Abstract
Manufacturers should repurpose their supply chain quickly in response to a new demand, which reflects a surge in market needs over a certain period of time. The ability to meet this demand depends on the reconfigurability of manufacturing supply chains across products; however, many industries do not have the quantitative tools for evaluating the reconfigurability. Repurposing manufacturing supply chains across industries (e.g., manufacturers from the auto and health industries) poses a unique challenge since existing suppliers should be reutilized to meet the new demand. This paper establishes a methodology that addresses the supply chain repurposing problem by identifying a new supply chain composed of pieces of existing ones to meet the new demand where cross-product reconfiguration is required. First, the supernetwork framework is used to represent the joint information in the manufacturing supply chain, including assembly task planning and manufacturer–supplier assignment. Next, a supernetwork similarity metric is proposed to assess the reconfigurability of existing supply chains across industries when detailed reconfiguration cost data are unavailable. Guided by the similarity metric, a nonlinear integer programming model is developed for joint decision-making in selecting assembly plans and supply chains to create a product in demand, leveraging existing supply chains of other products. The problem is solved by an evolutionary algorithm customized for the supply chain repurposing problem studied herein. The paper concludes with a case study demonstrating how five manufacturing supply chains that produce different products can be repurposed to generate an intubation ventilator system.
