Decentralized Multi-Echelon Supply Chains: Incentives and Information
Management Science
Industry Clockspeed: Measurement and Operational Implications
Manufacturing & Service Operations Management
Inventory Management of Remanufacturable Products
Management Science
Models for Supply Chains in E-Business
Management Science
Matching Demand and Supply to Maximize Profits from Remanufacturing
Manufacturing & Service Operations Management
Supply Chain Coordination for False Failure Returns
Manufacturing & Service Operations Management
OR FORUM---The Evolution of Closed-Loop Supply Chain Research
Operations Research
Consumer Returns Policies and Supply Chain Performance
Manufacturing & Service Operations Management
An Analysis of Coordination Mechanisms for the U.S. Cash Supply Chain
Management Science
Optimal production planning for a multi-product closed loop system with uncertain demand and return
Computers and Operations Research
Manufacturing & Service Operations Management
Money-Back Guarantees: Helping the Low-Quality Retailer
Management Science
Optimization of a stochastic remanufacturing network with an exchange option
Decision Support Systems
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Manufacturers and their distributors must cope with an increased flow of returned products from their customers. The value of commercial product returns, which we define as products returned for any reason within 90 days of sale, now exceeds $100 billion annually in the United States. Although the reverse supply chain of returned products represents a sizeable flow of potentially recoverable assets, only a relatively small fraction of the value is currently extracted by manufacturers; a large proportion of the product value erodes away because of long processing delays. Thus, there are significant opportunities to build competitive advantage from making the appropriate reverse supply chain design choices. In this paper, we present a network flow with delay models that includes the marginal value of time to identify the drivers of reverse supply chain design. We illustrate our approach with specific examples from two companies in different industries and then examine how industry clockspeed generally affects the choice between an efficient and a responsive returns network.