LINKING STRATEGIC OBJECTIVES TO OPERATIONS: TOWARDS A MORE EFFECTIVE SUPPLY CHAIN DECISION MAKING. Changrui Ren Jin Dong Hongwei Ding Wei Wang

Transcription

1 Proceedings of he 2006 Winer Simulaion Conference L. F. Perrone, F. P. Wieland, J. Liu, B. G. Lawson, D. M. Nicol, and R. M. Fujimoo, eds. LINKING STRATEGIC OBJECTIVES TO OPERATIONS: TOWARDS A MORE EFFECTIVE SUPPLY CHAIN DECISION MAKING Changrui Ren Jin Dong Hongwei Ding Wei Wang IBM China Research Laboraory Building 19 Zhongguancun Sofware Park, 8 Dongbeiwang WesRoad, Haidian Disric, Beijing , P. R. CHINA ABSTRACT Supply chain managers oday face an unremiing challenge o heir capabiliies in boh he volume and complexiy of facors o be reconciled. In order o achieve more effecive decision making, i is very necessary o link sraegic objecives o operaional acions. However, lile is available o guide managers in ranslaing a se of objecives ino operaions so far. This paper presens a comprehensive mehodology o address his gap. In his mehodology, sraegic objecives are ranslaed ino performance merics by qualiaive sraegy map and meric nework firsly, and hen quaniaive echniques such as sysem dynamics simulaion and opimizaion are adoped o ake managers hrough he sages of sraegy mapping, acion evaluaion and decision making. A case sudy, suppored by a sofware ool, is carried ou hroughou he paper o illusrae how he mehod works. 1 INTRODUCTION Supply chain is a ypical complex, adapive, and dynamic sysem wih nonlineariies, delays, and neworked feedback loops. So i is very difficul for supply chain managers o clearly undersand supply chain operaional mechanisms and hus make appropriae decisions wihin he limied ime o adap o he ever-changing, compeiive, and urbulen business environmen. Performance measuremen is an effecive way o know how a supply chain operaions, however, besides he acual value of key performance indicaors (KPIs) or performance merics, managers may ofen ask quesions like below: Wha is he impac of an invenory increase by 5% on oal cos? Wha could be he boleneck causing he revenue decreased by 5% in he las 6 monhs? Which operaions should be paid more aenion, and which acions should be aken o achieve a 5% revenue growh in he following quarer? Performance measuremen can only help o idenify he problems exising in he curren supply chain, while i is helpless in exploring he roo causes of hese problems and hus choosing corresponding acions o improve supply chain performance. So here is a gap beween sraegic objecives and supply chain operaions. To hose sraegic hinkers, hey mainly concenrae on hings like revenue, profi, or cos, ec., however, all sraegic objecives mus depend on acions from operaional level o achieve. The conflic beween he op-down sraegy decomposiion and he boom-up implemenaion process is serious. Therefore, in order o overcome he above issues, i is very necessary o link sraegic objecives o operaions, which could help managers, especially hose operaing a a sraegic level, o know more operaional mechanism of supply chains, i.e., how various KPIs in supply chains affec each oher, and make more effecive decisions consequenly. The objecive of his paper is o describe our research work conduced on a comprehensive mehodology and ool o link sraegic objecives o operaions, so ha enables a more effecive supply chain decision making. The remainder of his paper is srucured as follows. A firs, a lieraure review on sraegy managemen and he mehods of linking sraegies o operaions is performed in Secion 2. Then in Secion 3, he framework and mehodology for linking sraegic objecives o operaions is presened. Each sep of he process is discussed in deail, and a case sudy is also used hroughou his secion o illusrae how each sep works. A sofware ool o suppor he whole process is inroduced in Secion 4. Finally, in Secion 5, we conclude wih some closing remarks /06/$ IEEE 1422

2 2 LITERATURE REVIEW The process of ranslaing sraegic objecives ino acions is a difficul ask. This difficuly is due o he wide range of possibiliies and he lack of srucured informaion. Managers mus ake ino accoun relevan informaion and generae a range of opions before a decision is reached. So far, lile is available o guide managers in ranslaing a se of objecives ino acions (Tan and Plas 2003). Effecive sraegy formulaion requires he seing of objecives, he idenificaion and evaluaion of alernaive acions, and he implemenaion of he seleced choice. However, a review of he lieraure shows ha he emphasis of sraegy formulaion is very much on he seing of sraegic objecives. The curren sraegy frameworks and processes seem o focus on broad direcions and he esablishmen of sraegic objecives, bu are weak in ranslaing hese ino acions for furher implemenaion. Garvin (1993) indicaed ha sraegic objecives (cos, qualiy, delivery, ec.) were oo highly aggregaed o direc decision making. They are broad and generic caegories wih a muliude of possible inerpreaions. For example, qualiy can mean reliabiliy, durabiliy, or aesheic appeal. Many researchers have indicaed ha he process of linking sraegic objecives o acions is ofen overlooked and poorly implemened. The Balanced Scorecard (BSC) (Kaplan and Noron 1992, 1993, 1996, 2000, 2001ab) is no only a performance measuremen sysem, bu a sraegy managemen ool ha can faciliae managers o find performance drivers, o explore and describe sraegic acion map precisely, o implemen sraegy effecively, and o learn from he circular process. The BSC can help o balance sraegic focuses on four perspecives (financial, inernal business process, cusomer, learning and growh), complex cause and effec relaionships, leading and lagging indicaors, and angible and inangible indicaors, and o develop more sysemic aligned sraegy. Figure 1 (Kaplan and Noron 1996) inroduces four managemen processes o link long-erm sraegic objecives wih shor-erm acions. Communicaing and Linking Communicaing and educaing Selling goals Linking rewards o performance measures Translaing he Vision Clarifying he vision Gaining consensus Balanced Scorecard Business Planning Selling arges Aligning sraegic iniiaives Allocaing resources Esablishing milesones Feedback and Learning Ariculaing shared vision Supplying sraegic feedback Faciliaing sraegy review and learning Figure 1: Four Processes for Managing Sraegy However, despie he widespread recogniion of he imporance of he BSC in sraegy managemen, some lieraures show ha he BSC heory and pracice have some limiaions. Akkermans and Oorscho (2002) advocaed five limiaions o BSC developmen. The limiaions were unidirecional causaliy oo simplisic, does no separae cause and effec in ime, no mechanisms for validaion, insufficien beween sraegy and operaions, and oo inernally focused. They furher proposed he heory of using sysem dynamics (SD) as a mehod o overcome he before-menioned limiaions. Sysem dynamics (Forreser 1961) is an approach for exploring he nonlinear dynamic behavior of a sysem and sudying how he srucure and parameers of he sysem lead o behavior paerns. The essenial viewpoin of SD is ha feedback and delay cause he behavior of sysems. In lieraure, here are many oher aemps (Schoeneborn 2003, Wolsenholme 1998, Young and Tu 2004) in developing BSC from a feedback loops perspecive o undersand and manage he dynamic complexiy, which is generaed by he complex cause-andeffec relaionships, he rade-offs among muliple objecives and measures, he resource and capaciy consrains, and he ime delays. The inroducion of SD could enhance he BSC by adding quaniaive and dynamic facors. From he perspecive of performance measuremen, i also has an emerging idea o sudy he relaionships beween performance merics. Sanos e al. (2002) incorporaed SD and muli-crieria analysis o analyze he relaionships among performance merics. Suwignjo e al. (2000) used cogniive map, cause and effec diagram, and analyic hierarchy process (AHP) o build hierarchical model and deermine prioriies of performance merics. Malina and Selo (2006) and Banker e al. (2004) made use of saisics and daa mining mehods o sudy he balance of BSC based on hisorical daa. Linking performance merics in a logical manner could help much boh on performance measuremen and decision-making. In summary, we can learn from lieraure ha linking is no a novel idea for sraegy managemen, however, i is sill immaure and a lile far from being effecively applied - he problem and difficuly lie in how o effecively link sraegic objecives o operaions, i.e., how o model and how o analyze. In lieraure, he approaches of building linkages can be divided ino wo main groups, namely qualiaive (Tan and Plas 2003, Kaplan and Noron 1996, 2000, 2001ab) and quaniaive (Akkermans and Oorscho 2002, Schoeneborn 2003, Wolsenholme 1998, Young and Tu 2004). The qualiaive approach, represening by he radiional BSC, is weak in he expression of more accurae and dynamic facors; while he quaniaive approach, represening by he adopion of SD, is oo rigid in he expression of quaniaive relaionships, especially o hose sraegic objecives. No single approach could work well, so i sill requires furher sudy if i is o be effecive in supporing he supply chain decision making process. 1423

3 3 THE FRAMEWORK AND METHODOLOGY We propose a framework (see Figure 2) wih comprehensive mehodology and ool suppor for effecive supply chain decision making by linking sraegic objecives o operaions, which incorporaes feaures from boh qualiaive and quaniaive approaches. 1. Financial: The sraegy for growh, profiabiliy, and risk viewed from shareholders perspecive. 2. Cusomer: The sraegy for creaing value and differeniaion from he perspecive of he cusomer. 3. Inernal business processes: The sraegic prioriies for various business processes ha creae cusomer and shareholder saisfacion. 4. Learning and growh: The prioriies o creae a climae ha suppors organizaional change, innovaion, and growh. Figure 3 (Kaplan and Noron 2000) gives a ypical example of how he BSC links sraegic objecives from differen perspecives ogeher. Revenue Growh Sraegy Improve qualiy of our revenue by undersanding cusomer needs and differeniaing ourselves accordingly Revenue Growh Volume Growh Ne Margin Figure 2: The Decision Making Process by Linking Sraegic Objecives o Operaions Deligh he Consumer Win-Win Dealer Relaions The following hree main processes are idenified in he framework: Nongasoline Producs & Services Bes in Class Franchise Team Producs On Spec On Time 1. Link sraegic objecives o KPIs. 2. Formulae mahemaical model. 3. Analyze and decide. Each of hese is described below. 3.1 Link Sraegic Objecives o KPIs In his process, sraegy map and meric nework are used o link sraegic objecives o KPIs (performance merics) from wo differen levels respecively. On he op level, we uilize sraegy map o express he cause-and-effec links among sraegic objecives; while on he boom level, meric nework is used o organize he value drivers of sraegic objecives from he operaional perspecive. Obviously, he linkages beween hese wo levels are also imporan. The concep of sraegy map (Kaplan and Noron 1996, 2000, 2001ab) originaes from he BSC, which provides a visual represenaion of an organizaion s criical objecives and he relaionships among hem ha drive organizaional performance. The BSC provides a framework for organizing sraegic objecives ino he four perspecives as below: Personal Growh Funcional Excellence Process Improvemen Figure 3: Revenue Growh Sraegy Map The sraegy map helps o srucure sraegic objecives in a logical way, hen we have o ranslae hese objecives ino operaions, which is more imporan bu difficul. Forunaely, he SCOR model provides a framework and a se of merics ha can be used as he saring poin for building meric nework and decomposing sraegic objecives. The SCOR (Supply Chain Operaions Reference) model is a process reference model ha was inroduced in 1996 hrough he Supply Chain Council (SCC) and suppored by more han 1000 academic and indusrial organizaions o become an indusrial sandard for supply chain managemen. The SCOR model is inended o describe he business aciviies, operaions and asks corresponding o all levels of saisfying supply chain inernal and exernal cusomer demands (Supply-Chain Council 2006). Besides he well-known conceps of business process reengineering and benchmarking, SCOR also defines a se of merics ha one can use o evaluae processes a each level of he proc- 1424

4 ess hierarchy. The performance aribues and merics are measured in five differen caegories, namely supply chain reliabiliy, supply chain responsiveness, supply chain flexibiliy, supply chain coss, and supply chain asse managemen. Each SCOR meric is associaed wih cerain SCOR processes. Based on SCOR, we can consruc meric nework for each sraegic objecive, hus achieve he decomposiion of sraegy ino operaional merics a differen levels. Figure 4 shows he whole picure of he link from a sraegy map o SCOR based meric nework. On he lef side, he sraegy map enables o decompose objecives in he sraegic world; while on he righ side, SCOR merics provide a very good foundaion for ranslaing sraegic objecives ino supply chain operaions of differen levels. Processing Cos Order Cos Transpor Cos Supply Chain Coss Supply Chain Managemen Cos Invenory Holding Cos Invenory Cos of Goods Sold Iem Cos Uni Order Disr Orders PCT Margin Figure 5: Meric Nework for Supply Chain Coss uilizing AHP o weigh sraegy map and he links o low level merics, while applying SD o meric neworks for exploring supply chain operaional mechanisms Weighing Sraegic Objecives For he difficuly in direcly quanifying, we use AHP o assign weighs o each elemen from sraegic objecives o supply chain performance aribues, according o he conribuion o is faher nodes in he map. The AHP (Saay 1980) is a commonly used ool for solving muli-crieria decision-making problems, and provides a framework o cope wih muliple crieria siuaions involving angible and inangible, quaniaive and qualiaive aspecs. I consiss of hree main seps: Figure 4: Linking Sraegy Map o Meric Nework In fac, based on he framework provided by SCOR merics, we can furher drill down o differen levels according o he acual requiremens by adding more deails beyond SCOR o he meric nework. For example, o a pharmacy wholesaler ABC company, is Level 1 meric Supply Chain Coss for one of is ypical producs can be furher expressed by he meric nework as Figure Formulae Mahemaical Model The sraegy map and meric nework lay a solid foundaion for furher decision making, however, he linkages in hese maps are all qualiaive ones which represen logical or causal dependencies, so he nex sep is o expand he iniial qualiaive framework ino a series of inerlinked mahemaical equaions ha specify how he elemens are relaed quaniaively. Quaniaive relaionships are no easy o obain in sraegy maps and he links o meric neworks. So in his paper, we design a hybrid mechanism ha 1. Decomposing he complex problem ino a hierarchy of differen levels of elemens. 2. Using a measuremen mehodology o esablish prioriies among he elemens. 3. Synhesizing he prioriies of elemens o esablish he final decision. The AHP helps o rank and make decision in a raional and sysemaic way. Weighs can be changed according o differen companies or indusries, hus i is a flexible kind of daa analysis. The AHP allows flexibiliy o aid he managemen decision-making process and reduces assessmen bias by pairwise comparison Quanifying Meric Nework In he operaional level, merics ha associaed wih business processes can be more easily o be accuraely quanified, so we inroduce SD o quanify meric neworks. Suppored by SD, dynamic facors and causal loops are allowed in his level, so more deails in operaions could be addressed. In general, he srucure of a meric nework can be divided ino wo pars: well-defined srucure and ill-defined 1425

5 srucure. In he well-defined srucure, he relaionships among merics can be direcly quanified by mahemaical equaions, which include algebraic equaions, differenial equaions, and logical equaions (such as he rules like IF THEN ). However, for he below hree reasons: Ren, Dong, Ding, and Wang 1. People sill know lile abou he sysem. 2. The informaion and daa people have are no enough o build he quaniaive relaionships. 3. Some relaionships in he srucure are uncerain in naure. People can only use semi-quaniaive or qualiaive mehods o express he relaionships in he ill-defined srucure. So in order o apply SD o he whole meric nework, we design a process o quanify he ill-defined srucure. Figure 6 illusraes he soluion o his problem. Well-defined srucure Express he relaions by mahemaical equaions Meric nework Yes Ill-defined srucure Add auxiliary variables Well-defined srucure? No Figure 7: Piecewise Linear Approximaion 3.3 Analyze and Decide The las sep focuses on puing he model o use. The qualiaive and quaniaive models we have buil can help o evaluae, analyze and design policies for supply chain decision making. Under he hypohesis of meric nework, designing a new policy for supply chain managemen is an aciviy of (1) assigning alernaive values for parameers; (2) changing linkages among sysem elemens; or (3) insering new elemens ino a model. Therefore, a policy can be expressed by some parial srucures in a meric nework. For example, we can simply add a periodic review singe iem invenory conrol policy (, R, M) o he previous model by exending he meric nework wih he srucure in Figure 8. The formas of he mahemaical equaions are uncerain The paameers of he mahemaical equaions are uncerain Receips Invenory Unis Sold Enough daa available? No Piecewise linear approximaion Yes Regression Parial Holisic Model calibraion Figure 6: The process of Quanifying Meric Nework Leadime Qy on Order Disr Orders Orders We can see from Figure 6 ha hree main mehods are inroduced o quanify he ill-defined srucure in meric nework: piecewise linear approximaion, adding auxiliary variables, and model parameers esimaion. So o an illdefined srucure, if here is enough daa available, he quaniaive relaionships can be obained by esimaion; piecewise linear approximaion, which is similar o he able funcion in SD, can work when only some limied daa is available; while adding auxiliary variables, is a simple bu effecive means under many condiions. For example, in Figure 5, Transpor Cos is deermined by Uni Order and Disr Orders, bu he quaniaive relaionship is hard o derive. By using he piecewise linear approximaion, we can easily ge an approximae expression of he funcion f for equaion (1). Transpor Cos = f (Disr Orders/Uni Order) (1) M R Figure 8: The (, R, M) Policy The value of Disr Orders can be calculaed hrough he below logical equaion (2): IF Invenory + Qy on Order < R THEN Disr Orders = (M - Qy on Order - Invenory)/ ELSE Disr Orders = 0 (2) Policy Evaluaion The AHP can no only be used for weighing sraegy map, bu also as a ool for policy evaluaion. Based on he sraegy map and meric nework, we can direcly use AHP o evaluae differen policy opions, jus as in Figure 9. This 1426

6 mehod can be used in differen levels of he model, bu i is more useful in some sraegic decision-makings. Figure 9: Policy Evaluaion Using AHP Policy Analysis The quaniaive model can be used for answering a broad range of wha if quesions, i.e., given he changes of one or more elemens in he model, wha are he impacs o oher relaed elemens? For example, if we increase Price by 10%, wha are he impacs o Sales and Toal Profi? Wha if analysis allows he organizaion o experimen in advance wih he full, long-erm consequences of poenial policies, acions or changed condiions before commiing o acion, and in pracice, such analyses can help organizaions o (Mayo and Wichmann 2003): Find policies ha produce desired benefis. Fine une he iming and sequencing of sraegy implemenaion. Spo and miigae undesirable consequences ha arise under a poenial se of acions. This kind of problem can be solved by SD simulaion based on he quaniaive models. Addiionally, he SD based analysis also brings some oher powerful auomaed analysis capabiliies ha can enhance he abiliy of organizaions o explore a rich selecion of policy opions, via for example (Mayo and Wichmann 2003): Sensiiviy esing: Which acions make he mos difference o he desired oucomes? Mone Carlo analysis: Given a poenial range of acion effeciveness, wha is he expeced value of benefis delivered, and over wha expeced ime frame will hey be delivered? Anoher kind of analysis is o find he roo causes for he exising phenomena. For example, one has observed he Toal Profi decreased by 10% in he las 5 monhs, wha could be he bolenecks causing his decrease? Such problems are no easy o solve, and one feasible approach is he rial-and-error mehod by repeaed wha-if analyses using he SD simulaion. Moreover, he eigenvalue analysis mehod can also be used o enhance he undersanding of he problemaic behaviors (Rabelo e al. 2004) Policy Design Policy design is acually an opimizaion problem, i.e., o find he opimal mix of acions (he elemens ha can be changed as decision variables) o achieve a given goal of obaining a paricular se of benefis wihin a paricular ime frame (usually a funcion of one or several elemens in he model). For example, given he objecive of achieving a 90% Perfec Order Fulfillmen, how o se he meric Price o achieve his goal? The objecive of his opimizaion problem can eiher be a payoff funcion or a arge rajecory. When he opimizaion objecive is a payoff funcion, he problem can be formulaed as below: Min g1( p), g2 ( p), L, g n ( p) (3) p s.. c (s, p)=0, ll p ul (4) Where g i (p) - he ih objecive (i=1, 2,, n), s - sae variables, p - decision variables, ll - lower limi of decision variable feasible range, ul - upper limi of decision variable feasible range, c- equaions in SD model. When he opimizaion objecive is a arge rajecory, i means ha he ime facor will be considered. So in his condiion, he problem can be formulaed as below: s = 1 s = 1 s Min f ( y1 yˆ 1 ), f ( y2 yˆ 2 ), L, f ( y yˆ ) (5) P = 1 s.. Y =c (s, p), ll p ul (6) Where Y=(y 1, y 2,, y n ) - objecive variables, y ˆi - arge rajecory for y i (i=1, 2,, n), i (i=1, 2,, s) - sampling poins, p=(p 1, p 2,, p m ) - decision variables, ll - lower limi of decision variable feasible range, ul - upper limi of decision variable feasible range, c- equaions in SD model. This nonlinear opimizaion model is difficul o solve, heurisic algorihms need o be developed. Usually, he geneic algorihms (GA) is a choice o his kind of problems. For he previous example in Figure 8, if we wan o design an opimal invenory conrol policy (, R, M) o ge much higher profi, we can use he model as Figure 10. n n 1427

7 Profi Supply Chain Coss Revenue Supply Chain Managemen Cos Cos of Goods Sold <Unis Sold> Order Cos Invenory Holding Cos Iem Price Processing Cos Transpor Cos <Invenory> Iem Cos The ool has been seamlessly inegraed wih an IBM sandard business process managemen (BPM) plaform - IBM WebSphere Business Modeler (IBM WBM), so all feaures in IBM WBM can be leveraged. Togeher wih oher modules in SmarSCOR, such as business process reengineering, logisics nework opimizaion, and supply chain simulaion, an end-o-end supply chain ransformaion soluion can be provided. The ool has easy-o-use graphic user inerfaces, see Figure 12. Uni Order <Disr Orders> PCT Margin Figure 10: The Model for Policy Design The objecive funcion of his opimizaion problem is o maximize he Profi, while he decision variables are he parameer R and M in he policy. Given he feasible range of 0 R and 100 M 10000, we finally derive he opimal policy a R=698 and M=4520 afer 364 runs. Figure 11 shows he resul of Profi under he opimal policy, in conras o he base condiion (R=200, M=1000). (a) Sraegy and Performance Modeling Figure 11: The Opimizaion Resul 4 THE TOOL SUPPORT In order o suppor he whole process we have presened for supply chain decision making, we develop a sofware ool for sraegy and performance modeling and analysis, as par of our supply chain ransformaion plaform - IBM SmarSCOR (Dong e al. 2006). The main funcions of his ool include: Suppor dynamic definiion and modeling of sraegy map and meric nework. Provide many reusable and exensible emplaes of indusry bes pracices and benchmarks, such as he SCOR model. Build in many analyics capabiliies, powered by he SD engine we developed. 5 CONCLUSIONS (b) Policy Analysis and Design Figure 12: The Sofware Tool This paper presens a comprehensive mehodology for supply chain decision making by ranslaing sraegic objecives ino operaions. Wih he sofware ool suppor, his mehod makes i possible o inegrae all of he key environmenal and behavioral elemens and heir inerrelaionships ino a single consisen, explici, and flexible sraegic level analysis sysem. Furhermore, he framework in 1428

8 his paper can also be used for supply chain diagnosis, supply chain ransformaion, and he exploraion of supply chain operaional mechanisms. This paper is inended o describe he main framework of his mehodology, raher han elaborae echnical deails. Moreover, is conribuions would be esed in furher pracices wih necessary adjusmens. In fuure, here are sill many issues which require furher sudy if his mehod is o be more effecive in supporing he decision making process and supply chain performance improvemen, such as he validaion of he quaniaive model, he accumulaion of reference modes and bes pracices by indusry, and he enrichmen of policy opimizaion mehods. REFERENCES Akkermans, H. and Kim van Oorscho Developing a balanced scorecard wih sysem dynamics. Proceeding of 2002 Inernaional Sysem Dynamics Conference. Banker, R. D., H. Chang, S. N. Janakiraman, and C. Konsans A balanced scorecard analysis of performance merics. European Journal of Operaional Research 154(2): Dong, Jin, Hongwei Ding, Changrui Ren, and Wei Wang IBM SmarSCOR - a SCOR based supply chain ransformaion plaform hrough simulaion and opimizaion echniques. Proceedings of he 2006 Winer Simulaion Conference, acceped. Forreser, J. W Indusrial dynamics. MIT Press, Cambridge, MA. Garvin, D. A Manufacuring sraegic planning. California Managemen Review 35(4): Kaplan, R. S. and D. P. Noron The balanced scorecard: measures ha drive performance. Harvard Business Review January-February: Kaplan, R. S. and D. P. Noron Puing he balanced scorecard o work. Harvard Business Review Sepember-Ocober: Kaplan, R. S. and D. P. Noron Using he balanced scorecard as a sraegic managemen sysem. Harvard Business Review January-February: Kaplan, R. S. and D. P. Noron Having rouble wih your sraegy? Then map i. Harvard Business Review Sepember-Ocober: Kaplan, R. S. and D. P. Noron. 2001a. Transforming he balanced scorecard from performance measuremen o sraegic managemen: Par I. Accouning Horizons 15(1): Kaplan, R. S. and D. P. Noron. 2001b. Transforming he balanced scorecard from performance measuremen o sraegic managemen: Par II. Accouning Horizons 15(2): Malina, M. A. and F. H. Selo Causaliy in performance measuremen models [online]. Available via <hp://www-us.colorado.edu/faculy/ selo/home.hml> [accessed March 17, 2006]. Mayo, D. D. and K. E. Wichmann Tuorial on business and marke modeling o aid sraegic decision making: sysem dynamics in perspecive and selecing appropriae analysis approaches. Proceedings of he 2003 Winer Simulaion Conference, eds. S. Chick, P. J. Sanchez, D. Ferrin, and D. J. Morrice Rabelo, L., M. Helal, and C. Lerpaarapong Analysis of supply chains using sysem dynamics, neural nes, and eigenvalues. Proceedings of he 2004 Winer Simulaion Conference, eds. R.G. Ingalls, M. D. Rossei, J. S. Smih, and B. A. Peers Saay, T. L The analyic hierarchy process. McGraw-Hill, New York. Sanos, S. P., Valerie Belon, and Susan Howick Adding value o performance measuremen by using sysem dynamics and mulicrieria analysis. Inernaional Journal of Operaions & Producion Managemen 22(11): Schoeneborn, F Linking balanced scorecard o sysem dynamics. Proceeding of 2003 Inernaional Sysem Dynamics Conference. Supply-Chain Council SCOR version 7.0 overview [online]. Available via <hp:// [accessed March 21, 2006]. Suwignjo, P., U. S. Biici, and A. S. Carrie Quaniaive models for performance measuremen sysem. Inernaional Journal of Producion Economics 63(1-3): Tan, K. H. and K. Plas Linking objecives o acions: a decision suppor approach based on causeeffec linkages. Decision Sciences 34(3): Wolsenholme, E Balanced sraegies for balanced scorecards: he role of sysem dynamics in supporing balanced scorecard and value based managemen. Proceeding of 1998 Inernaional Sysem Dynamics Conference. Young, S. H. and C. K. Tu Exploring some dynamically aligned principles of developing a balanced scorecard. Proceeding of 2004 Inernaional Sysem Dynamics Conference. AUTHOR BIOGRAPHIES CHANGRUI REN is a Researcher a IBM China Research Laboraory. He joined IBM Research in 2005 afer receiving his Ph.D. degree in Conrol Science and Engineering from Tsinghua Universiy in Beijing, P. R. China. His research ineress include supply chain managemen, logisics nework design, performance managemen, and business process managemen. He is currenly working on an end-o-end supply chain ransformaion mehodology and ool. His address is <rencr@cn.ibm.com>. 1429

9 JIN DONG, Manager of Supply Chain Managemen and Logisics Research in IBM China Research Laboraory. He received his Ph.D. degree in Tsinghua Universiy from P.R. China in Before joined IBM, he was he Research Assisan Professor in Indusrial Engineering Deparmen of Arizona Sae Universiy in USA. His address is HONGWEI DING is a Researcher a IBM China Research Laboraory. He received his Ph.D. in Auomaion from INRIA (French Naional Insiue of Compuer Science & Conrol), France. Before joined IBM, he was a researcher a INRIA. His research ineress include supply chain modeling, opimizaion and simulaion. His address is <dinghw@cn.ibm.com>. WEI WANG is a R&D Engineer a IBM China Research Laboraory. He joined IBM Research in 2005 afer receiving his maser degree in Conrol Science and Engineering from Tsinghua Universiy in Beijing, P. R. China. His research ineress include supply chain simulaion, performance managemen, and business process managemen. His address is <wangwcrl@cn.ibm.com>. 1430