Elevating Customer Experiences and Maximizing Profits with Predictable Stockout Prevention Modelling
Keywords:
Inventory management, LSTM, Gradient Boosting, Random Forest, Ensemble learning algorithmsAbstract
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.
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Zhang, Tao & Lin, Wuyin & Vogelmann, Andrew & Zhang, Minghua & Xie, Shaocheng & Qin, Yi & Golaz, Jean‐Christophe. (2021). Improving Convection Trigger Functions in Deep Convective Parameterization Schemes Using Machine Learning. Journal of Advances in Modeling Earth Systems. 13. 10.1029/2020MS002365.
Sapountzoglou, Nikolaos & Lago, Jes & Raison, Bertrand. (2020). Fault diagnosis in low voltage smart distribution grids using gradient boosting trees. Electric Power Systems Research. 182. 106254. 10.1016/j.epsr.2020.106254.
Kurian, Dony S., C. R. Maneesh, and V. Madhusudanan Pillai. "Supply chain inventory stockout prediction using machine learning classifiers." International Journal of Business and Data Analytics 1.3 (2020): 218-231.
Shukla, Saurav, and V. Madhusudanan Pillai. "Stockout Prediction in Multi Echelon Supply Chain using Machine Learning Algorithms." Proceedings of the 2nd Indian International Conference on Industrial Engineering and Operations Management Warangal, Telangana, India. 2022.
Melançon, Gabrielle Gauthier, et al. "A machine learning-based system for predicting service-level failures in supply chains." INFORMS Journal on Applied Analytics 51.3 (2021): 200-212.
Oroojlooyjadid, Afshin, Lawrence Snyder, and Martin Takáč. "Stock-out prediction in multi-echelon networks." arXiv preprint arXiv:1709.06922 (2017).
Sharma, Sugandha, Deepika Deepika, and Gunwant Singh. "Intelligent Warehouse Stocking Using Machine Learning." 2021 IEEE International Conference on Mobile Networks and Wireless Communications (ICMNWC). IEEE, 2021.
Qi, Meng, et al. "A practical end-to-end inventory management model with deep learning." Management Science 69.2 (2023): 759-773.
Demizu, Tsukasa, Yusuke Fukazawa, and Hiroshi Morita. "Inventory management of new products in retailers using model-based deep reinforcement learning." Expert Systems with Applications 229 (2023): 120256.
Ntakolia, Charis, et al. "An explainable machine learning model for material backorder prediction in inventory management." Sensors 21.23 (2021): 7926.
Kosasih, Edward Elson, and Alexandra Brintrup. "Reinforcement learning provides a flexible approach for realistic supply chain safety stock optimisation." IFAC-PapersOnLine 55.10 (2022): 1539-1544.
Singh, Ranu, and Vinod Kumar Mishra. "Inventory model using Machine Learning for demand forecast with imperfect deteriorating products and partial backlogging under carbon emissions." Annals of Operations Research (2023): 1-23.
Abdali, Nasser, et al. "A priority queueing-inventory approach for inventory management in multi-channel service retailing using machine learning algorithms." Kybernetes (2024).
Andreetta, Massimo. "Machine and Deep Learning Applications for Inventory Replenishment Optimization."
Kharfan, Majd, and Vicky Wing Kei Chan. "Forecasting Seasonal Footwear Demand Using Machine Learning." (2018).
Gonçalves, João Nuno Costa. Towards enhanced safety stock estimation: exploring machine learning strategies for supply chain demand forecasting. Diss. Universidade do Minho (Portugal), 2020.
Gonçalves, João NC, M. Sameiro Carvalho, and Paulo Cortez. "Operations research models and methods for safety stock determination: A review." Operations Research Perspectives 7 (2020): 100164.
Wahedi, Hadid J., et al. "Forecasting and Inventory Planning: An Empirical Investigation of Classical and Machine Learning Approaches for Svanehøj’s Future Software Consolidation." Applied Sciences 13.15 (2023): 8581.
Du Plessis, Marno. Reinforcement learning for inventory management in information-sharing pharmaceutical supply chains. Diss. Stellenbosch: Stellenbosch University, 2020.
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