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Contracting for Infrequent Restoration and Recovery of Mission-Critical Systems

Author

Listed:
  • Sang-Hyun Kim

    () (Yale School of Management, Yale University, New Haven, Connecticut 06511)

  • Morris A. Cohen

    () (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Serguei Netessine

    () (INSEAD, 77305 Fontainebleau, France)

  • Senthil Veeraraghavan

    () (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

Abstract

Firms that rely on functioning mission-critical equipment for their businesses cannot afford significant operational downtime due to system disruptions. To minimize the impact of disruptions, a proper incentive mechanism has to be in place so that the suppliers provide prompt restoration and recovery services to the customer. A widely adopted incentive mechanism is performance-based contracting (PBC), in which suppliers receive compensation based on realized system uptime. A key obstacle is that disruptions occur infrequently, making it very expensive for a supplier to commit the necessary resources for recovery because they will be idle most of the time. In this paper, we show that designing a successful PBC creates nontrivial challenges that are unique to this environment. Namely, because of the infrequent and random nature of disruptions, a seemingly innocuous choice of performance measures used in contracts may create unexpected incentives, resulting in counterintuitive optimal behavior. We compare the efficiencies of two widely used contracts, one based on sample-average downtime and the other based on cumulative downtime, and identify the supplier's ability to influence the frequency of disruptions as an important factor in determining which contract performs better. We also show that implementing PBC may create high agency cost when equipment is very reliable. This counterintuitive situation arises because the realized downtimes from which the customer might intuit about the supplier's capacity investment are highly uncertain when there are not many samples of downtimes, i.e., when disruptions occur rarely.

Suggested Citation

  • Sang-Hyun Kim & Morris A. Cohen & Serguei Netessine & Senthil Veeraraghavan, 2010. "Contracting for Infrequent Restoration and Recovery of Mission-Critical Systems," Management Science, INFORMS, vol. 56(9), pages 1551-1567, September.
  • Handle: RePEc:inm:ormnsc:v:56:y:2010:i:9:p:1551-1567
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    File URL: http://dx.doi.org/10.1287/mnsc.1100.1193
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    References listed on IDEAS

    as
    1. Sang-Hyun Kim & Morris A. Cohen & Serguei Netessine, 2007. "Performance Contracting in After-Sales Service Supply Chains," Management Science, INFORMS, vol. 53(12), pages 1843-1858, December.
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    10. Stephen M. Gilbert & Z. Kevin Weng, 1998. "Incentive Effects Favor Nonconsolidating Queues in a Service System: The Principal--Agent Perspective," Management Science, INFORMS, vol. 44(12-Part-1), pages 1662-1669, December.
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    Cited by:

    1. repec:eee:proeco:v:194:y:2017:i:c:p:88-101 is not listed on IDEAS
    2. repec:eee:ejores:v:264:y:2018:i:1:p:239-256 is not listed on IDEAS
    3. Hezarkhani, Behzad, 2017. "Optimal design of uptime-guarantee contracts under IGFR valuations and convex costs," European Journal of Operational Research, Elsevier, vol. 256(2), pages 556-566.
    4. Wang, Yulan & Wallace, Stein W. & Shen, Bin & Choi, Tsan-Ming, 2015. "Service supply chain management: A review of operational models," European Journal of Operational Research, Elsevier, vol. 247(3), pages 685-698.

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