A quantitative and simulation model for managing sudden supply delay with fuzzy demand and safety stock

In this paper, a recovery model is developed for managing sudden supply delays that affect retailers’ economic order quantity model. For this, a mathematical model is developed that considers fuzzy demand and safety stock, and generates a recovery plan for a finite future period immediately after a sudden supply delay. An efficient heuristic solution is developed that generates the recovery plan after a sudden supply delay. An experiment with scenario-based analysis is conducted to test our heuristic and to analyse the results. To assess the quality and consistency of solutions, the performance of the proposed heuristic is compared with the performance of the generalised reduced gradient method, which is widely applied in constrained mathematical programming. A simulation model is also designed to bring the recovery model closer to real-world processes. Several numerical examples are presented and a sensitivity analysis is performed to demonstrate the effects of various parameters on the performance of the heuristic method. The results show that safety stock plays an important role in recovery from sudden supply delays, and there is a trade-off between backorder and lost sales costs in the recovery plan. With the help of the proposed model, supply chain decision-makers can make accurate and prompt decision regarding recovery plans in case of sudden supply delay.

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