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.

In the rapidly changing business world, insurance companies should observe long-term operational performance and focus on its resilience. Existing studies rarely investigate the resilience issue of performance under the context of the insurance sector. Hence, this study develops a dynamic data envelopment analysis (DEA) model to measure the operational performance of insurance companies by considering multiple periods. Different from the existing works adopting traditional static DEA, our work develops a dynamic DEA model to estimate performance by incorporating multiple periods via a common objective function. Besides, the measurement for estimating the resilience of performance is further developed. The proposed approach in this study is applied to 25 Chinese insurance companies for three years from 2017 to 2019. The results reveal that most insurance companies’ performances still have improvement potential. And there exists a distinct difference in performance between property and casualty insurance companies and life insurance companies during the observed period. Additionally, the resilience of the performance of most inefficient insurance companies is at a weak level. Finally, some management implications are provided for improving operational performance.