sae index in a supply chain, i = 1, 2, 3, 4 Supply chain index, = 1, 2,..., 9 Time period n Number of ime periods SQ, Quaniy shipped by sae i of supply chain in period i D i O, O Cusomer demand in period Order quaniy of sae i of supply chain in period Esimae of averae demand per period of supply chain Esimae of variance of orders placed by sae i in supply chain i, Producion order by facory of supply chain in period PO i I, Endin invenory of sae i of supply chain in period TISC Toal invenory of supply chain

Iniial saus of supply chains: T. Chinna Pamuley and V. Madhusudanan Pillai /Uncerain Supply Chain Manaemen 4 (2016) 7 SQ i, 0, i, ; I i, 40, i, ; O i, 0, i,. 0 0 0 2 Variance of orders placed by sae, i, where, O 48 O 7 i, Toalinvenory a each sae TISC 4 48 i1 7 I i, n i, I i, and n 42. i, 48 7 I i, 48 7 ( O i, n 1 O) 2, (1) (2) (3) SQ, 4 SQ, 3 SQ, 2 SQ, 1 PO Facory Disribuor Wholesaler Reailer Cusomer O 3, O 2, O 1, D Fi. 7. Order and shipmen flows in a supply chain. Table 2 General parameers and sae wise parameer General parameers Cusomer demand disribuion N(20,5) Duraion of he play 55 periods Performance evaluaion period 7 o 48 Sae wise parameers Sae Iniial invenory Holdin cos ($) Order lead ime Delivery lead ime Reailer 40 unis 0.5 0 1 period Wholesaler 40 unis 0.5 0 1 period Disribuor 40 unis 0.5 0 1 period Facory 40 unis 0.5 0 0 5. Resuls The performance measures are calculaed for each supply chain and he averae value of each performance measure over nine supply chains in each sein is shown in Fi.s 8 10. The averae value of variance of orders placed by each sae, oal invenory a each sae and he TISC under differen seins are shown in Fi. 8, Fi. 9 and Fi. 10, respecively. Fi. 8 shows ha he averae value of variance of orders a each sae is less under CDI sharin experimens han non-sharin informaion seins. The TISC under imperfec ADI sharin is less compared o oher seins from Fi. 10. The effec of differen forms of CDI sharin on variance of orders (BWE) placed by each sae

8 is shown in Fi. 11. I is also observed ha he variance of orders placed by each sae is less under imperfec ADI sharin han oher forms of CDI sharin. Fi. 8. Averae value of variance of orders a each sae under differen informaion sharin seins Fi. 9. Averae value of invenory a each sae under differen informaion sharin seins Fi. 10. Averae value of TISC under differen informaion sharin seins Fi. 11. Impac of differen forms of CDI sharin on BWE 6. Saisical ess conduced Various saisical ess are conduced o draw conclusions on he impac of differen forms of CDI sharin on he performance of he supply chains. Sin es is used o know he presence of bullwhip effec in he supply chain under various CDI sharin seins. Wilcoxon-Mann-Whiney es (also known as Wilcoxon es) is used o know he impac of various forms of CDI sharin on he variance of orders in he supply chain and on each sae of he supply chain. All he saisical ess are evaluaed a 5% sinificance level and one-sided p-values are repored for he es resuls. Resuls found o be sinifican are hihlihed in he respecive ables. 6.1 Tes for he presence of bullwhip effec To es he presence of bullwhip effec under each form of CDI sharin wih small lead ime (one period) and no backorders, he hypoheses formulaed are as follows: Hypohesis-H1: Bullwhip effec will no occur under periodic CDI sharin wih small lead ime and no backorders. Hypohesis-H2: Bullwhip effec will no occur under hisory of CDI sharin wih small lead ime and no backorders. Hypohesis-H3: Bullwhip effec will no occur under imperfec ADI wih small lead ime and no backorders. Sin es, a non-parameric saisical es, is used o es he presence of bullwhip effec in a supply chain (Croson & Donohue 2006; Wu & Kaok 2006). In his es, for each supply chain, an increase in variance of orders beween wo consecuive saes is coded as a success and a decrease is coded as a

T. Chinna Pamuley and V. Madhusudanan Pillai /Uncerain Supply Chain Manaemen 4 (2016) 9 failure. Success is represened by a plus (+) sin and a failure is represened by a minus ( ) sin. The probabiliy of occurrence of failure or success is equal and is 0.5. I is coded as zero, if here is no chane in variance of orders and is dropped from he analysis. The sum of he plus and he minus sins is considered as sample size (N). If he sum of plus sins is represened by X, hen he probabiliy of ein X or more plus sins is calculaed usin he Binomial disribuion. If his probabiliy is less han he sinificance level fixed, hen he hypohesis mus be rejeced. The above procedure is followed for esin he presence of bullwhip effec in each form of CDI sharin and he deails are iven in Table 3. Table 3 Tes for he presence of bullwhip effec under differen supply chain seins Sl. Success rae (%) Sin es parameers Hypohesis No. N X p 1 H1:Periodic CDI 88.88 27 24 0.0000 2 H2:Hisory of CDI 77.77 27 21 0.0029 3 H3:Imperfec ADI 74.07 27 20 0.0095 From Table 3, i is found ha he p-value of all informaion sharin seins is less han he sinificance level and hence he success rae is hiher han he chance rae of 50%. Thus, he hypohesis framed should be rejeced. Hence, he sudy concludes ha he bullwhip effec occurs in supply chain under all forms of CDI sharin. 6.2 The impac of informaion sharin on he maniude of variance of orders Wilcoxon-Mann-Whiney es (Sieel & Casellan 1988) is used o know he impac of differen forms of cusomer demand informaion sharin on he maniude of variance of orders (Croson & Donohue 2003; Seckel e al., 2004; Croson & Donohue 2006). The impac of a paricular form of CDI sharin on variance of orders is known by comparin i wih he variance of orders under non-sharin of informaion. This es considers he variance of orders placed by all saes under one se of experimen as one roup, say x, and he variance of orders placed by all saes in anoher se of experimen as anoher roup, say y. Le he number of observaions in roup x and y are l and m, respecively. In his es, he observaions of boh he roups are combined and he rank for each observaion is assined by arranin hem in ascendin order sarin from one o (l+m). If observaions are equal, averae rank is assined o boh observaions. If here is a sinifican difference beween he wo roups, hen mos of he hih ranks will belons o one roup and mos of he low ranks will belons o oher one. As a resul, he sum of he ranks belonin o each roup is quie differen. On he oher hand, if he wo roups are similar, hen he hih and low ranks are disribued fairly even beween he wo roups and he sum of he ranks belons o each roup are more or less same. Afer assinin ranks, he saisics W x, W y, and z (if l or m reaer han 10) are calculaed. W x and W y are he sum of he rank of observaions belonin o he roups x and y respecively. z W x 0.5 (4) w x W x l( K 1) 2 lm( K 1) where, Mean W x ; Variance W ; 2 x 12 K = l+m The null hypohesis is ha he variance of orders is equal in he supply chain under wo differen seins. This hypohesis needs o be rejeced when he probabiliy value for he calculaed z value is less han he fixed sinificance level α. If he number of observaions in any roup (l or m ) is less han 10, he probabiliy associaed wih he occurrence under null hypohesis of any W x as exreme as he observed value is deermined and he null hypohesis mus be rejeced, if his probabiliy is less han

10 he fixed sinificance level α. The hypohesis framed under differen seins is iven below and are esed by followin he above procedure. The deails of he es and resuls are iven in Table 4. Hypohesis-H4: Variance of orders in he saes of supply chain under non-sharin of informaion and under periodic CDI sharin is equal. Hypohesis-H5: Variance of orders in he saes of supply chain under non-sharin of informaion and under hisory of CDI sharin is equal. Hypohesis-H6: Variance of orders in he saes of supply chain under non-sharin of informaion and under imperfec ADI sharin is equal. Table 4 Tes for he impac of informaion sharin on maniude of variance of orders Sl. Wilcoxon Parameers Hypohesis No. Wx Wy z p 1 H4: Non-sharin of informaion Vs Periodic CDI 1517 1111 2.29 0.0110 2 H5: Non-sharin of informaion Vs Hisory of CDI 1461 1167 1.66 0.0485 3 H6: Non-sharin of informaion Vs Imperfec ADI 1619 1009 3.44 0.0003 Since he p-value of all he above ess is less han he sinificance level fixed, he hypohesis framed under hese seins mus be rejeced. Hence, i is inferred ha he variance of orders in a supply chain is sinificanly less under differen forms of CDI sharin compared o non-sharin informaion. Furher analysis is carried ou by conducin a similar es o know he bes form of CDI sharin and resuls are iven in Table 5. The hypoheses are he followin: Hypohesis-H7: Variance of orders in he saes of supply chain under periodic CDI sharin and under hisory of CDI sharin is equal. Hypohesis-H8: Variance of orders in he saes of supply chain under periodic CDI sharin and under imperfec ADI sharin is equal. Hypohesis-H9: Variance of orders in he saes of supply chain under hisory of CDI sharin and under imperfec ADI sharin is equal. Table 5 Tes for idenifyin he bes ype of CDI sharin. Sl. No. Hypohesis Wilcoxon Parameers Wx Wy z p 1 H7:Periodic CDI Vs Hisory of CDI 1406 1222 1.04 0.1492 2 H8:Periodic CDI Vs Imperfec ADI 1439 1189 1.41 0.0793 3 H9:Hisory of CDI Vs Imperfec ADI 1565 1063 2.83 0.0023 From Table 5, i is found ha he maniude of variance of orders under imperfec ADI sharin is sinificanly less han he oher CDI sharin ypes (wih 10% sinificance level) and here is no saisical evidence for he difference in variance of orders in supply chain under periodic CDI and hisory of CDI. Thus, he imperfec ADI sharin is he bes amon he CDI ypes. 6.3. Impac of informaion sharin on variance of orders of each sae Wilcoxon es is conduced o know he impac of differen forms of cusomer demand informaion sharin on he variance of orders a each sae. Since he experimens are conduced under hree forms of CDI sharin, he variance of orders a each sae is compared wih he variance of orders under nonsharin of informaion in-order o know he impac of CDI sharin. Hence here are hree hypoheses framed and esed for each sae. In his es, he roups x and y are he variance of orders of a sae under wo differen seins of he supply chain. The hypoheses framed are as follows:

T. Chinna Pamuley and V. Madhusudanan Pillai /Uncerain Supply Chain Manaemen 4 (2016) 11 For reailer sae: Hypohesis-H10: Variance of orders placed by reailer under non-sharin of informaion and under periodic CDI sharin is equal. Hypohesis-H11: Variance of orders placed by reailer under non-sharin of informaion and under hisory of CDI sharin is equal. Hypohesis-H12: Variance of orders placed by reailer under non-sharin of informaion and under imperfec ADI sharin is equal. For wholesaler sae: Hypohesis-H13: Variance of orders placed by wholesaler under non-sharin of informaion and under periodic CDI sharin is equal. Hypohesis-H14: Variance of orders placed by wholesaler under non-sharin of informaion and under hisory of CDI sharin is equal. Hypohesis-H15: Variance of orders placed by wholesaler under non-sharin of informaion and under imperfec ADI sharin is equal. For disribuor sae: Hypohesis-H16: Variance of orders placed by disribuor under non-sharin of informaion and under periodic CDI sharin is equal. Hypohesis-H17: Variance of orders placed by disribuor under non-sharin of informaion and under hisory of CDI sharin is equal. Hypohesis-H18: Variance of orders placed by disribuor under non-sharin of informaion and under imperfec ADI sharin is equal. For facory sae: Hypohesis-H19: Variance of orders placed by facory under non-sharin of informaion and under periodic CDI sharin is equal. Hypohesis-H20: Variance of orders placed by facory under non-sharin of informaion and under hisory of CDI sharin is equal. Hypohesis-H21: Variance of orders placed by facory under non-sharin of informaion and under imperfec ADI sharin is equal. The hypoheses framed are esed and parameers of he ess are lised in Table 6. From Table 6, i is eviden ha he CDI sharin of any form is no havin sinifican impac on he variance of orders of downsream saes such as reailer and wholesaler. Bu, here is a sinifican reducion in he upsream saes such as disribuor and facory. Since he informaion sharin is havin sinifican impac a upsream saes, ess are conduced o know which ype of informaion sharin is havin hihes impac a upsream saes (see Table 7) and i is found ha he imperfec ADI is havin he sinifican impac over he oher ypes. The imperfec ADI is, hus, he beer form of CDI han he oher forms esed in his sudy. The followin are he hypoheses framed. For disribuor sae: Hypohesis-H22: Variance of orders placed by disribuor under periodic CDI sharin and under hisory of CDI sharin is equal. Hypohesis-H23: Variance of orders placed by disribuor under periodic CDI sharin and under imperfec ADI sharin is equal. Hypohesis-H24: Variance of orders placed by disribuor under hisory of CDI sharin and under imperfec ADI sharin is equal. For facory sae: Hypohesis-H25: Variance of orders placed by facory under periodic CDI sharin and under hisory of CDI sharin is equal. Hypohesis-H26: Variance of orders placed by facory under periodic CDI sharin and under imperfec ADI sharin is equal.

12 Hypohesis-H27: Variance of orders placed by facory under hisory of CDI sharin and under imperfec ADI sharin is equal. Table 6 Tes of variance of orders: sae-wise hypohesis and Wilcoxon parameers Sl. Hypohesis No. Wilcoxon Parameers W x W y p Reailer 1 H10:Non-sharin of informaion Vs Periodic CDI 91 80 P(W x 91) 0.3332 2 H11:Non-sharin of informaion Vs Hisory of CDI 81 90 P(W x 81) 0.6668 3 H12:Non-sharin of informaion Vs Imperfec ADI 90 81 P(Wx 90) 0.3652 Wholesaler 4 H13:Non-sharin of informaion Vs Periodic CDI 103 68 P(W x 103) 0.0680 5 H14:Non-sharin of informaion Vs Hisory of CDI 83 88 P(W x 83) 0.6019 6 H15:Non-sharin of informaion Vs Imperfec ADI 102 69 P(W x 102) 0.0807 Disribuor 7 H16:Non-sharin of informaion Vs Periodic CDI 119 52 P(W x 119) 0.0009 8 H17:Non-sharin of informaion Vs Hisory of CDI 113 58 P(Wx 113) 0.0028 9 H18:Non-sharin of informaion Vs Imperfec ADI 126 45 P(W x 126) 0.0000 Facory 10 H19:Non-sharin of informaion Vs Periodic CDI 108 63 P(Wx 108) 0.0252 11 H20:Non-sharin of informaion Vs Hisory of CDI 120 51 P(W x 120) 0.0006 12 H21:Non-sharin of informaion Vs Imperfec ADI 126 45 P(W x 126) 0.0000 Table 7 Tes for he bes ype of informaion sharin a upsream saes Sl. No. Hypohesis Wilcoxon Parameers W x W y p Disribuor 1 H22:Periodic CDI Vs Hisory of CDI 96 95 P(W x 96) 0.1933 2 H23:Periodic CDI Vs Imperfec ADI 116 55 P(W x 116) 0.0028 3 H24:Hisory of CDI Vs Imperfec ADI 105 66 P(W x 105) 0.0470 Facory 4 H25:Periodic CDI Vs Hisory of CDI 95 76 P(W x 95) 0.2181 5 H26:Periodic CDI Vs Imperfec ADI 115 56 P(W x 115) 0.0039 6 H27:Hisory of CDI Vs Imperfec ADI 119 52 P(Wx 119) 0.0009 6.4 Impac of informaion sharin on invenory Impac of informaion sharin on oal invenory of he supply chain is analysed by Wilcoxon-Mann- Whiney es and he resuls are abulaed in Table 8. The resuls show ha he impac of imperfec ADI sharin is sinifican and i is concluded ha he invenory of supply chain under his informaion sharin is less han he oher ypes. Bu, a eneralized conclusion could no be arrived a, when he sae-wise invenories of he differen seins are esed. This may be due o he behaviour of safeharbour sraey of each sae member in he supply chain (Nienhaus e al., 2006). In safe-harbour sraey, a human bein orders more han he required. The hypoheses esed are as follows: Hypohesis-H28: Toal invenory of supply chain under non-sharin of informaion and under periodic CDI sharin are equal. Hypohesis-H29: Toal invenory of supply chain under non-sharin of informaion and under hisory of CDI sharin are equal. Hypohesis-H30: Toal invenory of supply chain under non-sharin of informaion and under imperfec ADI sharin are equal.

T. Chinna Pamuley and V. Madhusudanan Pillai /Uncerain Supply Chain Manaemen 4 (2016) 13 Table 8 Tes of oal supply chain invenory: Deails of Wilcoxon es parameers Sl. Hypohesis Wilcoxon Parameers W x W y p No. 1 H28:Non-sharin of informaion Vs Periodic CDI 93 78 P(W x 93) 0.2729 2 H29:Non-sharin of informaion Vs Hisory of CDI 93 78 P(W x 93) 0.2729 3 H30:Non-sharin of informaion Vs Imperfec ADI 105 66 P(W x 105) 0.0470 7. Discussion To know he impac of CDI sharin in he performance of a supply chain, a four-sae supply chain havin a small lead ime (one period) operain in a non-backorders siuaion is analysed experimenally usin supply chain role play ame sofware packae under non-sharin of informaion as well as differen forms of CDI sharin. Saisical ess, namely, sin es and Wilcoxon es are conduced o know he impac of differen forms of CDI on he followin aspecs of supply chain viz. presence of bullwhip effec, maniude of variance of orders, and invenory. The presen sudy shows ha here is a bullwhip effec in he supply chain under all forms of CDI sharin. Analysis of feedback of paricipans reveals ha he reason for he presence of bullwhip is he behaviour of paricipans. Some of he paricipans said ha hey were helpless, and were in frusraion when heir suppliers could no supply he ordered quaniy. This means ha he paricipan mih have used safe-harbour and/or panic sraey in heir invenory manaemen. This alon wih he delay in he availabiliy of ordered maerial mih have creaed an increase in he variabiliy of orders from downsream o upsream saes. I may be noed ha he effec of delay is minimised in he experimens by keepin he lead ime o he smalles possible value. Wilcoxon es reveals ha maniude of variance of orders is less in all forms of CDI sharin compared o non-sharin of informaion. Tess conduced o know he impac of CDI sharin on he variance of orders of each sae show ha here is no clear evidence of impac of CDI sharin on downsream saes such as reailer and wholesaler bu, here is a sinifican reducion in variance of orders a upsream saes such as disribuor and facory. In he radiional supply chains (i.e., non-sharin informaion based supply chains), he variance of orders placed by saes (wholesaler, disribuor, and facory) increases as we move from wholesaler o facory because he decisions are aken based on he orders placed by is downsream sae; is maniude increase is less under all oher informaion sharin cases. The imperfec ADI sharin is found o be he mos effecive han he oher ypes. A he end of each se of experimens, feedback is colleced from he players and is explained below. Many players paricipaed keenly in all experimens and heir feedback show ha hese experimens helped hem o undersand supply chain dynamics. Durin radiional (non-sharin of informaion) experimen, sae members were unable o predic heir cusomer demand and were helpless when hey could no mee he demand. Some were in frusraion when heir suppliers could no send he exac quaniy ordered by hem. Some of he reailers ook umos care o mee he cusomer demand durin he experimens and some were unhappy when hey could no mee i because of heir suppliers. Durin he experimens wih informaion sharin, hey were comforable o a cerain exen han non-sharin of informaion case. Durin periodic CDI sharin experimens, some players poined ou he usefulness of periodic CDI sharin o he immediae sae of he reailer bu no for far away saes due o lead ime. However, he periodic CDI sharin is useful o ake beer decisions han wihou informaion sharin o a cerain exen. They suesed ha sharin he hisory of CDI wih all members may be useful o upsream members. Some players repored ha he experimen helped hem o undersand he imporance of coordinaion in supply chain. All hese informaion from feedback show he relevance of his experimenal sudy, and he ineres and involvemen of he players durin he experimens.

14 The averae value of each performance measure of supply chain under CDI informaion sharin has shown improvemen compared o non-sharin informaion. The performance of supply chain is improved from periodic CDI o hisory of CDI, hisory of CDI o Imperfec ADI. The percenae reducion in maniude of variance of orders placed by each sae under various seins is calculaed and is abulaed in Table 9. (This is prepared for he cases where saisical es has shown sinificance.) The saisical es shows (see Table 5) ha he players used periodic CDI sharin and hisory of CDI sharin in he same manner. Table 9 Percenae reducion in he maniude of variance of orders in CDI sharin compared o non-sharin informaion Informaion sharin ype Sae name Reailer Wholesaler Disribuor Facory Periodic CDI --- --- 51.34 35.63 Hisory of CDI --- --- 43.23 48.60 Imperfec ADI --- --- 64.43 66.04 The percenae of behavioural componen presen in he variance of orders can be quanified by comparin he resuls wih a benchmark. For a supply chain, he benchmark resuls are possible by followin a bes or opimal invenory policy. Someimes, a combinaion of informaion such as onhand invenory of various saes, invenory in he form of ousandin order of various saes and cusomer demand disribuion sharin may furher reduce he bullwhip effec. These are he areas o be explored furher. Anoher avenue is findin he effec of sharin demand informaion in he form of forecas insead of demand disribuion. The presen sudy is havin many manaerial implicaions. I shows ha behavioural aspec is one of he causes for bullwhip effec. Lack of CDI is also cause for BWE (Serman 1989) and CDI sharin reduces he bullwhip effec as per he presen sudy. Buildin a supply chain havin cusomer demand disribuion sharin is more beneficial han accurae CDI sharin. I also infers ha he bullwhip effec canno be eliminaed compleely bu i can be conrolled or reduced only. 8. Conclusion Performance of a four-sae supply chain havin a small lead ime of one period operain under non-backorder siuaion is analysed experimenally o know he impac of Cusomer Demand Informaion (CDI) sharin. A supply chain role play ame sofware packae is developed o conduc he experimens. I is concluded ha CDI sharin definiely has an impac on he performance of a supply chain. The performance of a supply chain increases wih respec o he deree of cusomer demand informaion shared. Differen forms of CDI sharin esed in his research are periodic CDI, hisory of CDI and CDI in he form of disribuion. All hese forms of informaion sharin found o have sinifican impac on he reducion of bullwhip effec compared o non-sharin informaion. The saisical analysis also confirms ha sharin CDI in he form of disribuion is he mos effecive one amon he oher forms sudied and he upsream saes in he supply chain are he mos benefied under CDI sharin. These sudies also infer ha bullwhip effec canno be eliminaed compleely bu i can be reduced or conrolled. In he simulaion sudy of invenory policy for impulse demand, Wadhwa e al., (2009) proposed ha insead of usin complicaed ools o share accurae demand informaion o all saes, he mean demand informaion may improve he overall performance of a supply chain sinificanly. One of he conclusions of his experimenal sudy is also in line wih his concep. Saisical ess confirmed ha here is no difference in sae-wise invenories under differen supply chain seins in he presen research. The safe-harbour sraey of each sae member in he supply chain may be he reason for he insinifican difference in he invenory a each sae in differen supply chain seins. The conclusions of his sudy are limied o he assumpions, performance measures used, srucure of supply chain and he parameers under which he experimens are conduced. The robusness of hese resuls can be esed by conducin he experimens under differen cusomer

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