4.7 Article

A stochastic programming model with endogenous uncertainty for selecting supplier development programs to proactively mitigate supplier risk

Journal

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.omega.2021.102542

Keywords

Supplier risk mitigation; Stochastic programming; Supplier development program; Benders' decomposition; Greedy algorithm

Funding

  1. China Scholarship Council [20170 6630051]

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This paper investigates the selection of optimal supplier development programs to improve supplier performance and proactively reduce supplier risks for a manufacturer with a limited budget. The study finds that increasing the budget leads to higher profit growth, while increasing the number of available SDPs at lower budget levels results in more profit growth. The research also highlights the importance of considering uncertainty in supplier performance and multiple supplier risks for the firm.
Poor supplier performance can result in delays that disrupt manufacturing operations. By proactively managing supplier performance, the likelihood and severity of supplier risk can be minimized. In this paper, we study the problem of selecting optimal supplier development programs (SDPs) to improve suppliers' performance with a limited budget to proactively reduce supplier risks for a manufacturer. A key feature of our research is that it incorporates the uncertainty in supplier performance in response to SDPs selection decisions. This uncertainty is endogenous (decision-dependent), as the probability of supplier performance depends on the selection of SDPs, which introduces modeling and algorithmic challenges. We formulate this problem as a two-stage stochastic program with decision-dependent uncertainty. We implement a sample-based greedy algorithm and an accelerated Benders' decomposition method to solve the developed model. We evaluate our methodology using the numerical cases of four low-volume, highvalue manufacturing firms. The results provide insights into the effects of the budget amount and of the number of SDPs on the firm's expected profit. Numerical experiments demonstrate that an increase in budget results in profit growth, e.g., 5.09% profit growth for one firm. At a lower budget level, increasing the number of available SDPs results in more profit growth. The results also demonstrate the significance of considering uncertainty in supplier performance and considering multiple supplier risks for the firm. In addition, computational experiments demonstrate that our algorithms, especially our greedy approximation algorithm, can solve large-sized problems in a reasonable time. (C) 2021 Elsevier Ltd. All rights reserved.

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