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Effective management of performance-based contracts for sustainment dominant systems

Author

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  • Patra, Pradipta
  • Kumar, U. Dinesh
  • Nowicki, David R.
  • Randall, Wesley S.

Abstract

Performance-based contracting (PBC) is becoming the favoured procurement strategy among suppliers and customers of sustainment dominant systems (SDS) such as aircraft, weapon systems, mining equipment, etc. Under a PBC, supplier profit is linked to how well the SDS meets customer expectations in terms of relevant performance metrics. System availability is a common, contractually agreed upon performance metric that measures customer expectations. In this paper, we analyse performance contracts from the perspective of the supplier, where system availability serves as the performance metric. We develop single-period and multi-period supplier performance models and demonstrate how these models are used to maximise a supplier's profit in the context of a PBC. We provide empirical evidence from a mining and construction equipment industry and demonstrate how PBC overcomes the information asymmetry and moral hazard that is common in SDS principal agent models. Practically, our models can be used to increase the likelihood of success for both the supplier and the customer when they embrace PBC. Our main contributions include establishing the optimal availability that an original equipment manufacturer (OEM) can provide by considering their own probability of loss; analysing the properties of an OEM's future net profit function using first order autoregressive moving average (ARMA) process; and performing marginal analysis and providing bounds for the net profit function for linear and non-linear revenue functions. We derive a relationship between macro-level availability and micro-level parameters such as failure rate, fleet size and base stock levels.

Suggested Citation

  • Patra, Pradipta & Kumar, U. Dinesh & Nowicki, David R. & Randall, Wesley S., 2019. "Effective management of performance-based contracts for sustainment dominant systems," International Journal of Production Economics, Elsevier, vol. 208(C), pages 369-382.
  • Handle: RePEc:eee:proeco:v:208:y:2019:i:c:p:369-382
    DOI: 10.1016/j.ijpe.2018.11.025
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    3. Ming Zhang & Jianjun Zhu & Ponnambalam Kumaraswamy & Hehua Wang, 2019. "Evolutionary Game Analysis of the Effects of Problem Size and the Problem Proposing Mechanism on the Problem Processing Mechanism in a New Main Manufacturer–Supplier Collaborative System," Mathematics, MDPI, vol. 7(7), pages 1-19, July.

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