Multiple order-up-to policy for mitigating bullwhip effect in supply chain network

Keshari, Anupam, Mishra, Nishikant, Shukla, Nagesh, McGuire, Steven and Khorana, Sangeeta (2018) Multiple order-up-to policy for mitigating bullwhip effect in supply chain network. Annals of Operations Research, 269 (1-2). pp. 361-386. ISSN 0254-5330

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This paper proposes a multiple order-up-to policy based inventory replenishment scheme to mitigate the bullwhip effect in a multi-stage supply chain scenario, where various transportation modes are available between the supply chain (SC) participants. The proposed policy is similar to the fixed order-up-to policy approach where replenishment decision “how much to order” is made periodically on the basis of the predecided order-up-to inventory level. In the proposed policy, optimal multiple order-up-to levels are assigned to each SC participants, which provides decision making reference point for deciding the transportation related order quantity. Subsequently, a mathematical model is established to define optimal multiple order-up-to levels for each SC participants that aims to maximize overall profit from the SC network. In parallel, the model ensures the control over supply chain pipeline inventory, high satisfaction of customer demand and enables timely utilization of available transportation modes. Findings from the various numerical datasets including stochastic customer demand and lead times validate that—the proposed optimal multiple order-up-to policy based inventory replenishment scheme can be a viable alternative for mitigating the bullwhip effect and well-coordinated SC. Moreover, determining the multiple order-up-to levels is a NP hard combinatorial optimization problem. It is found that the implementation of new emerging optimization algorithm named bacterial foraging algorithm (BFA) has presented superior optimization performances. The robustness and applicability of the BFA algorithm are further validated statistically by employing the percentage heuristic gap and two-way ANOVA analysis.

Item Type: Article
Schools and Departments: University of Sussex Business School > Strategy and Marketing
Depositing User: Joy Blake
Date Deposited: 26 Jun 2017 09:17
Last Modified: 04 Nov 2019 12:36

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