Understanding the Value of Fulfillment Flexibility in an Online Retailing Environment

Problem definition: Fulfillment flexibility, the ability of distribution centers (DCs) to fulfill demand originating from other DCs, can help e-retailers reduce lost sales and improve service quality. As the cost of full flexibility is prohibitive, we seek to understand the value of partially flexible fulfillment networks under simple and effective fulfillment policies. Academic/Practical Relevance: We propose a general method for understanding the practical value of (partial) fulfillment flexibility using a data-driven model, theoretical analysis, and numerical simulations. We then apply this method for a large e-retailer. Our method applies to e-retailers with local fulfillment constraints and customer abandonment, two features that are new to the fulfillment literature. We also introduce a new class of spillover limit fulfillment policies with attractive theoretical and practical features. Results: We derive optimal fulfillment policies in various settings using theoretical analysis, which provides guidelines on which policies to test in numerical simulations. We then use simulations to estimate for our partner that a proposed fulfillment network with additional flexibility equates to a profit improvement on the order of tens of millions of U.S. dollars. Managerial Implications: We provide an approach for e-retailers to understand when fulfillment flexibility is most valuable, and find it provides the most benefit for our collaborator when fulfillment costs are high, or centrally held inventory is low. Also, we identify the risks of myopic fulfillment with additional flexibility and demonstrate that an effective spillover limit policy mitigates these risks. Methodology: Our analysis uses dynamic and stochastic optimization, applied probability, and numerical simulations.

[1]  Stephen C. Graves,et al.  Benefits of Reevaluating Real-Time Order Fulfillment Decisions , 2009, Manuf. Serv. Oper. Manag..

[2]  Amitabh Sinha,et al.  Joint Dynamic Pricing and Order Fulfillment for E-Commerce Retailers , 2016, Manuf. Serv. Oper. Manag..

[4]  Sunil Kumar,et al.  A Re-Solving Heuristic with Bounded Revenue Loss for Network Revenue Management with Customer Choice , 2012, Math. Oper. Res..

[5]  Gilvan C. Souza,et al.  INVENTORY RATIONING AND SHIPMENT FLEXIBILITY ALTERNATIVES FOR DIRECT MARKET FIRMS , 2009 .

[6]  Stephen C. Graves,et al.  Making Better Fulfillment Decisions on the Fly in an Online Retail Environment , 2015, Manuf. Serv. Oper. Manag..

[7]  Enver Yücesan,et al.  Transshipments: An emerging inventory recourse to achieve supply chain leagility , 2002 .

[8]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[9]  Panos Kouvelis,et al.  Inventory Rationing for Multiple Class Demand under Continuous Review , 2016 .

[10]  Xuan Wang,et al.  Online Resource Allocation with Limited Flexibility , 2018, Manag. Sci..

[11]  Ruud H. Teunter,et al.  Inventory models with lateral transshipments: A review , 2011, Eur. J. Oper. Res..

[12]  William C. Jordan,et al.  Principles on the benefits of manufacturing process flexibility , 1995 .

[13]  Amin Saberi,et al.  Online stochastic matching: online actions based on offline statistics , 2010, SODA '11.

[14]  Hau L. Lee A multi-echelon inventory model for repairable items with emergency lateral transshipments , 1987 .

[15]  Patrick Jaillet,et al.  Online Stochastic Matching: New Algorithms with Better Bounds , 2014, Math. Oper. Res..

[16]  K. Littlewood. Special Issue Papers: Forecasting and control of passenger bookings , 2005 .

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

[18]  Seyed M. R. Iravani,et al.  An Efficient and Robust Design for Transshipment Networks , 2011 .

[19]  William L. Cooper Asymptotic Behavior of an Allocation Policy for Revenue Management , 2002, Oper. Res..

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

[21]  J. Ryan,et al.  Emergency transshipment in decentralized dealer networks: When to send and accept transshipment requests , 2006 .

[22]  Stephen C. Graves,et al.  Mitigating Spillover in Online Retailing via Replenishment , 2016, Manuf. Serv. Oper. Manag..

[23]  Ivo J. B. F. Adan,et al.  Approximate evaluation of multi-location inventory models with lateral transshipments and hold back levels , 2012, Eur. J. Oper. Res..

[24]  George Tagaras,et al.  Pooling in multi-location periodic inventory distribution systems , 1999 .

[25]  Claudia R. Rosales,et al.  Retailer Transshipment versus Central Depot Allocation for Supply Network Design , 2013, Decis. Sci..

[26]  Maqbool Dada,et al.  A two-echelon inventory system with priority shipments , 1992 .

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

[28]  Amitabh Sinha,et al.  Joint inventory and fulfillment decisions for omnichannel retail networks , 2018, Naval Research Logistics (NRL).

[29]  Sven Axsäter,et al.  A New Decision Rule for Lateral Transshipments in Inventory Systems , 2003, Manag. Sci..

[30]  R. Kapuściński,et al.  Shipping Consolidation with Delivery Deadline and Expedited Shipment Options , 2017 .

[31]  Amitabh Sinha,et al.  An LP-Based Correlated Rounding Scheme for Multi-Item Ecommerce Order Fulfillment , 2015, Oper. Res..

[32]  Rachel Q. Zhang,et al.  Online Demand Fulfillment Under Limited Flexibility , 2018 .

[33]  Garrett J. van Ryzin,et al.  A Multiproduct Dynamic Pricing Problem and Its Applications to Network Yield Management , 1997, Oper. Res..

[34]  Annie I. Chen,et al.  Large-scale optimization in online-retail inventory management , 2017 .