Applying Revenue Management to the Reverse Supply Chain

We study the disposition decision for product returns in a closed-loop supply chain. Motivated by the asset recovery process at IBM, we consider two disposition alternatives. Returns may be either refurbished for reselling or dismantled for spare parts. Reselling a refurbished unit typically yields higher unit margins. However, demand is uncertain. A common policy in many firms is to rank disposition alternatives by unit margins. We show that a revenue management approach to the disposition decision which explicitly incorporates demand uncertainty can increase profits significantly. We discuss analogies between the disposition problem and the classical airline revenue management problem. We then develop single period and multi-period stochastic optimization models for the disposition problem. Analyzing these models, we show that the optimal allocation balances expected marginal profits across the disposition alternatives. A detailed numerical study reveals that a revenue management approach to the disposition problem significantly outperforms the current practice of focusing exclusively on high-margin options, and we identify conditions under which this improvement is the highest. We also show that the value recovered from the returned products critically depends on the coordination between forward and reverse supply chain decisions.

[1]  Vineet Padmanabhan,et al.  Comments on "Information Distortion in a Supply Chain: The Bullwhip Effect" , 1997, Manag. Sci..

[2]  K. Talluri,et al.  The Theory and Practice of Revenue Management , 2004 .

[3]  Gilvan C. Souza,et al.  Time Value of Commercial Product Returns , 2006, Manag. Sci..

[4]  Mark E. Ferguson,et al.  The Effect of Competition on Recovery Strategies , 2006 .

[5]  R. Dekker,et al.  Reverse logistics : quantitative models for closed-loop supply chains , 2004 .

[6]  S. Mitra Revenue management for remanufactured products , 2007 .

[7]  Simme Douwe Flapper,et al.  Product recovery in stochastic remanufacturing systems with multiple reuse options , 2001, Eur. J. Oper. Res..

[8]  S. Tayur,et al.  Due Date Management Policies , 2004 .

[9]  Karen Donohue,et al.  A Threshold Inventory Rationing Policy for Service - Differentiated Demand Classes , 2003, Manag. Sci..

[10]  Albert Y. Ha Inventory rationing in a make-to-stock production system with several demand classes and lost sales , 1997 .

[11]  Michael R. Galbreth,et al.  Optimal Acquisition and Sorting Policies for Remanufacturing , 2006 .

[12]  Patrick Beullens,et al.  Reverse Logistics Network Design , 2002 .

[13]  Ruud H. Teunter,et al.  Matching Demand and Supply to Maximize Profits from Remanufacturing , 2003, Manuf. Serv. Oper. Manag..

[14]  Hui Zhao,et al.  Inventory Sharing and Rationing in Decentralized Dealer Networks , 2005, Manag. Sci..

[15]  Gilvan C. Souza,et al.  The Optimal Disposition Decision for Product Returns , 2008 .

[16]  Yves Dallery,et al.  Dynamic vs static pricing in a make-to-stock queue with partially controlled production , 2007, OR Spectr..

[17]  L. Beril Toktay,et al.  Market Segmentation and Product Technology Selection for Remanufacturable Products , 2005, Manag. Sci..

[18]  Necati Aras,et al.  Optimal Prices and Trade-in Rebates for Durable, Remanufacturable Products , 2005, Manuf. Serv. Oper. Manag..

[19]  Gp Gudrun Kiesmüller,et al.  A continuous time inventory model for a product recovery system with multiple options , 2002 .

[20]  Gilvan C. Souza,et al.  Good buy? Delaying end-of-life purchases , 2003, Eur. J. Oper. Res..

[21]  L. V. Wassenhove,et al.  MANAGING PRODUCT RETURNS FOR REMANUFACTURING , 2001 .

[22]  Mark Ferguson,et al.  The Value of Quality Grading in Remanufacturing , 2009 .

[23]  A. Ingold Revenue management: Hard-core tactics for market domination , 2002 .

[24]  D. M. Topkis OPTIMAL ORDERING AND RATIONING POLICIES IN A NONSTATIONARY DYNAMIC INVENTORY MODEL WITH n DEMAND CLASSES , 1968 .

[25]  Jayashankar M. Swaminathan,et al.  Managing New and Remanufactured Products , 2006, Manag. Sci..

[26]  Harvey M. Wagner,et al.  Global Sensitivity Analysis , 1995, Oper. Res..

[27]  D. Guide,et al.  Business Aspects of Closed-Loop Supply Chains , 2003 .