Inventory control strategy based on neural network and fuzzy algorithm in intelligent warehousing system

• Published in: Discover Artificial Intelligence Vol. 5; no. 1; pp. 159 - 27
• Main Authors: Xie, Chunmei, Xie, Cong
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2025; Springer Nature B.V; Springer
• Subjects: Ablation; Accuracy; Algorithms; Architecture; Artificial Intelligence; Computer Science; Cost control; Decision making; Efficiency; Engineering; Fuzzy logic; Fuzzy logic algorithm; Intelligent warehousing; Inventory; Inventory control; Inventory management; Neural networks; Neuro-fuzzy model; Optimization; R&D; Random variables; RBFNN; Research & development; Supply chains; United States > US; Fuzzy logic algorithm; Neuro-fuzzy model; Inventory control; RBFNN; Intelligent warehousing
• ISSN: 2731-0809; 2731-0809
• DOI: 10.1007/s44163-025-00423-5

To cope with the inventory control problem of an intelligent warehousing system, this paper proposes a neuro-fuzzy dynamic inventory regulation model (NFDIRM), which integrates radial basis function neural network (RBFNN) and fuzzy logic algorithm, aiming to solve the problems of poor prediction accuracy and poor decision flexibility of traditional models. The experiment is based on historical inventory data from a large e-commerce platform, encompassing over 500 commodities across three years. NFDIRM is compared with the economic order quantity (EOQ) model and the ARIMA model, and an ablation analysis is conducted. The results show that the comprehensive average inventory turnover rate of NFDIRM is 22.08, the average out-of-stock rate is 2.77%, and the average inventory cost is 324,600 yuan, which is significantly better than the control model. Ablation analysis reveals that after removing the RBFNN module, the comprehensive average turnover rate decreases to 15.65, while the comprehensive average out-of-stock rate increases to 6.93%. After removing the fuzzy logic decision module, the comprehensive average turnover rate drops to 18.0, and the comprehensive average out-of-stock rate rises to 5.1%. The NFDIRM model proposed in this study enhances the accuracy and efficiency of inventory control, offering a novel solution for intelligent warehouse inventory management. However, the applicability of the model in different industry scenarios still needs to be verified by further research.