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Industry-Propelled Evolution of Teaching and Research in Supply Chain Management

Industry-Propelled Evolution of Teaching and Research in Supply Chain Management. Hau L. Lee Stanford University. 2007. The bullwhip effect as an example of the evolution of supply chain management The new emphasis on empirical-research

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Industry-Propelled Evolution of Teaching and Research in Supply Chain Management

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  1. Industry-Propelled Evolution ofTeaching and Research in Supply Chain Management Hau L. Lee Stanford University 2007

  2. The bullwhip effect as an example of the evolution of supply chain management The new emphasis on empirical-research The interactive nature of empirical and model-based research on the bullwhip effect. Overview

  3. Order variability is amplified up the supply chain: upstream is worse. What you see is not what they face. Bullwhip, whip-saw, whip-lash effect; or acceleration principle. Information Distortion:The Bullwhip Effect

  4. Information Distortion: The Bullwhip Effect Order Variability Up the Pampers Supply Chain Wholesalers Retailers Customers P & G Babies Source: Lee, Padmanabhan and Whang, 1997

  5. Information Distortion: The Bullwhip Effect Order Variability Up the Pampers Supply Chain Wholesalers Retailers Customers P & G 3 M Babies

  6. Teaching: Teaching cases (Barilla, Campbell, Solectron, West Marine, etc.) Renewed interest in beer game, computerized beer game, web-based beer game. Research: Bullwhip descendants Value of information sharing and collaborative forecasting Incentives Multi-site coordination Empirical research Industry practice ECR, QR, EFR, … Information sharing, visibility, RFID, … Bullwhip Impact

  7. Mitigating the bullwhip Taming the bullwhip Y2Kbullwhip Cracking the bullwhip Disaster bullwhip Dot-com bullwhip Dampening the bullwhip Gulf war bullwhip Controlling the bullwhip ECR Countering the bullwhip QR EFR

  8. Bullwhip Effect at Barilla SpA

  9. JITD at Barilla SpA Stockout Results of Test at Cortese's Marchese DC Shipments Inventory Source: Hammond

  10. The Mosquito Link Source: Benchmarking Partners

  11. Empirical research is multi-dimensional: Field-based case studies Ethnographical approaches Statistical data analyses Richer knowledge advances through interactive empirical and model-based research Empirical-Model-Empirical-Model- … Examples: RFID Logistics friction Bullwhip Interest in Empirical Research

  12. Empirical Research of Bullwhip Sources Purposes Focus Establish existence Model building for causes and remedial actions Management messages Micro -- firm or supply chain level Various cases studies Understand extensiveness of phenomenon Where is it more prevalent Macro -- economy level Economists, Cachon et al.

  13. 45,000 350,000 40,000 300,000 35,000 250,000 30,000 25,000 200,000 20,000 150,000 15,000 100,000 10,000 50,000 5,000 0 0 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 Bullwhip in Electronics Industry Peripheral Product Consumables 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 1 4 7 week week Unit orders from a major retailer to manufacturer Total unit sales at outlets of retailer

  14. More Empirical Evidence Volume Volume Time Time in a Year PC Chicken Noodle Soup Orders POS

  15. Demand Variability -- Bullwhip Effect in LaserJet L Series 4L 5L 4L 5L Shipments Sell Thru-To

  16. Reseller Order Bullwhip -- 5L 12000 Constrained Supply 10000 8000 Channel Inefficiencies Sell-To Std Dev 6000 4000 Comp USA Office Depot OfficeMax Staples 2000 Tandy Corp Micro Electronics Best Buy Elek-Tek PC Warehouse 0 0 2000 4000 6000 8000 10000 12000 Order Std Dev

  17. Bullwhip Factors in Fransoo and Wouters (2000) Node Production DC Retail franchisee Meals 1.75 1.26 1.67 Salads 1.23 2.73 2.09 Bullwhip factor defined as: COV of customer orders -------------------------------------------------------------------------- COV of orders places at suppliers

  18. Firm-based: Fransoo and Wouters (convenience stores, 2000) de Kok et al (Philips Electronics, 2005); Lai (Sebastian de la Fuente, 2005) Industry-based: Anderson et al. (machine tools, 2000); Terwiesch et al., (semiconductors, 2005) Economy-based: Cachon et al. (2005) Empirical Research on Bullwhip

  19. Research questions: How prevalent is bullwhip effect in economy? Why do strengths of bullwhip effect differ across industries in an economy? Are there any shifts in the intensity of bullwhip effect over time? Data: US Census Bureau, 1992-2004 Monthly sales and inventories in retail, wholesale and manufacturing sectors Cachon, Randall & Schmidt (2005)

  20. Assume shipment = demand. Data de-trended but not de-seasonalized. Data aggregated over industry sector (and monthly). Adjust shipment to account for margin so that it is comparable to inventory. Imputed productiont = Shipmentt + (Inventoryt – Inventoryt–1) Take natural log of all data. Setup of Cachon et al (2005)

  21. Bullwhip Amplification Ratio AR = Var(Production)/Var(Shipment) Results: Strong bullwhip effect observed if data was seasonally adjusted. With seasonally unadjusted data: little bullwhip at manufacturers (62% with AR < 1) and retailers (86% with AR < 1, some at wholesalers (84% with AR > 1). Production smoothing due to predictable seasonality may have overwhelm tendency to amplify. Cachon et al (2005) Results

  22. Claim: no need to focus on demand order, since it is information which is costless to supply chain – but, isn’t it the case that distorted information creates inefficiencies in the supply chain? Production inferred by differences in Average Inventory in consecutive months, which is a “smoothed” measure and not the same as beginning and ending inventory. Production levels are constrained by capacity and material availabilities, but demand orders are not. Aggregation may hide bullwhip Aggregation across substitutable products Aggregation across time Potential Measurement Problems

  23. Flexibility Contracts

  24. Value of information sharing inconclusive, probably based on specific demand model used. Can we use the most general demand model to generalize results? Information sharing usually assumes supplier having knowledge of actual demand model and order policy used at retail level. What if supplier doesn’t? Ordering decisions are based on two motivations: responsive to demand, and order smoothing. How can we analyze bullwhip effect in the presence of order smoothing effects? Three Key Problems

  25. A Generalized Demand ModelChen and Lee (2007) Dt : Demand in period t. t-i,t : IID random variable normally distributed with mean 0 and standard deviation si , where • Termed MMFE (Martingale Model of Forecast Evolution • IID, AR(1), IMA(0,1,1), general ARIMA, and ADI models are all special cases of MMFE model.

  26. Forecasting Under MMFEChen and Lee (2007) Ft-i,t : Forecast of period t made in period t-i.

  27. General Order-Up-To PolicyChen and Lee (2007) St : Order-up-to level in period t. wi: Row vector of weights. t-i: Row vector of forecast revisions made in period t-i. Ot : Order quantity in period t. ei: Unit vector with the i-th element equal to one. w0= 0.

  28. Retailer could optimize m and wi to minimize its cost. Supplier also uses generalized order-up-to policies with inventory borrowing assumption when stockouts. Supplier could optimize its own m’ and wi’ to minimize its cost, based on whether retailer shares its forecast revision data to supplier or not. Difference of supplier cost with or without retailer forecast revision data constitutes the value of forecast information sharing. Such sharing requires retailer sharing its order policy (m and wi), and the forecast revisions twith supplier. Setup of Supply Chain Model

  29. Advanced Order Revision ModelChen and Lee (2007) 1. Retailer gives advanced order projections to supplier. 2. ot-i,t : Revision of order projection to supplier for period t made in period t-i. 3. Final order for period t is: 4. Can show that the advanced order revision model is equivalent to the model of forecast revision sharing, but NO need to share retailer order policy and forecast revisions.

  30. Total Supply Chain Analysis Optimizing total supply chain cost results in: (where L is the lead time to retailer and  is a computed constant between 0 and 1.) • Observations: • If retailer optimizes its own cost, then the resulting order revision vector has element given by above, but that = 0. This is equivalent to postponing a fraction of the order quantity to the subsequent period, i.e. order smoothing. • With order smoothing, bullwhip may or may not exist.

  31. The Evolution Cycle Industry Practice New Ventures Teaching Research

  32. Supply chain management as a field has benefited from the joint evolutions from industry practice, teaching and research. Close interactions have created a field with more rigor, relevance, and business values. Such evolutions also breed a new group of research-based business ventures. Evolutions are still on-going, and the opportunities remain great. Summary

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