Performance Contracting in After-Sales Service Supply Chains

Performance-based contracting is reshaping service support supply chains in capital intensive industries such as aerospace and defense. Known as Power by the Hour in the private sector and as Performance-Based Logistics (PBL) in defense contracting, it aims to replace traditionally used …xed-price and cost-plus contracts in order to improve product availability and reduce the cost of ownership by tying a supplier’s compensation to the output value of the product generated by the customer (buyer). To analyze implications of performance-based relationships, we introduce a multitask principalagent model to support resource allocation and use it to analyze commonly observed contracts. In our model the customer (principal) faces a product availability requirement for the “uptime”of the end product. The customer then o¤ers contracts contingent on availability to n suppliers (agents) of the key subsystems used in the product, who in turn exert cost reduction e¤orts and set spare parts inventory investment levels. We show that the …rst-best solution can be achieved if channel members are risk-neutral. When channel members are risk-averse, we …nd that the second-best contract combines a …xed payment, a cost-sharing incentive, and a performance incentive. Furthermore, we study how these contracts evolve over the product deployment life cycle as uncertainty in support cost changes. Finally, we illustrate the application of our model to a problem based on aircraft maintenance data and show how the allocation of performance requirements and contractual terms change under various environmental assumptions. The authors are grateful to the seminar participants at the Wharton School, Cornell University, University of Texas at Dallas, University of Washington, Columbia University, UC Berkeley, and Naval Postgraduate School for helpful discussions. The authors would also like to acknowledge input from L. Gill, S. Gutierrez, M. Lebeau and M. Mendoza, who provided valuable information concerning current practices. Finally, the authors are grateful for the assistance of Ashish Achlerkar of MCA Solutions, who provided valuable assistance in testing the model and providing access to a real-world data set. This research was supported by Fishman-Davidson Center for Service and Operations Management at the Wharton School and partially funded by a grant from the National Science Foundation (Grant 540075-8001). yEmail: [email protected], [email protected], [email protected]