Abstract
The development of data science and sensing technologies provides opportunities to mitigate uncertainty in hybrid manufacturing–remanufacturing systems, and drives manufacturers to invest in intelligent manufacturing (IM). This paper considers the adoption of IM to mitigate classification errors in the end-of-use (EoU) products’ acquisition while improving the operational efficiency of manufacturing–remanufacturing systems. By constructing game models, we jointly investigate the investment, pricing, production and order quantities, and collection decisions in a closed-loop supply chain. Interestingly, we find that IM does not always increase manufacturers’ enthusiasm to engage in remanufacturing, firms’ profits or consumer surplus. It does not always reduce environmental impact either. Analytical results show that IM reduces manufacturers’ enthusiasm to engage in remanufacturing if the initial core misclassification rate is low. Furthermore, we identify key factors that impact the firms’ profits, consumer surplus, and carbon emissions. For a manufacturer producing high-cost new products, investing in IM hurts its profits when the remanufacturing cost savings due to intelligent manufacturing is small and the manufacturing benefit due to IM is not significant. Moreover, investing in IM reduces consumer surplus if the cost of new products is high, and the IM does not lead to significant remanufacturing cost saving and manufacturing benefit increase. The results reveal that investing in IM is preferred from the environmental point of view if the IM-induced manufacturing cost saving is small. Our research provides management insights into the intelligent transformation of manufacturers with hybrid manufacturing–remanufacturing systems, as well as policymakers concerned with value creation for customers and environmental impacts.
Published Version
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