Improving cold chain logistics performance: Insights from digital twins and multi-source data integration

This quantitative investigation delves into the complex dynamics of cold chain logistics, focusing on enhancing efficiency and resilience by strategically employing multi-source heterogeneous data, digital twins, and seamlessly integrating digital and real systems. By employing a structured questionnaire distributed among supply chain professionals and academic experts in China, encompassing key variables such as temperature control, digital twins, system integration, data standardization, and logistics network structure, the analysis seeks to comprehensively understand the interplay between these factors. Statistical analysis utilizing Statistical Package for Social Sciences (SPSS) and SmartPLS 3 software tools enables the identification of significant correlations and pathways between the variables under investigation. The findings shed light on several critical aspects of cold chain logistics optimization. The investigation revealed a positive association between enhanced temperature control measures and improvements in both operational efficiency and resilience within the logistics network. It demonstrates the efficacy of integrating digital twins in enhancing predictive analytics and mitigating risks effectively. The seamless integration of digital and real systems is found to expedite response times to disruptions, enhancing the overall agility of the logistics network. Furthermore, the study underscores the importance of data standardization efforts in promoting interoperability and collaboration among stakeholders. This study contributes to existing theoretical frameworks by integrating the Socio-Technical Systems Theory and the Resource-Based View. The practical implications of this study suggest that supply chain managers should leverage digital twins, data integration, and standardized processes to enhance temperature control, mitigate risks, improve visibility, and drive operational efficiency and resilience in cold chain logistics.