Strengthening Supply Chain Resilience for Performance: The Role of Big Data Analytics Capabilities and Integrated Logistics Capabilities on Chinese Manufacturing Firms

The main objective of this study is to examine the impact of big data analytics capabilities, including management and personal capabilities, on integrated logistics capabilities, including demand, supply, and information management interface capabilities. Furthermore, we investigate how this interplay influences supply chain resilience and performance. Another aspect of our study is to investigate the impact of big data analytics capabilities on logistics and supply chain operations in the Chinese manufacturing sector. We analyzed the data from 254 Chinese manufacturing companies using Smart PLS 4.0 (PLS-SEM). The research employed a reflective measuring methodology with 32 indicators spanning several characteristics. We used cross-sectional data collection techniques to collect data from the target population. The findings indicate that big data analytics capabilities, encompassing management and personal capabilities, are crucial for enhancing integrated logistics capabilities, including demand, supply, and information management. These capabilities enable accurate forecasting, disruption identification, and strategic planning, leading to more responsive and resilient supply chains. By optimizing logistics processes, they improve customer satisfaction, reduce costs, and enhance transparency, communication, and collaboration with stakeholders. The study also emphasizes the importance of combining logistical and big data analytics capabilities to strengthen supply chain resilience and performance, particularly during crises, ensuring minimal impact on operations and delivery schedules.