In this era of intense global competition, supply chains are facing challenges in coping with emerging market issues. Within diverse industries worldwide, supply chains are experiencing accelerated reconfiguration, with one of the most notable transformations being the digitalization of supply chain operations. But the literature lacks empirical evidence about how digital transformation effectively contributes to it. Thus, this paper delves into the implications of the supply chain digital transformation (SCDT) and supply chain reconfigurability (SCR) on its overall performance. Cross-sectional data from 379 respondents in the machinery, electronics, and home appliance manufacturing industries were collected through a closed questionnaire. Utilizing a hybrid approach involving the Partial Least Squares Structural Equation Model (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA), this study employs a cross-validation of the conceptual model. Initially, the PLS-SEM is employed to unveil the direct impact of SCDT on supply chain performance (SCP), as well as the intermediary effect of SCR. Building on this foundation, the fsQCA method is further utilized to investigate the configuration paths that enable enterprises to achieve high SCP under the combined influence of SCR and SCDT. The research results affirm the significant influence of SCDT on SCP. Likewise, the findings highlight the crucial intermediary role of SCR between SCDT and SCP. Ultimately, three distinct configurations driving high supply chain performance are identified: technical, management, and flexible configurations, each playing a unique role.

In recent years, with the emergence of new technologies, big data, artificial intelligence, and other technologies have had a greater impact on supply chain management. Among them, big data analysis capability, as one of the important capabilities that supply chain enterprises should have, has a particularly significant impact on supply chain resilience management. From the perspective of performance management, based on supply chain resilience theory, the relationship between the supply chain performance management level, supply chain collaboration, and other supply chain resilience elements, as well as big data analysis capability and supply chain performance can be analyzed to study the impact of big data analysis capability on supply chain performance of enterprises of different scales. The impact on the level of supply chain performance is being studied. This paper investigates the problem of supply chain performance evaluation and optimization based on the LMBP algorithm and provides some references for supply chain performance evaluation and optimization.