Will emergency order shifting perform better than recovery waiting at costs of carbon tax and carbon emission reduction?

In recent years, frequent extreme disasters have challenged supply chain operations while smart risk warning systems are developed to facilitate firms' emergency order shifting to a new manufacturer. It is noted that reliable manufacturers are usually located in countries/regions levying carbon tax to achieve high ESG scores, so we consider a cross-border supply chain consisting of a global brand, a local brand, an overseas manufacturer and a local manufacturer to investigate the main tradeoffs for the global brand to emergently shift orders from the overseas manufacturer facing disruptions to a stable local manufacturer subject to carbon tax cost. The global brand has the option to wait for the recovery of overseas production but if it chooses emergent order shifting, it has to invest in carbon emission reduction due to ESG requirements.

Information value versus flexibility cost: Comparison of dual sourcing and artificial intelligence sourcing for resilient supply.

In global trade practices, varying inspection and quarantine standards frequently cause import disruptions. To manage such customs risk, artificial intelligence (AI)-based intelligent sourcing strategy and traditional dual-sourcing strategy are two widely used strategies to guarantee supply resilience. In this study, we formulate the main trade-offs to adopt AI sourcing, including the information analytics value, the increased flexibility cost, and the altered competition/cooperation structure among the stakeholders.

Enabling emergency production shifting: The value of blockchain in supply chain resilience confronting COVID-19.

Nowadays, factories located in COVID-19 infected countries/regions are facing random outbreaks. If blockchain is adopted, then the outbreaks can be known immediately and emergency production shifting can be enabled, although high crash cost will be incurred. Otherwise, production delay will become inevitable.

Sharing Knowledge to an Entrant for Production Investment Confronting COVID-19: Incentive Alignment and Lose-Lose Dilemma.

Facing the urgent demand of medical devices for COVID-19 treatment, many automakers have recently begun manufacturing ventilators, even though they are inefficient in production and uninformed of demand variability. To help them, some incumbent ventilator manufacturers have chosen to share knowledge, such as production techniques and demand information. Clearly, the incumbent ventilator manufacturers are fulfilling social responsibility, but is their knowledge sharing rewarding, especially when the automakers are entrant rivals? If possible, are win-win situations in the sense of social responsibility and firms' profitability identifiable? In this work, we develop a game-theoretic model in which an incumbent and an entrant ventilator manufacturer engage in two-dimensional competition in production investment and sales volume.

Conflict management in a multinational firm's production shifting decisions.

In recent years, many apparel multinational firms (MNFs) have shifted their production from traditional manufacturing bases (e.g., China) to the emerging ones located in Southeast Asia (e.

Better Demand Signal, Better Decisions? Evaluation of Big Data in a Licensed Remanufacturing Supply Chain with Environmental Risk Considerations.

Big data ability helps obtain more accurate demand signal. However, is better demand signal always beneficial for the supply chain parties? To answer this question, we investigate a remanufacturing supply chain (RSC), where demand uncertainty is significant, and the value to reduce environmental risk is large. Specifically, we focus on a licensed RSC comprising an original equipment manufacturer (OEM) and a third-party remanufacturer (3PR).