Allocation Models and Heuristics for the Outsourcing of Repairs for a Dynamic Warranty Population

Li Ding (), Kevin D. Glazebrook () and Christopher Kirkbride ()
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Li Ding: Durham Business School, Durham University, Durham DH1 3LB, United Kingdom
Kevin D. Glazebrook: Department of Management Science, Lancaster University, Lancaster LA1 4YX, United Kingdom
Christopher Kirkbride: Department of Management Science, Lancaster University, Lancaster LA1 4YX, United Kingdom

Management Science, 2008, vol. 54, issue 3, 594-607

Abstract: We consider a scenario in which a large equipment manufacturer wishes to outsource the work involved in repairing purchased goods while under warranty. Several external service vendors are available for this work. We develop models and analyses to support decisions concerning how responsibility for the warranty population should be divided between them. These also allow the manufacturer to resolve related questions concerning, for example, whether the service capacities of the contracted vendors are sufficient to deliver an effective post-sales service. Static allocation models yield information concerning the proportions of the warranty population for which the vendors should be responsible overall. Dynamic allocation models enable consideration of how such overall workloads might be delivered to the vendors over time in a way which avoids excessive variability in the repair burden. We apply dynamic programming policy improvement to develop an effective dynamic allocation heuristic. This is evaluated numerically and is also used as a yardstick to assess two simple allocation heuristics suggested by static models. A dynamic greedy allocation heuristic is found to perform well. Dividing the workload equally among vendors with different service capacities can lead to serious losses.

Keywords: approximate dynamic programming; greedy heuristics; index policies; outsourcing; warranty repairs (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (1)

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