Ingeniería. Investigación y Tecnología ISSN: 1405-7743 [email protected] Universidad Nacional Autónoma de México México

Villegas Morán, Felipe A.; Carranza, Octavio; Antún, Juan P. Supply chain dynamics, a case study on the structural causes of the Bullwhip Effect Ingeniería. Investigación y Tecnología, vol. VII, núm. 1, enero-marzo, 2006, pp. 29-44 Universidad Nacional Autónoma de México Distrito Federal, México

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Supply Chain Dynamics, a Case Study on the Structural Causes of the Bullwhip Effect1

F. Villegas-Morán1 , O. Carranza2 y J.P. Antún3

Operations Management, Manchester Busines School. University of Manchester, UK1 .

Universidad Panamericana, México 2 e Instituto de Ingeniería, UNAM 3 E- mails: [email protected], [email protected] y [email protected]

(recibido: febrero de 2005; aceptado: junio de 2005)

Resumen Este artículo es un caso de estudio sobre el modelado de la estructura de toma de decisiones de la cadena de suministro de una embotelladora en México. Al modelar las cadenas de suministro de esta manera, es posible identificar las políticas gerenciales y los flujos de informacion que introducen y amplifican distorsiones en la demanda. En la segunda parte de este artículo, utilizamos dos escenarios para analizar posibles modificaciones en las políticas de dirección. Este trabajo ilustra no sólo una innovadora forma de estudiar el efecto látigo, o una forma distinta de modelar las cadenas de suministro usando los principios de dinámica de sistemas, sino que también establece una relación entre la estructura de información, las políticas de los gerentes y las distorsiones en la cadena de suministro.

Descriptores: Dinámica de sistemas, cadenas de suministro, caso de estudio, efecto látigo.

Abstract This is a case study about the mod el ling of a sup ply chain de ci sion struc ture of a Mex i can bot tling com pany. We find that by mod el ling the in for ma tion and de ci sion struc ture of sup ply chains, it is pos si ble to iden tify man a ge rial pol i cies and in for ma tion flows that dis tort and am plify mar ket de mand sig nals. In the sec ond part of the pa per we use two sce nar ios to ana lyse var i ous changes in pol i cies. This pa per il lus trate not only an in no va tive form to study the Bull whip Ef fect nor only a dif fer ent way to model sup ply chains us ing Sys tem Dy nam ics, but also it es tab lishes a re la tion ship be tween in for ma tion struc tures, de ci sions rules, and de mand dis tor tion in sup ply chains.

Keywords : Sys tem dy nam ics, sup ply chain man age ment, case study, bull whip ef fect.

Introduc tion tional strat egy and infor ma tion availabil ity. Order ful fil ment is constrained by pro duc tion capac ity, The study of sup ply chain dy nam ics is about com- trans por ta tion ca pac ity and inven tory availabil ity. panies oper at ing manu fac tur ing supply chains of Sup ply chain sys tems have mainly two time de lays: multi ple eche lons subject to lim ited produc tion and dis tri bu tion ca pac i ties. At each ech e lon, ope- 1 Por razones de confidencialidad, los datos referidos en ra tion man ag ers re ceive or ders from a down stream este artículo (a excepción de los públicos) han sido ech e lon and try to ful fil them by tak ing two de ci- modificados. Por tanto, este modelo no refleja sions: shipping from available inven tory, and or- forzosamente la realidad del negocio en cuestión. Sin dering more products to the eche lon upstream. embargo, sentimos que esas modificaciones no afectan Order poli cies are based on expe ri ence, opera- la validez científica de la investigación. Supply Chain Dynamics, a Case Study on the Struc tural Causes of the Bull whip Effect

or ders are commu ni cated with infor ma tion time sys tem in time, and that the sup ply chain pol i cies de lays, and they are ful filled with op er a tional time can be always reduced to a set of par tial dif fer en - delays too (e.g., produc tion and deliv ery). The tial equa tions that can be solved. As we know, this supply chain dynam ics problem con sists in that is not the case of real sup ply chains that are typ i - given a set of order pol i cies from man ag ers at each cally non-linear par tial differ en tial equations of ech e lon, market demand signals will be dis torted higher order. Lee et al . (1997a, 1997b) did not and am pli fied (the Bullwhip Effect) through the suggest any new set of poli cies to improve the eche lons. The objec tive of supply chain dynam ics supply chains dy nam ics be hav iour re sponse. prob lems is to mini mize op er a tional costs de rived from those dis tor tions and ampli fi ca tions by im - PepsiCo has two di vi sions, Pepsi Cola North prov ing man ag ers order pol i cies. America, for the US, and PepsiCo Bev er ages In ter - national, for the rest of the world. In 2003, In the context of the supply chain dynam ics Pepsi-Cola North Amer ica (PCNA) had in cre ments prob lem, For rester (1962), and Sterman (1989, on volume (4%), reve nue (18%) and op er at ing 2000), have explored the impact of time de lays. profit (13%) as indi cated in figure 1. PCNA grew Lee et al. (1997a, 1997b) have ex plored the im pact faster than its largest compet i tor. In fact, PCNA that batching, price discounts, ration ing ex pec ta- gained share while Coca-Cola share de clined. They tions and fore cast ing, have in the def i ni tion of are sure that in no va tion was the driver of that order poli cies that lead to distor tions of market growth, because in fact PCNA brought an array of de mand sig nals. Towill et al. (1991, 1995), Naim et new prod ucts to the mar ket place. al . (2002) and Dejonckheere et al. (2002, 2003, 2004) have used an approach based on opti mal Much of that inno va tion focused on carbo- con trol the ory to find con trol pol i cies to smooth nated soft drinks (Figure 2). Pepsi Twist, which is the bull whip ef fect. Pepsi with a hint of lemon, helped the growth in their cola business. Within 30 days of launch ing However, For rester and Sterman’s approaches Pepsi Twist in the US, Pepsi bot tlers had sold more fall short of study the supply chain dynam ics be - than 10 mil lion cases. In addi tion, in its first full cause they use a pre de fined flow of infor ma tion year on the mar ket, lemon-lime Si erra Mist gen era- and manage ment rules which are not lon ger valid ted healthy sales and, where it was avail able, drove for com pa nies that use in for ma tion sys tems. Towill growth in the lemon-lime cate gory. Meanwhile, et al. (1996, 2000), Dejonckheere et al. (2002, 2003, Moun tain Dew Code Red contrib uted to strong 2004) as sume flow con ti nu ity for the sup ply chain Moun tain Dew growth of 6%.

Figure 1: EMSA, PepsiCo worldwide beverage volume by region (Source: Annual report 2002)

30 INGENIERIA Investigación y Tecnología FI-UNAM F. Villegas-Morán, O. Carranza y J.P. Antún

Figure 2: EMSA, Pepsi-Cola North America product mix and channels (Source: Annual report 2002)

While tradi tional car bon ated soft drinks account growth came from products under the Dole and for the bulk of bever age volume, as consum ers SoBe brands. PCNA’s goal is to continue to im - seek greater va ri ety, their non-carbonated drinks prove its posi tion in the market (Figure 4) to be - have been grow ing very rapidly, with volume up come the fast est grow ing broad-based bever age more than 30% in 2001. In fact, over the last de - com pany. For this strat egy it is cen tral to keep the cade they have built the lead ing port fo lio of con tin u ous ex pan sion of its prod uct port fo lio. non-carbonated drinks (Figure 3) — includ ing Aquafina bot tled water, Lipton ready-to-drink teas, PCNA, work ing with Frito-Lay North America Frappuccino coffee drinks, Dole juices and drinks (FLNA), also added excite ment with awarded mar - and SoBe bev er ages. ket ing cam paigns in 27 urban cen tres across the U.S. They included mer chan dis ing, promo tions Aquafina is already the top-selling single-serve and ad ver tis ing that cap tured the at ten tion of Af ri - bot tled water in the US. On the year of its in tro duc- can-American and Latino consum ers. PCNA and tion (2001), it vol ume grew about 45%. The launch FLNA acti vated more than 5,500 ac counts and of a new bot tle helped PCNA growth of more than achieved vol ume gains of more than 25% in par tic i - 20% in Lipton Iced Tea. And addi tional volume pat ing stores.

Figure 3. EMSA, U.S. Non-carbonated beverage market (Source: Annual report 2002)

Vol.VII No.1 -enero-marzo- 2006 31 Supply Chain Dynamics, a Case Study on the Struc tural Causes of the Bull whip Effect

Figure 4: EMSA, U.S. Top-selling carbonated soft drinks (Source: Annual report 2002)

PepsiCo Bever ages Inter na tional (PBI), formed after year. Vol ume was up nearly 5% (Table 1), matching the PepsiCo-Quaker merger by com bin ing the in ter- their largest compet i tor. Reve nue was up 2%. Ope- national oper a tions of Pepsi-Cola, Gatorade and rat ing profit was up 31%. Tropicana, posted a solid per for mance in its first

Table 1: EMSA, Pepsi-Cola North America operating profits (Source: Annual report 2002)

Pepsi-Cola North America % Change B/(W) 2001 2000 1999 2001 2000 Net Sales Reported $3,842 $3,289 $2,605 17 26 Comparable $3.842 $3,253 $3,005 18 8

Operating profit Reported $927 $833 $751 11 11 Comparable $927 $820 $751 13 9

PepsiCo Beverages International % Change B/(W) 2001 2000 1999 2001 2000 Net Sales Reported $2,582 $2,531 $2,407 2 5 Comparable $2,582 $2,531 $2, 429 2 4

Operating profit $221 $169 $108 31 56

32 INGENIERIA Investigación y Tecnología FI-UNAM F. Villegas-Morán, O. Carranza y J.P. Antún

In partic u lar, the volume growth in Rus sia, China, purchase sugar based on price. Every year they se - Brazil and Thailand contrib uted to advances in lect a small set of sup pli ers from a pool of pos si ble mar ket share. In fact, PBI gained share in most of vendors. Sugar price var ies ac cord ing to mar ket. In its top mar kets, with partic u lar progress in Leb a- Mexico most of the produc ers are state owned. non, Rus sia, Ven e zuela, Viet nam and Egypt. There is a min i mum amount of sugar to buy on a monthly basis of 185Ton. Purchase manag ers are Here too, in no va tion was a big fac tor. Ex ten sions also re spon si ble for the sup ply of alu minium cans of the flagship Pepsi trademark helped to drive and plas tic or glass bottles. Purchase manag ers growth in a vari ety of local markets. For exam ple, gener ate a supply plan once every month and at Pepsi Limón and Pepsi Twist — in both cases, Pepsi least one month in advance. Pepsi uses its own with a hint of lemon — proved also to be popular in fleet of trucks to pickup the mate ri als from some disimilar coun tries such as Mex ico and Saudi Ara bia. suppli ers. The follow ing is an extract from the in - The launch of Moun tain Dew contributed sig nif i- ter views with the pur chase man ager: cantly to growth in Rus sia. And new ad di tions to the estab lished line-up of Mirinda brand flavours were “We have two main warehouses per plant: one for launched in more than 30 mar kets. raw ma te ri als (sugar, la bels, bot tles and cans), and an other for Pepsi syrup only. Right now we have During 2003, PBI gained impor tant ad van tages by US$1.2m in inven to ries of raw ma te ri als. In this bringing together Pepsi-Cola, Gatorade and Tropi- ware house, there are com po nents that are man aged cana. Combining the gen eral and admin is tra tive against sched ule or ders: la bels, bot tles and cans etc. functions of these busi nesses around the globe yields We have a min i mum stock in ven tory pol icy… very sub stan tial cost sav ings. In ef fect, the com bi na- tion of Gatorade, Tropicana and Pepsi’s water made a We order based on a max i mum and min i mum with pow er ful port fo lio for a wide range of needs — from small cor rec tions ac cord ing to the real de mand…We simple refresh ment to nutri tion to post-exercise have to take into ac count main te nance, and order in hydration — for con sum ers around the world. ad vance when needed. We have also or ders to be con- firmed on a monthly basis. Every week we check our Modelling consider ations in ven to ries and pay their in voices. 80% of our pur- chase is Pepsi syrup and sugar. In our case study we work with the main bot tler of PepsiCo Bever ages In ter na tional in Mex ico: EMSA When a new product launch hap pens, we have to (Embotelladora Mexicana Sociedad Anónima), work closely with design ers from PepsiCo Mex ico. which at tend Cen tral Mex ico, in clud ing the states of The de signs are pro vided from the cor po rate head- Jalisco and the Bajío. Ac cord ing with its supply quarters, we then forward them to our label chain man ager, EMSA is con sid ered the op er a tional suppliers along with an ini tial pur chase order… stan dard for the rest of Latin Amer ica. We se lected a high sales vol ume prod uct, in this case Pepsi 600ml My main prob lems with Logis tics are that they which rep re sent al most 40% of net sales. never give me the pro duc tion programme!”

As with any other bev er ages com pa nies, EMSA Production manager is mainly in ter ested in per fect order pol i cies. That is, keep ing in ven to ries in all pos si ble retail ers, When we in ter viewed the produc tion manager, since prod uct sub sti tu tion against the com pe ti tion apart from being proud of their ex cel lence awards is very fre quent. In their business, prod uct pre- in qual ity and achievements in re duc ing waste, he sence at sales point is trans lated into sales. pointed out that one of the problems was the ob - soles cence of prod uct due to shelf life. When a Purchase manager pro duc tion short age hap pens, they use past sales as a guide to as sign avail able prod ucts to ful fil de - The main raw mate rial for the produc tion of mand or ders from RDCs. This has gen er ated in the Pepsi-Cola, apart of water of course, is sugar. They sales manag ers the cul ture of over or der ing when

Vol.VII No.1 -enero-marzo- 2006 33 Supply Chain Dynamics, a Case Study on the Struc tural Causes of the Bull whip Effect

ration ing expec ta tions appear. The produc tion a more pre cise fore cast and they asked us to make a man ager also de cides about ex ter nal pro duc tion of more pre cise pre dic tion. We pro duced that fore cast com po nents, spe cially for bot tle pro duc tion. for 4 or 5 months directly, creat ing the forecast from our sales esti ma tions based on the “last “I am based very much on stock po si tions in the in for - month sales” and we multi plied it by a factor ma tion sys tem. Mainly, I look at in ven tory po si tions month by month... together with past sales and in ware houses or CEDIS (CEntro de DIS tri bu tion). I new sale ex pec ta tions we prod uced a forecast by have my own pol icy of in ven to ries. I always try to space, brand, ware house, fla vours… we then sent follow my pol icy, which is op ti mal. I look at the in- that forecast to produc tion… our accu racy was ven to ries once a week and from there I make a weekly around 96% with some fail ures in fla vours… some plan: How much do I re quire for every prod uct for the times boys [sales men] re quired more orange than next week based on my forecast and stock po si tion? apple fla vours and then again we had some com- How much is my ex cess or short age?... then I de cide if plaints from pro duc tion. We fi nally agree that fore- I need to pro duce many or a few. cast ing was going to be again a respon si bil ity of produc tion, but under the as sess ment of the sales Now, in [the case of] plas tic and glass bot tled pro- depart ment… that they make it, but ask ing us ducts, be cause we never have high [ex pen sive] in ven- and compar ing against our own expec ta tions… to ries, I need to be very flexi ble in schedul ing. But since then we have not fol lowed this initia tive that is not the case of cans; [there] I try to make long properly… as I told you about fore casts, they know pro duc tion runs per week. In this way I can op ti mize it very well, but up to now, we do not have well the num ber of changes and set ups, for dif fer ent fla- solved who is in charge of fore casts… they never vours and sizes…[therefore] scrap is reduced… if I call us to vali date the fore cast… that is what we make many changes and set ups, scrap is pro - have to im prove!... duced…[that is why] my in ten tion is to make long runs each week”. Ev ery thing goes together with sales… if we do not have the product we can not sell… the chal lenge of Sales managers produc tion is to pro duce all the nec es sary prod ucts (packages, labels) in order to send the products on They have all the market infor ma tion in a system time to reach warehouse early and then the sales - called SIME (Sistema de Informacion de MErcado), man can take the prod uct and de liver it to our cus- customer by customer. They have more than tomers as it should be: high quality, good image, 150,000 sales points. They recog nise that their good con di tions of bot tles, etc… I be lieve that pro- main busi ness is dis tri bu tion since ad ver tis ing de- duc tion used to do a good job, same as sales… we pends on PepsiCo Head quar ters. The av er age level have lots of things to im prove.” of edu ca tion reached by a sales man is second ary school. In prin ci ple, the forecast is produced by Logistics manager oper a tional manag ers using econo met ric stan - dards, and the sales manag ers are respon si ble of Their main prob lem is dis tri bu tion, in par tic u lar re - fine tune it with expected demand volumes per lated to the admin is tra tion of differ ent sizes of zone and by prod uct. The sales man ag ers do not trucks and vans, and the use of third party trans - fol low the bottom up approach to create a fore - porta tion. The lo gis tics man ag ers do not have a cast, because of previ ous ex pe ri ence, where de - clear vi sion about which RDCs can re ceive full size mand was ex ag ger ated by sales men in an ac cu mu- trucks, but they know that inter-plants can re ceive la tive per cent age of 80%, driven by the in stinct to dou ble-sized trucks. They are try ing to use the en sure prod uct avail abil ity. in-house fleet as much as pos si ble but with out re - placing them, due to a strategy to move from “… About fore cast… I be lieve that we never fol low owned trucks to third party transpor ta tion. His them… some time ago produc tion used to supply per for mance is mea sured in re la tion with the trans - us every thing that we ordered, what the mar ket por ta tion cost (per prod uct unit), and the av er age needed and we sold, but later pro duc tion asked for ca pac ity loaded per truck (% load/ca pac ity).

34 INGENIERIA Investigación y Tecnología FI-UNAM F. Villegas-Morán, O. Carranza y J.P. Antún

Model description also be used to an a lyze the con gru ency of de ci sion mak ers with re spect the in for ma tion sys tems. Given the nature of the System Dynam ics metho- dology (Sterman 2002; Lane 2001; Doyle and Ford We have selected for model vali da tion and ca- 1998), the model will not emphasise the de tail of the l i bra tion (parameterization) the historic demand for Supply Chain network. SD mod els are abstrac tions the year 2002. Based on this demand we have that con cen trate the atten tion not in a detailed mod elled the supply chain dynam ics by includ ing mod el ling of the re al ity but in the cause-effect and heu ris tic poli cies as described by the supply chain feedback loops that gen er ate a given behaviour. In manag ers dur ing our inter views. The model shows our case the study be hav iour is the Bull whip Ef fect, the main ag gre gated be hav iour of inven to ries, diffe- and the causes of the behav iour are de fined by the rences be tween plan and ex e cu tion and the re sult ing poli cies of the supply chain manag ers, that make service level. The deci sion mak ing happens at the deci sions based on a given flow of infor ma tion. be gin ning of every week, when man ag ers look at the Therefore, the model is lim ited in de tail but not in in for ma tion sys tems and de cide how much to order meaning since our analy sis of distor tions is of an up stream. Every event with less that one week du- aggre gated na ture. Partic u larly, a model of this ra tion is con sid ered as a simultaneous one for the na ture does not need to de tail mul ti ple plants or DCs pur poses of the simu la tion. The time step unit is and products to ana lyze the infor ma tion use and weeks and all order quanti ties are in finished goods de ci sion mak ing pro cess of man ag ers. equiv a lent units.

The model lays empha sis on the mod el ling of Fig ure 5 shows the model di a gram for the Pepsi pol i cies of the sup ply chain man ag ers that may be 600ml. Rect an gles rep re sent stock po si tions of raw based on their own expe ri ence or knowledge. We ma te ri als, WIP and fin ished goods. As can be seen, make explicit the use of infor ma tion flows and in the model we have de fined four stock po si tions their sources. The model shows the avail abil ity and in the model: raw ma te rial (RM), work in pro cess reli abil ity of the in for ma tion through the in for ma- (PLANT), finished goods at warehouses (DC) and tion sys tems used by the busi ness. The model can fin ished goods in de pots (RDC). The raw mate rial

Figure 5: EMSA Supply chain model

Vol.VII No.1 -enero-marzo- 2006 35 Supply Chain Dynamics, a Case Study on the Struc tural Causes of the Bull whip Effect

t1 stock units rep re sent all the com po nents needed RM = ò (proc _ RM(t)- production(t))dt to build one unit of fin ished goods. t0

t1 Vari ables are rep re sented with cir cles, and Factory = ò( production - prod _ output(t))dt con stants with dia monds. The variable value or t 0 t con stant is commu ni cated to another variable by 1 draw ing a sin gle arrow. Some vari ables rep re sent DC = ò( prod _ output( t)- distribution(t))dt t de ci sion mak ers (man ag ers) and in clude the use of 0 t 1 in for ma tion in puts into a func tion that ends with a Retailers = ò(distribution(t)- s ales(t))dt nu mer i cal de ci sion (e.g., pro duc tion order). Sup ply t 0 chain manag ers are rep re sented by the variables proc_mgr, prod_mgr, and log_mgr . In gen eral, these man ag ers use the stock posi tions, forecast and Rate vari ables are de fined: safety stock tar get for their de ci sion mak ing. proc _ RM = DELAYPPL( Pr oc _ mgr ,1,0) ìRM + proc _ RM , Pr od _mgr > RM + proc_ RM EMSA oper a tional manag ers use the term production = í “cover age” to define the safety stock pol icy îProd _ mgr, Prod _mgr £ RM + proc _ RM de fined in terms of forecasted days/weeks of prod _ output = product ion ìDC + prod _ output ,Dist _ mgr > DC + pord _output de mand. The safety stock pol i cies, or safety stock distribution = í tar get, are constant values. Cover age pol i cies are îDis t_ mgr, Di st _ mgr £ DC+ pord _output ìReatilers + distribution,Demand > Retail er s + distribution differ ent for raw mate ri als and finished goods s ales = í mainly be cause there is a delay of more than one îDemand, Demand £ Retailers + dist ribution week from pur chase to de liv ery of ma te ri als. Aux il iary vari ables are: Demand forecast is calcu lated using the last 3 weeks (PastTime) of his toric de mand and we use ìSS _ RM + forecast _ 2,SS _ RM + forec ast _ 2 > RM Proc _ mgr = í them to project the next FutureTime demand î0, SS _ RM + forec ast _ 2 £ RM ac cord ing to the FORECAST func tion ex trap o la tion ì0,DC > forec ast _1+ SS_ DC Prod _ mgr =í that uses ex po nen tial smooth ing. î forecast _1+ SS_ D C,D C£ forecast _1 +SS _ DC ì0, Retaile rs > forecas t _1+SS_Retailer Dist _mgr = í The model groups vari ables/pa ram e ters in two îforecast _1+ SS_Retailer, Retaile rs£ forecast _1+SS_Retaile r rectan gles that repre sent the in for ma tion system fore cast _1= FORECAST (Demand ,3,1) fore cast _ 2 = FORECAST (Demand ,3, 2) where the infor ma tion is allo cated. Pepsi-EMSA ì10250*Fc st _ Promotions,TIMEIS(9) AdvancedProduc tion = í has an ERP sys tem de rived from IBM’s AS400 and î0,otherwise an in for mal fore cast sys tem based in Excel. SS_ DC = forecast _1*cov erage _ PT SS_ Re tailer = forecast _1*cove rage _ PT The model can in clude pro mo tional events and SS_ RM = coverage _ rm* forecast _ 2 the intro duc tion of new products, in such a way that the forecast is not only in flu enced by past Ini tial val ues and pa ram e ters: weeks but also by mar ket ing cam paigns. Also some coverage_RM =0.5 spe cial sea sons where some pro duc tion needs to coverage_PT =0.5 be allo cated in advance to avoid produc tion DC(t0 ) = 20,000units overload. These ideas are captured by the Factory(t0 ) = 0units vari ables Fcst_Proms and Adv_Produc tion . Retailers(t0 ) = 20,0 00 units RM(t ) = 20,000 units 0 Given that our model is con tin u ous, non-linear and fourth degree system, we used a numer i cal The DELAYPPL func tion is an in fi nite Order Ma- solu tion method for the analy sis. The model is te rial Delay. In the hy po thet i cal in fi nite order delay described in math e mat i cal form as follows. First (pipe line delay) noth ing happens to the output the state vari ables are de fined by: until the delay time has elapsed. At this time the

36 INGENIERIA Investigación y Tecnología FI-UNAM F. Villegas-Morán, O. Carranza y J.P. Antún

input variable is repro duced ex actly. A pipeline Di a gram: The pipe-line delay, figure 6, may delay may be looked upon as a “mov ing side walk” be modelled using a vector with Delay or con veyor belt, where items are put on the con - Time/TIMESTEP el e ments, which is shifted veyor at one end, and expelled at the other end lin early to the right every time step: after a fixed time. Equations This delay may be mod elled using a num ber of lev els that equal the num ber of time steps in the delay time, i.e., DelayTime/TIMESTEP. In each time step, ma te rial is moved from one level to the next, until it reaches the final level, where it is out put. In Powersim this may be modelled using a vector level, and apply ing the SHIFTLIF function at each time step to shift ele ments from one posi tion to the next. Figure 6. Delay Pipe-Line

Pipeline delay: Equations of an Infi nite Order aux Input = ... Ma te rial Delay if we as sume there are ten steps in a init InTransit = ... delay time, the equa tions be come: dim InTransit = 1..10 flow InTransit(i) = dt*(Input | i=1;0) - dt*(Out put | aux Input = “Input rate to be de layed” i=LAST(i);0) aux Out put = SHIFTLIF init InTransit = “Ini tial con tents of delay” dim InTransit = 1..10 flow InTransit(i) = dt*(Input | i=1;0) – dt*(Out put | SHIFTLIF_Condi tional_Lin ear_Shift_of_Vec tor_ El e- ments>func(TRUE, InTransit) i=LAST(i);0) aux Out put = SHIFTLIF(TRUE, InTransit) The num ber of el e ments of InTransit should be The function DELAYPPL is used to ex press this set equal to the number of time steps in a De- layTime pe riod, i.e., DelayTime/TIMESTEP. kind of delay, we can write di rectly:

aux Output = DELAYPPL(Input, Valida tion DelayTime, 0) When a sim u la tion is ran using historic demand Syntax: DELAYPPL (Input, DelayTime[, Ini - from the year 2002, we can observe some dy- tial=Input]) namics result ing from the deci sion mak ing structure used by the man ag ers and in ad di tion of Input: Variable to be delayed (delayed un cer tain de mand. parameter).

DelayTime: Delay time mea sured in the time Table 3. EMSA Finished good´s inventory move ments at unit of the sim u la tion (start-up pa ram e ter). RDCs

RDC Input Initial: Ini tial delay value (optional start-up Week Sales initial orders pa ram e ter with de fault equal to Input). 0 20000 0 13083 Re sult: The value of Input at DelayTime time 1 6917 17189 15392 units earlier in the sim u la tion. Dur ing the 2 8714 15105 15392 first DelayTime time units of the sim u la tion, the values spec i fied by Ini tial are returned 3 8427 19823 17701 (Initial is a vec tor with one el e ment per time step for a pe riod equal to DelayTime). con tin u ous...

Vol.VII No.1 -enero-marzo- 2006 37 Supply Chain Dynamics, a Case Study on the Struc tural Causes of the Bull whip Effect

Table 3. EMSA Finished good´s inven tory move ments at restores it’s the planned stock levels. In effect, RDCs (...contin u a tion) dur ing the fol low ing week, new de mand for 15,392 units is served and 17,189 units of stock are re - Week RDC Input Sales ceived, reach ing a final in ven tory of 8,714 units. initial orders 4 10549 8096 12884 Given the motive of this business, it is not 5 5761 17224 15157 possi ble to count on the supply of backorders either. If dur ing a given week demand exceeds 6 7849 15086 15157 inven tory on hand, the sup plier manager only 7 7779 19545 17431 serves as much as possi ble, and does not 8 9893 15285 16501 con sider the short age for later. 9 8678 22162 19413 It is im por tant to see that dur ing the ini tial mo- 10 11427 18910 19413 ments of the sim u la tion, we start from ini tial in ven - 11 10925 24978 22325 to ries (pa ram e ters), and after a few mo ments the model reaches a warm-up state that corre sponds 12 13578 15680 19314 more to the evo lu tion of the sys tem than to the ini - 13 9945 25939 22723 tial val ues. There fore, we will con sider only the be - 14 13161 22205 33723 hav iour of the sys tem after the 10th week. 15 12643 29248 26131 In fig ure 7 we show the cus tomer ser vice level. 16 15760 32484 29574 The dotted line rep re sents the fore cast value and in green we have the ‘real’ demand. The con- In table 3 we can see the stock move ment in the tinu ous line repre sents sales: since it coin cides RDCs. The ini tial in ven tory is 20,000 units. Dur ing with the de mand, it is cov ered be hind. There fore, the first week we have no arriv als but sales of the model shows that given the heu ris tic pol i cies 13,083 units, re sult ing in a clos ing inven tory of from the supply chain manag ers dur ing the year 6,917 units. How ever, dur ing the first week the dis- 2002, no short age to cus tom ers was ex pe ri enced. tribu tion manager or ders finished goods from the DC up stream to re turn to the planned stock lev els In the con sumer goods in dus try, and in par tic u- and cover expected future prod uct de mand. The lar the food in dus try, it is known that the cus tomer shipment from DC to RDC happens dur ing the never waits for backorders. There fore, the as sump - week. There fore, at the end of the week the RDC tion of 2002 demand to test the model is

Figure 7: EMSA Customer service and demand fore cast

38 INGENIERIA Investigación y Tecnología FI-UNAM F. Villegas-Morán, O. Carranza y J.P. Antún

mean ing ful to pro vide an in ter pre ta tion. How ever, inven tory pol i cies, the safety stock is defined as the com pany only has re cords about sales and not days of cover age times the forecast. Inven tories ‘real’ de mand. Since we use sales as input for the peak between weeks 15 and 25 which coin cides fore cast, a bias can be in tro duced. It can hap pen with the summer. Notice that in ven to ries are that a low fore cast causes lost sales re sult ing in a ap prox i mately half of de mand. This is be cause the differ ence between sales and ‘real’ demand. If we cov er age pol icy is 3 days of de mand. use sales in stead of de mand in fore cast ing we can con strain the mar ket to sell only what we think that Work in pro cess in ven to ries is equal to 0 units, we will sell, in stead of what the cus tomer wants. be cause produc tion time is always less than a week. There fore, noth ing is in pro cess at the end of If we an a lyze the in ven to ries graph, fig ure 8, we every week. can observe that high inven to ries are held, and therefore a cost of inven to ries derived from the From fig ure 8 it is pos si ble to see that fin ished heu ris tic pol i cies from the sup ply chain man ag ers. goods inven to ries at the RDCs move before the fin ished goods at the DCs. In fact, with one week of In figure 8 we can also see high raw mate rial phase lag. This phase lag it is not caused by the stock posi tions in compar i son with the finished de liv er ing time, which is less than a week, but by the goods inven to ries. This can be caused be cause: de mand which is first served from the RDC before first, the de liv ery time is more than one week; and the RDC man ager sends an order to the DCs. sec ond be cause the cover age pol icy is one week. These factors together can cause os cil la tions like We can also see in fig ure 8 that we do not have the ones shown in the graph, since when the pur - any neg a tive stock. No tice that the os cil la tory fre - chase man ager de cides not to ask for ma te ri als, we quency does not have any re la tion to the de mand reach the safety stock limits and a big order is vari a tions. De mand is clearly sea sonal dur ing the placed lead ing to ex cess in ven tory. year, with peaks dur ing the summer between weeks 15 and 25. This os cil la tory dis tor tion is ex - Also, in fig ure 8, since the stocks have a noisy plained next. ini tial value we can see that it takes around 10 weeks to dis si pate, and then the ‘real’ be hav iour of In fig ure 9 we can see, in the first place how pro- the sys tem ap pears. duc tion or ders and pur chases vary with re spect the re ceipt of raw ma te ri als and pro duc tion of fin ished Accord ing to the current heuris tic poli cies, goods. Purchase and produc tion vari abil ity are inven to ries fol low a simi lar behav iour to the one caused by the time delay and/or the lack of raw described by the demand signal. Due to the ma te rial to pro duce.

Figure 8: EMSA DC, RDC and RM Inven tories

Vol.VII No.1 -enero-marzo- 2006 39 Supply Chain Dynamics, a Case Study on the Struc tural Causes of the Bull whip Effect

Figure 9: EMSA Produc tion and procure ment plans and execu tion In figure 9, in rela tion to produc tion orders, we or ders, and con se quently os cil la tions in in ven to ries can see a per fect ex e cu tion of pro duc tion or ders even when the safety stock is con stant. The am- with the ex cep tion of week 45. Due to a short age pli tude and frequency of these os cil la tions are of raw ma te rial, it is not pos si ble to pro duce the uncorrelated with market oscil la tions. Such un- full require ment com ing from the produc tion correlated oscil la tions can produce some stock manager. This raw mate rial shortage produces a posi tions near zero, and in par tic u lar for the 45th re duc tion of fin ished goods in ven to ries to al most week produce a shortage in produc tion, which 0 in the same week. This kind of ar ti fi cial short age af fects the DC and RDC inven to ries, and it is close is caused by the struc ture of heu ris tic pol i cies de- to im pact ing on cus tomer ser vice. fined by the sup ply chain man ag ers. It is clear that dur ing week 45, no spe cial de mand in cre ment was Finally, figure 10 shows distri bu tion orders, expe ri enced. produc tion and purchase for each manager in the supply chain com pared, with the demand signal. In fig ure 9 we can also see the ex is tence of a one From the graph we can see that de mand os cil la tions week delay be tween the pur chase order and sup ply. are less than dis tri bu tion, pro duc tion and pur chase The Pur chase man ager uses his stock po si tion and oscil la tions re spec tively. We see the increased fore cast to order. Given the time delay and the time distor tion of oscil la tion mani fest the Bullwhip ho ri zon, he produces oscil la tions in purchase Ef fect, as de scribed by For rester (1962).

Figure 10: EMSA Bull whip effect

40 INGENIERIA Investigación y Tecnología FI-UNAM F. Villegas-Morán, O. Carranza y J.P. Antún

Finally, figure 10 shows distri bu tion orders, Business case discussion pro duc tion and pur chase for each man ager in the supply chain com pared, with the de mand sig nal. A model that rep re sents the pol i cies of sup ply chain From the graph we can see that demand oscil la - man ag ers can be used as a ‘lab o ra tory’ where pol icy tions are less than distri bu tion, produc tion and changes can be tested towards a better supply purchase oscil la tions respec tively. We see the chain perfor mance, ac cord ing to pre-defined cor- increased distor tion of oscil la tion man i fest the po ra tive goals. We prepared for Pepsi-EMSA some Bull whip Ef fect, as de scribed by For rester (1962). ini tial sce nario anal y sis that included pol icy chan- ges for the Pepsi 600 ml prod uct. Scenarios in- The bullwhip ef fect can drive wrong deci sions cluded changes in fore cast pol i cies and purchase when the produc tion or trans port ca pac ity is de- or ders. We will il lus trate just what kind of sce nar ios fined. In our model we can see that the ware house could be de vel oped for a more de tailed study, and for raw ma te ri als needs a ca pac ity of 90,000 units, how to asses the im pact of new pol i cies. and even more than that for finished goods. This warehouse ca pac ity not only repre sents a fixed Changes in purchase orders asset cost but also an inven tory cost due to the finan cial invest ment. Consider also that the As we have said, the purchase pol icy rule for raw sup pli ers can re ceive or ders that vary from 80,000 mate ri als implies dra matic amounts of am pli fi ca - to zero units from one month to the next. tion, phase lag and oscil la tion in the purchase or ders. We should ex pect that a better purchase In ef fect, os cil la tions are partic u larly evi dent policy ex ists in order to mini mize order and raw in purchase orders, and they are in flu enced by ma te rial in ven to ries. Sup pose that we im ple ment a pre vi ous or ders down stream in the sup ply chain. purchase policy for four sea sons, that is, for each No tice for in stance that dur ing the 25th week, sea son we will de fine a con stant vol ume of weekly de mand is low just after the summer sea son, purchases. which is ampli fied by dis tri bu tion and pro duc- tion. But dur ing that same week, the purchase Fig ure 11 shows the val ues that raw ma te rial in- man ager re ceives more than 80,000 units due to ven to ries can take if a sea sonal pur chase pol icy is a pur chase order launched dur ing the mid dle of adopted. We shall say that the max i mum de mand the sum mer. is for 60,000 units, that is, 20,000 units less than the pre vi ous pol icy, with the ad van tage of sta bil ity The bullwhip ef fect is attrib uted mainly to two for the sup plier. causes: first, the under es ti ma tion of time delays be tween or ders and their ful fil ment, sec ond, to the A pos si ble problem to define such a seasonal exis tence of a moti va tion among supply chain pol icy is the un cer tainty. This sea sonal pol icy be - man ag ers to re quest more ma te ri als than needed. haves rel a tively well for the his toric de mand of the Better coor di na tion of the supply chain by ma- year 2002, but due to its rigid ity, the same per for - n ag ers can be pro moted once manag ers are mance for the fol low ing years is not ex pected. con scious of the global effects of their heuris tic pol i cies in the sys tem. For the pro posed sce nario, we can see how the purchase manager has stopped see ing the It is in tu itive to think that a pro duc tion, dis tri- forecast as his heuris tic pol icy. However, notice bution or purchase manager will prefer stabil ity that the raw ma te rial in ven tory vari a tion does not rather than variabil ity. However, we know that have any rela tion ship with the demand varia tion. since it is im pos si ble to com pletely elim i nate the In general, the ex is tence of a trade off balance bull whip ef fect, it is desir able to define heuris tic between or ders and inven tory vari abil ity is ex- poli cies that help to control and coor di nate the pected. An opti mal pol icy will manage an equi- supply chain while customer service is high, re - lib rium point where the vari a tion of order quan- sult ing in higher oper at ing and finan cial per- tities will be eco nom i cal and equiva lent to va- formance. ri a tions in in ven to ries.

Vol.VII No.1 -enero-marzo- 2006 41 Supply Chain Dynamics, a Case Study on the Struc tural Causes of the Bull whip Effect

Figure 11: EMSA, Scenario 1

Figure 12: EMSA, Scenario 2 Changes in forecast he de cides to re duce the cov er age from 1 week to 0.5 weeks to gether with the rest of the man ag ers. Now sup pose that we could de velop a fore cast sys tem that pro vides in for ma tion for two weeks in ad vance, in Fig ure 12 shows the impact of this new pol icy. such a way that the purchase man ager can order raw We no tice that the max i mum in ven tory of raw ma - mate ri als in advance to receive them the week when te ri als is now ap prox i mately 50,000 units, while the they are needed. Because of this new fore cast sys tem cus tomer ser vice is kept in good health.

42 INGENIERIA Investigación y Tecnología FI-UNAM F. Villegas-Morán, O. Carranza y J.P. Antún

Oscil la tion of the purchase or ders are not elimi - With mod els like the one pre sented here it is pos si - nated, vary ing from 0 to 70,000 units in side a given ble to studied and compare differ ent compa nies season. Even though the bullwhip ef fect has de- and dif fer ent sec tors by using ex per i men tal input creased we can not declare it to be solved. The signals, and sup ply chain per for mance mea sures inven tory costs are still high and the inven tory taken from ei ther op er a tions man age ment ort from oscil la tions due to the raw mate rial os cil la tions control theory. Un for tu nately, the space here is cause stresses in differ ent eche lons. The oscil la - short to de scribe those meth ods in de tail but use - tion fre quency is con sid er ably high. ful ref er ences may be found in Villegas (2005).

Under this sce nario we have re duced the de liv ery Finally, it is im por tant to say that even when the time from sup pli ers to one week. Hence, the ef fect model’s cal i bra tion process has not been des- of pos si ble ne go ti a tion on deliv ery time and fre - cribed in de tail in this paper it is in gen eral pos si ble quency can add more con trol to the os cil la tions. to cali brate a model of this com plex ity to match many data samples. What is im por tant of SD Conclusions and further research models, as it has been stated in the field, is that they repre sent the main cause-effect dynam ics In this paper it was not our in ten tion to de velop that gener ate a given system’s behav iour. As a a technique to define the best poli cies, nor the con se quence a SD model will be good in ex plain ing best way to de fine new pol i cies in order to im prove but lim ited in predict ing. The model’s valid ity is supply chain behav iour. Our in ten tion was to de- based on the con sen sus and ac cep tance from the fine a model where the main dynam ics caus ing man ag ers rather than in the sta tis ti cal proves. Bull whip Ef fect may be stud ied in order to com pre- hend the cause-effect re la tion ships be tween pol i- Acknowl edg ments cies, infor ma tion flows and deci sion rules of a given sup ply chain. We have shown that is pos si ble We would like to thank the anon y mous ref er ees to build such a model and to cap ture with rel a tive for their help ful sugges tions that have allowed us simplic ity but high degree of ab strac tion the com- to im prove the ex po si tion of this re search plex i ties of a Sup ply Chain. References However, due to its simplic ity, the model is lim ited in dif fer ent ways. For in stance, the SD model Dejonckheere J., Disney S.M., Lambrecht M.R. can be ex tended to study scenar ios where more and Towill D.R. (2002). Transfer Function in for ma tion flows are avail able, where some con flict Anal ysis of Fore casting Induced Bull whip in of in ter est affect ing the pol i cies between inter nal Supply Chains. Interna tional Journal of Pro- and exter nal manag ers are consid ered, such as duc tion Economics, 78, pp.133-144. per for mance measure ments. Also the model may Dejonckheere J., Disney S.M., Lambrecht M.R. be used to study the partic u lar i ties of differ ent and Towill D.R. (2003). Measuring and indus tries and es tab lish compar i sons across in- Avoiding the Bullwhip Effect: A Control dus tries, to study the influ ences of differ ent fore- Theo retic Approach. Euro pean Journal of cast meth ods as well as con sen sus meet ings, etc. Oper a tional Research, 147, 3, pp.567-590. Conse quently, in this paper, and for the sake of Dejonckheere J., Disney S.M., Lambrecht M.R. brev ity we have only fo cused in de scribe a busi ness and Towill D.R. (2004). The Impact of Infor - case where a SD model was cre ated to il lus trate and mation Enrichment in the Bullwhip Effect in ana lyse a partic u lar sit u a tion, but not to solve the Supply Chains: A Control Engi neering Pers- Bull whip Ef fect. What is in tended on this paper is to pective. European Journal of Opera tional Re- em pha size meth od ol ogy used to exam ine a par- search, 153, 3, pp.727-750 ticu lar problem, espe cially be cause in our opin ion, Doyle J.K. and Ford, D.N. (1998). Mental and we co in cide with many other authors, the Models Concepts for System Dynamics Bull whip Ef fect is a problem con cerned with the Research. System Dynamics Review, 14, 1, in for ma tion flow and pol icy align ment. pp. 3-29.

Vol.VII No.1 -enero-marzo- 2006 43 Supply Chain Dynamics, a Case Study on the Struc tural Causes of the Bull whip Effect

Forrester J.W. (1962). Indus trial Dynamics. USA: Dynamic Decisions Making Exper i ment. The MIT Press. Manage ment Science, 35, 3, pp. 321-339. Naim NM., Childerhouse O., Disney S.M. and Sterman J. (2000). Busi ness Dynamics. USA: Towill D.R. (2002). A Supply Chain Diag- MacGraw-Hill. nostic Methodology: Determining the Vec- Sterman J. (2002). All Models are Wrong, tor of Change. Computers & Industrial Engi - Reflec tions on Becoming a Systems Scien- neering, 43, pp.135-157. tist. Systems Dynamics Review, 18, 4, pp. Lee H., Padmanabhan V. and Whang S. 501-531. (1997a). Infor ma tion Distor tion in a Supply Towill D.R., Wikner J.J. and Naim M. (1991). Chain: The Bullwhip Effect. Management Smoothing Supply Chain Dynamics. Inter na - Sciences, 43, 4, pp.546-558. tional Journal of Produc tion Economics, 22, Lee H., Padmanabhan V. and Whang S. pp.231-248. (1997b). The Bullwhip Effect in Supply Towill D.R. (1995). Industrial Dynamics Chains. Sloan Management Review, Spring, Modelling of Supply Chains. Interna tional pp.93-102. Journal of Phys ical Distribu tion Logis tics Lane D.C. (2001). Rerum Cognoscere Causas: Manage ment, 26, 2, pp.23-42. How do the Ideas of System Dynamics Villegas F. (2005). Supply Chain Dynamics, Relate to Ttraditional Social Theories and Structural Causes of the Bullwhip Effect. the Volunta rism/deter minism Debate? Sys- Working Paper , Manchester Busi ness School, tem Dynamics Review, 17, 2, pp. 97-118. Univer sity of Manchester. Sterman J. (1989). Modelling Manage rial Behaviour: Misperceptions of Feedback in

Semblanza de los autores Felipe A. Villegas-Morán. Labora en el Departamento de Dirección de Operaciones en la Manchester Busi ness School, Univer s ity of Manchester en el proyecto de “Supply Chain Dynamics”. Es investigador en The School of Manage me nt, Univer sity of Edinburgh en el proyecto de “Transfer Prices in Multi na tional Supply Chains”. Su investigación doctoral es sobre la implantación de sistemas de información en cadenas de suministro en la Facultad de Ingeniería de la UNAM. Octavio Carranza. Obtuvo el Premio Nacional de Logística 2005, labora en el Departamento de Logística y Distribución en la Escuela de Ciencias Contables Económico Administrativas, Universidad Panamericana, México. Es doctor en dirección de operaciones por la Universidad de Navarra, España y autor del libro “Mejores prácticas logísticas”. Juan P. Antún . Obtuvo el Premio Nacional de Logística 2005. Actualmente es investigador y profesor titular del seminario de investigación en logística empresarial MIT/DEPFI/UNAM, en el Laboratorio de Transporte y Sistemas Territoriales de la Coordinación de Ingeniería de Sistemas del Instituto de Ingeniería, UNAM. Asimismo, es consultor en logística estratégica e internacional, profesor de logística internacional MAE/ITAM e instructor externo del Instituto de Desarrollo Exportador BANCOMEXT.

44 INGENIERIA Investigación y Tecnología FI-UNAM