Resilient Supply Chain Framework for Semiconductor Distribution and an Empirical Study of Demand Risk Inference

• Main Authors: Fu, W. (Author), Jing, S. (Author), Liu, Q. (Author), Zhang, H. (Author)
• Format: Article
• Language: English
• Published: MDPI 2023
• Subjects: data analytics; demand forecast; intelligent decision making; risk analysis; supply chain resilience
• Online Access: View Fulltext in Publisher; View in Scopus
• ISBN: 20711050 (ISSN)
• DOI: 10.3390/su15097382

Supply chain uncertainty is high due to low information transparency in the upstream and downstream, long lead time for supply chain planning, short product life cycles, lengthy production cycle time, and continuous technology migration. The construction and innovation of the new program of supply the chain faces huge challenges. This study aims to propose a smart resilient supply chain framework with a decision-making schema through the plan-do-check-act management cycle. It can enhance supply chain resilience and strengthen industrial competitiveness. Moreover, an empirical study of demand forecast and risk inference for semiconductor distribution is conducted as a validation. Through demand pattern clustering and forecasting for historic customer order behaviors, the demand status of each customer is classified, and an optimal planning solution is released to support decision-making. The result has shown the practical viability of the proposed approach to drive collaborative efforts in enhancing demand risk management to improve supply chain resilience. The proposed forecast model performs better than all four benchmark models, and the revised recall of the proposed risk reference model shows high accuracy in all demand risk levels. As supply chain resilience is about to be reconstructed due to the industrial revolution, a government and industry alliance should follow the resilient supply chain blueprint to gradually make the manufacturing strategy a technology platform in the Industry 4.0 era. © 2023 by the authors.