Warranty Inventory Optimization for Hitachi Global Storage Technologies, Inc

John Khawam (), Warren H. Hausman () and Dinah W. Cheng ()
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John Khawam: Department of Management Science and Engineering, Stanford University, Stanford, California 94305
Warren H. Hausman: Department of Management Science and Engineering, Stanford University, Stanford, California 94305
Dinah W. Cheng: Hitachi Global Storage Technologies, Inc., San Jose, California 95193

Interfaces, 2007, vol. 37, issue 5, 455-471

Abstract: Warranty inventory management is a challenge that many companies must confront. Customers return allegedly defective units to a company for replacement or credit. The company can then economically recover the unit through either a testing or remanufacturing process; it can use recovered units to fulfill future warranty requests. The company also has the option of purchasing a new product from the production line. In high-volume situations, warranty inventory management involves many complexities such as stochastic demand rates, probabilistic requests for credit instead of replacement, probabilistic repairs, multiple sources of supply, and tight customer-service constraints. Companies may also have to consider the complexities that a batch remanufacturing process causes.In this paper, we formulate several related models of such warranty inventory systems. In these models, we study a periodic, single-location, inventory system that is dedicated to warranty returns. We find near-optimal policies for each system using well-developed heuristics. The models include the following complexities: random warranty claims, random requests for replacement or credit, three sources of supply (testing, remanufacturing, and new product), random flows of returned products into testing and remanufacturing, random yields from testing and remanufacturing, different lead times for each resupply process, remanufacturing lead time variability, and random batching of remanufacturing. The results of the models provide near-optimal inventory-control policies in this complex environment and demonstrate the payoffs that result from reducing production lead times and batching in remanufacturing.Hitachi GST has gained a great deal from this modeling process. In addition to the direct benefit from the model's calculations, additional sensitivity analyses have shed light on the quantitative importance of various factors, including demand volatility, the percentage of credit requests, the percentage of units successfully remanufactured, and batching effects in remanufacturing.

Keywords: inventory; applications; stochastic; closed-loop supply chains; warranty returns; periodic; single location; heuristics (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (8)

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