The Value of Information in Quick Response Supply Chains: An Assortment Planning View

Many see timely accurate information availability as the key of successful customer-behaviour-reactive, wait-and-see planning in agile environments. Because of the extensive use of lean retailing, these quick response (QR) strategies require substantially reduced lead times across the supply chain, with the competitive advantage of enabling constant new product supply. However, successful market attempts are rapidly copied by the competition, leading to an accelerating spiral of variety-pricing games, with the inevitable result of often trivial product differentiation, reduced quality and pressure on prices. The environmental, psychological and operational effects of this spiral are severe. This work discusses the operational aspects from an assortment planning point of view, referring to the products to be included in the portfolio, as well as their inventory levels. The aim is to shift focus from timely information availability across the supply chain and wait-and-see planning, to the actual information needed to make substantial and potentially important changes, and to information available at the time when important decisions are to be taken. A proposed decision support framework - with corresponding tool set of estimation and optimization methods - helps evaluating this, by measuring the value and risk of different assortment strategies, and decisions taken at different information levels. The estimation and optimization models presented here are consistent in their use of subjective knowledge and in emphasizing the importance of attribute-based assortment planning in contemporary QR supply chains.

[1]  Kumar Rajaram,et al.  The impact of product substitution on retail merchandising , 2001, Eur. J. Oper. Res..

[2]  S. Wallace,et al.  Product variety arising from hedging in the fashion supply chains , 2008 .

[3]  R. Lowson Strategic Operations Management: The New Competitive Advantage , 2003 .

[4]  Michal Kaut,et al.  Modelling consumer directed substitution , 2011 .

[5]  Danny Berry,et al.  Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain , 1999 .

[6]  M. Dougherty,et al.  Hypothesis generation, probability judgment, and individual differences in working memory capacity. , 2003, Acta psychologica.

[7]  Ram Akella,et al.  Single-Period Multiproduct Inventory Models with Substitution , 1999, Oper. Res..

[8]  Daniel Kahneman,et al.  Judgment under uncertainty: Subjective probability: A judgment of representativeness , 1982 .

[9]  Jian Chen,et al.  A coordination mechanism for a supply chain with demand information updating , 2006 .

[10]  Marshall L. Fisher,et al.  Demand Estimation and Assortment Optimization Under Substitution: Methodology and Application , 2007, Oper. Res..

[11]  Louise Canning,et al.  Rethinking market connections: mobile phone recovery, reuse and recycling in the UK , 2006 .

[12]  D. Towill,et al.  Engineering the leagile supply chain , 2000 .

[13]  M. Fisher,et al.  Assortment Planning: Review of Literature and Industry Practice , 2008 .

[14]  Andrew McAfee . Vincent Dessain . Anders Sjoman Zara: IT for Fast Fashion (9-604-081) , 2006 .

[15]  Els Gijsbrechts,et al.  The impact of retailer stockouts on whether, how much, and what to buy , 2003 .

[16]  Hon-Shiang Lau The Newsboy Problem under Alternative Optimization Objectives , 1980 .

[17]  Michal Kaut,et al.  A Heuristic for Moment-Matching Scenario Generation , 2003, Comput. Optim. Appl..

[18]  A. Mehrez,et al.  A two-item newsboy problem with substitutability , 1996 .

[19]  T. Poiesz The Free Market Illusion Psychological Limitations of Consumer Choice , 2004 .

[20]  K. Donohue Efficient Supply Contracts for Fashion Goods with Forecast Updating and Two Production Modes , 2000 .

[21]  Jinxing Xie,et al.  The Impact of Forecast Errors on Early Order Commitment in a Supply Chain , 2002, Decis. Sci..

[22]  Tsan-Ming Choi,et al.  Mean–Variance Analysis for the Newsvendor Problem , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[23]  M. Christopher,et al.  CREATING AGILE SUPPLY CHAINS IN THE FASHION INDUSTRY , 2004 .

[24]  Anand Paul,et al.  A Generalized Model of Operations Reversal for Fashion Goods , 2001, Manag. Sci..

[25]  J. Hammond,et al.  2. Globalization in the Apparel and Textile Industries: What Is New and What Is Not? , 2003, Locating Global Advantage.

[26]  M. Christopher,et al.  Measuring agile capabilities in the supply chain , 2001 .

[27]  Angappa Gunasekaran,et al.  Agile supply chain capabilities: Determinants of competitive objectives , 2004, Eur. J. Oper. Res..

[28]  A. Gunasekaran,et al.  Agile manufacturing: The drivers, concepts and attributes , 1999 .

[29]  Nils Rudi,et al.  Centralized and Competitive Inventory Models with Demand Substitution , 2002, Oper. Res..

[30]  Oakdene Hollins,et al.  Recycling of Low Grade Clothing Waste , 2006 .

[31]  Angappa Gunasekaran,et al.  Agile manufacturing: A framework for research and development , 1999 .

[32]  Vishal Gaur,et al.  Assortment Planning and Inventory Decisions Under a Locational Choice Model , 2006, Manag. Sci..

[33]  L. Claudio,et al.  Waste Couture: Environmental Impact of the Clothing Industry , 2007, Environmental health perspectives.

[34]  Garrett J. van Ryzin,et al.  Stocking Retail Assortments Under Dynamic Consumer Substitution , 2001, Oper. Res..

[35]  Elizabeth C. Hirschman,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[36]  Ananth V. Iyer,et al.  Backup agreements in fashion buying—the value of upstream flexibility , 1997 .

[37]  Martin Kenney,et al.  Locating global advantage: industry dynamics in the international economy , 2003 .

[38]  Marshall L. Fisher,et al.  Reducing the Cost of Demand Uncertainty Through Accurate Response to Early Sales , 1996, Oper. Res..

[39]  Jan A. Van Mieghem,et al.  Commissioned Paper: Capacity Management, Investment, and Hedging: Review and Recent Developments , 2003, Manuf. Serv. Oper. Manag..

[40]  Houmin Yan,et al.  Channel coordination in supply chains with agents having mean-variance objectives , 2008 .

[41]  Tsan-Ming Choi,et al.  Mean-variance analysis of a single supplier and retailer supply chain under a returns policy , 2008, Eur. J. Oper. Res..

[42]  Michal Kaut,et al.  The value of numerical models in quick response assortment planning , 2011 .

[43]  Russell King,et al.  Quick Response: Managing the Supply Chain to Meet Consumer Demand , 1999 .