Optimizing inventory management: a causal inference-driven Bayesian network with transfer learning adaptation

Inventory management faces increasing challenges, including data limitations and demand uncertainty. To enhance inventory forecasting and optimization in supply chain management, this study proposes a Transfer-learning Bayesian Network (TBN) framework that integrates causal inference and transfer learning. Unlike traditional inventory forecasting models that rely on historical data patterns, the proposed framework introduces a causal inference-based Bayesian network to establish explicit causal relationships between sales volume, sales revenue, and inventory levels. To address data scarcity and improve generalization, a novel transfer learning mechanism is incorporated, leveraging a balanced weight coefficient method to optimize model adaptation from a source domain to a target domain. The results indicate that the proposd approach ensures effective knowledge transfer and maintains prediction accuracy with limited training data. The TBN model consistently outperforms traditional machine learning methods and other Bayesian-based models. On the self-constructed dataset, the TBN framework achieved a mean squared error (MSE) of 4.97 and a mean absolute error (MAE) of 2.78, demonstrating superior predictive accuracy. Additionally, an analysis of the balance weight coefficient further validated its role in enhancing transfer learning efficiency and model robustness, which provides a scalable and adaptable solution for intelligent inventory management in supply chain systems.