With the development of central-private enterprises integration, selecting suitable key suppliers are able to provide core components for smart complex equipment. We consider selecting suitable key suppliers from matching perspective, for it not only satisfies natural development of smart complex equipment, it is also a good implementation of equipment project in central-private enterprises integration context. In in this paper, we carry out two parts of research, one is evaluation attributes based on comprehensive analysis, and the other is matching process between key suppliers and core components based on the matching attribute. In practical analysis process, we employ comprehensive evaluated analysis methods to acquire relevant attributes for the matching process that follows. In the analysis process, we adopt entropy-maximum deviation method (MDM)-decision-making trial and evaluation laboratory (DEMATEL)-technique for order preference by similarity to an ideal solution (TOPSIS) to obtain a comprehensive analysis. The entropy-MDM is applied to get weight value, DEMATEL is utilized to obtain internal relations, and TOPSIS is adopted to get ideal evaluated solution. We consider aggregating two types of evaluation information according to similarities of smart complex equipment based on the combination between geometric mean and arithmetic mean. Moreover, based on the aforementioned attributes and generalized power Heronian mean operator, we aggregate preference information to acquire relevant satisfaction degree, then combine the constructed matching model to get suitable key supplier. Through comprehensive analysis of selecting suitable suppliers, we know that two-sided matching and information aggregation can provide more research perspectives for smart complex equipment. Through analysis for relevant factors, we find that leading role and service level are also significant for the smart complex equipment development process.

Revenue-sharing (RS) contracts are a common approach in incentivizing innovation of upstream suppliers by addressing the uneven profit distribution between upstream and downstream firms. Considering the possible overconfidence characterizing decision makers in the supply chain, we investigate the effect of the RS contract and the tendency of overconfidence of supply chain members on the investment in R&D of key components of products in the context of an upstream supplier that is a leader in the R&D and production of key components. We find that regardless of the bargaining power of either party, an RS contract can increase the R&D investment in key components. Regarding the effects of overconfidence of either the downstream manufacturer or upstream supplier, an RS contract can increase the R&D investment in key components. Supplier (manufacturer) overconfidence can harm their own profits but increase the profits of the manufacturer (supplier), and when the level of overconfidence is below a certain threshold, the damage to their own profits is less than the increase in each other’s profits, thus benefiting the whole supply chain. In addition, we also find a joint effect of RS contracts and overconfidence: when the bargaining power of the supplier is low, the RS contract has a certain compensatory effect on the loss of their own profits caused by overconfidence.

This paper examines the supply chain resilience of Taiwan Semiconductor Manufacturing Co. (TSMC) using a Bayesian network (BN) model developed from the Supply Chain Operations Reference (SCOR) framework. This hybrid model allows for an integrated analysis of various key performance indicators (KPIs) across TSMC’s supply chain, providing a comprehensive view of its resilience. By simulating multiple disruption scenarios, the study captures the dynamic interactions and cascading effects of disruptions, such as inventory shortages, transportation delays, and labor cost fluctuations. This approach offers a quantitative analysis of TSMC’s resilience under varied scenarios, revealing critical strengths, such as flexibility in resource allocation, as well as vulnerabilities, particularly in response to high-impact events like geopolitical tensions and natural disasters. Insights from this model highlight the areas where strategic improvements can further strengthen resilience. Overall, the research demonstrates the applicability of Bayesian networks as a powerful tool for resilience assessment, not only in TSMC’s context but also as a scalable solution for other high-complexity, high-dependency supply chains within the semiconductor industry. This study contributes valuable knowledge to the broader field of supply chain resilience and advances the methodologies available for industry practitioners and researchers alike.