Elevating Customer Experiences and Maximizing Profits with Predictable Stockout Prevention Modelling - Priyanka Koushik, Sumit Mittal (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Preventing stockouts while optimizing revenues is a constant problem for inventory management in retail and supply chain operations. In order to detect stockouts and optimize inventory levels, this study investigates the effectiveness of MLmethods in tackling these problems. Three well-known ML algorithms—Random Forest, GBM, and LSTM—were applied and contrasted using a dataset with 2000 rows and 15 columns that captured various variables linked to inventory management and stockout events. To ascertain how preprocessing methods affected algorithm performance, three different approaches—feature scaling, dimensionality reduction, and no preprocessing—were assessed. The findings show that in terms of accuracy, precision, recall and F1 score, ensemble learning algorithms—in particular, Gradient Boosting and Random Forest—performed better than LSTM. Furthermore, all algorithms performed noticeably better when features were scaled using MinMaxScaler, underscoring the significance of preprocessing in raising model accuracy. These results add to the body of literature by highlighting the importance of preprocessing approaches in the optimization of inventory management strategies and offering empirical proof of the efficacy of ML algorithms in stockout prevention tasks. Businesses can improve customer happiness, improve inventory management procedures, and reduce financial losses from stockouts by utilizing cutting-edge machine learning techniques. This study highlights how ML-based strategies can spur innovation and enhancement in supply chain and retail operations.