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Abstract Digital transformation has become the core engine for high-quality economic development. By reshaping interactive networks among enterprises, suppliers, and customers and establishing a real-time data-sharing ecosystem, digital transformation enables procurement plans to dynamically align with production demands, logistics scheduling to precisely respond to market fluctuations, and inventory strategies to intelligently adapt to supply chain variability. This process propels the transformation of inventory management from experience-driven to data-driven, inevitably exerting a profound impact on enterprise inventory management. However, existing literature still lacks systematic research on the mechanisms through which digital transformation affects enterprise inventory levels. This study focuses on the impact mechanisms of digital transformation on inventory management, addressing two core questions: First, does digital transformation exhibit a significant inventory optimization effect? Second, through what channels does it achieve such optimization?
Based on the Economic Order Quantity (EOQ) model, this paper first constructs a theoretical framework for inventory management, revealing the dynamic correlation mechanism between enterprises’ safety inventory levels and demand uncertainty, lead time length, and lead time uncertainty. Next, leveraging textual data from listed companies’ annual reports, we develop a digital transformation index using the Term Frequency-Inverse Document Frequency (TF-IDF) machine learning method. Empirical analysis via panel fixed-effects models validates the impact of digital transformation on inventory management, transcending the traditional framework of internet technology-driven optimization. Finally, we analyze the underlying mechanisms and heterogeneity across firms in terms of data-driven capabilities and information-sharing practices. The findings indicate that digital transformation optimizes inventory levels by effectively reducing demand uncertainty, shortening and stabilizing lead times through data-driven approaches, intelligent forecasting, and information sharing. Unlike internet technologies, which are confined to optimizing information transmission, digital transformation leverages big data, artificial intelligence, and the Internet of Things (IoT) to deeply exploit data value, reshaping inventory management models from optimizing inventory forecasting to supply chain collaboration. Thus, digital transformation represents not merely “optimized information transmission” but “intelligent decision-making empowerment”, yielding a more far-reaching impact on inventory management. Further analysis shows that the inventory reduction effect of digital transformation is more pronounced in enterprises with strong data generation and acquisition capabilities and high willingness to share information, whereas the effect is relatively weaker in enterprises with restricted data circulation, low social network status, or distant customer geographical proximity.
Digital transformation serves as a critical pathway to enhancing enterprise supply chain competitiveness. The government needs to build a multi-level and systematic support system for enterprise digital transformation. On the one hand, while promoting digital transformation, it should accelerate the construction of digital infrastructure and improve data property rights and transaction regulations to form a coordinated policy implementation framework. On the other hand, it is necessary to adopt differentiated transformation incentives and inventory reduction guidance policies based on the specific conditions of enterprises to enhance the precision and effectiveness of policy implementation. Priority should be given to launching digital transformation pilot projects in enterprises with strong data element integration capabilities and high willingness to share information, while providing targeted subsidies and technological empowerment to enterprises with restricted data circulation.
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Published: 22 July 2025
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