Optimizing supply chain cooperation with rebate mechanisms under demand and price uncertainty

Sibo Ding (), Linshuang Yuan () and Xiao-Jun Zeng ()
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Sibo Ding: Henan University of Technology, School of Management
Linshuang Yuan: Henan University of Technology, School of Management
Xiao-Jun Zeng: University of Manchester, Department of Computer Science

Fuzzy Optimization and Decision Making, 2025, vol. 24, issue 4, No 4, 669-693

Abstract: Abstract This study investigates the optimization of supply chain cooperation using rebate mechanisms under conditions of demand and price uncertainty. By employing uncertainty theory to model the interdependence of these key factors, the main contribution of this research is the development and evaluation of an effective rebate range. This range provides a more adaptable and resilient approach to encouraging collaboration between manufacturers and retailers compared to fixed rebate systems. Through numerical simulations, the study shows that changes in critical parameters, such as the retailer’s expectations of the manufacturer’s supply, production costs, wholesale price, and initial market selling price, have a substantial impact on the optimal order quantity, the boundaries of the rebate range, and the overall performance of the supply chain. The adoption of this rebate range promotes cooperative behavior, facilitates fair distribution of benefits, strengthens resilience in uncertain market conditions, advances the theoretical understanding of rebate mechanisms, and offers practical decision-making support for managers crafting flexible strategies to improve supply chain cooperation efficiency.

Keywords: Rebate mechanisms; Uncertainty theory; Demand uncertainty; Price uncertainty; Supply chain cooperation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10700-025-09458-w

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