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The rapid emergence of Industry 4.0 technologies, such as big data, IoT, and AI, has significantly transformed the distribution and logistics landscape. Smart logistics platforms are increasingly adopted in this sector, leading to improvements in various aspects, including demand forecasting accuracy. This study aims to investigate the mediating effects of data compatibility, real-time risk response, and product history tracking on the relationship between a company’s smart logistics platform utilization capability and its data-driven demand forecasting performance. Drawing upon theoretical insights from existing literature, this study develops a research model and corresponding hypotheses. Operationalization of key constructs is achieved through the development of a structured questionnaire based on established measurement scales. To ensure the representativeness of our sample, the survey is distributed to professionals utilizing smart logistics platforms in domestic distribution and logistics companies. Following a rigorous data collection process, a total of 280 valid responses are retained for further analysis. This study aims to highlight the growing importance of smart logistics platform utilization capability as smart technologies advance. Based on the research results, it presents practical situations in the industrial field and provides policy implications for supporting small and medium-sized enterprises (SMEs) to government agencies and local governments.
Kim et al. (Wed,) studied this question.