A fair profit distribution as a resilient strategy in supply chains under ripple effect

A fair distribution of profits among primary partners is crucial to promote long-term relationships in global supply chains. In this paper, a fair decision-making is studied in a multi-tier supply chain under propagated disruptions. Instead of a common game-theoretic framework, the fair optimization theory approach is applied to determine an equitably efficient distribution of expected profits among primary partners of a supply chain. A novel stochastic mixed integer program is developed. The fair profit distribution is optimized along with optimal selection of recovery supply portfolios and coordination of production across the supply chain for each disruption scenario to mitigate the impact of the ripple effect. In the supply chain a collaborative partnership is applied to ensure that the expected service level of manufacturer is not less than the service level of each supplier. This study’s contribution and novelty lie in the development of a computationally efficient optimization model for a fair allocation of expected profits in multi-tier supply chains under the ripple effect. The proposed fair optimization approach integrates a multi-tier structure of supply chain, propagated disruptions across the supply chain, recovery portfolio decisions, and the collaborative service requirements. The computational findings indicate that if manufacturer’s objective is to maximize its individual profit, then the associated profits of all primary suppliers may converge to their respective maxima as well as to the fair profit distribution. Moreover, the fair profit distribution under collaborative partnership may ensure coordinated production across the supply chain. Overall, this study demonstrates that supply chain fairness can be a driver of supply chain resiliency. Both, a better coordination of the manufacturer and primary suppliers production, and an efficient collaboration with backup suppliers may improve the supply chain resilience.