ABSTRACT
The global pandemic of COVID-19 has caused severe damage to the supply chain, and manufacturers may face long-term supply disruptions. A new product design change program taking into account product life cycle and lead time is introduced and incorporated into a disruption recovery model for a serial supply chain that minimizes manufacturer losses after supply chain disruption. A mixed-integer linear programming (MILP) model is presented addressing this multi-period, multi-supplier, and multi-stage problem with long-term supply disruptions. A heuristic algorithm is designed to solve the model. In a numerical example, five disruption scenarios of the recovery model are solved. The results show that the proposed disruption recovery strategy can effectively reduce the profit loss of manufacturer due to supply disruption, and demonstrate the role of the product life cycle in the selection of product design change options. The proposed model can offer a potentially useful tool to help the manufacturers decide on the optimal recovery strategy whenever the supply chain system experiences a massive disruption.
ABSTRACT
The global pandemic of COVID-19 has caused severe damage to the supply chain such that manufacturers may face long-term supply disruptions. In this paper, a disruption recovery strategy of a supply chain system is investigated from the perspective of product change, in which the life cycle and design change time of a new product are both considered in order to minimize the losses of manufacturer after disruptions. A mixed-integer linear programming (MILP) model is presented to address the disruption recovery problem for this multi-period, multi-supplier, and multi-stage supply chain system. A two-stage heuristic algorithm is designed to solve the problem. Experimental results show that the proposed disruption mitigation strategy can effectively reduce the profit loss of manufacturer due to supply disruption, and demonstrate the impact of product life cycle in the selection of new product design planning. A sensitivity analysis is performed to ensure the applicability of the model in the actual environment, which illustrates the effect of different parameter changes on the results. This work can help manufacturers establish an optimal recovery strategy whenever the supply chain system experiences supply disruptions.