In the context of supply chain 4.0, Data Science (DS) can improve operations and inventory management by using statistical and machine learning techniques. Additionally, big data can reveal valuable insights for predictive and prescriptive analytics, which can aid in enhancing competitiveness in today’s business environment. Accurate demand forecasting in supply chain inventory is a fundamental step to improve inventory management, address ordering uncertainties, and minimize costs while meeting customer demands. This requires the efficient use of demand forecasting tools, assessing predictive data analysis techniques. Challenges can arise from inefficient ordering models, selecting accurate forecasting models, and considering issues like over-and underestimation leading to food waste and profit margin impacts, particularly for products with short shelf life. Other challenges include enhancing inventory optimization efficiency, adapting to dynamic demands, and formulating optimal inventory decisions. In this work, we introduce a supply chain 4.0 inventory management approach, where we combined DS techniques, predictive analytics, and big data approach to enhance inventory control. A prediction model is also introduced in order to forecast incoming and outgoing inventory. This model is based on a detailed dataset that takes into account the districts and seasons data. The model’s performance was evaluated based on performance measurements, such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Significantly, the results show that the random forest algorithm was the best in predicting OUT inventory quantities with an average error equal to 0.011, while the linear regression algorithm produced the best performance in predicting IN inventory quantities with an average error of 0.03.

[1] Afridi M., Nieto-Isaza S., Ehm H., Ponsignon T., and Hamed A., “A Deep Reinforcement Learning Approach for Optimal Replenishment Policy in a Vendor Managed Inventory Setting for Semiconductors,” in Proceedings of the Winter Simulation Conference, Orlando, pp. 1753-1764, 2020. DOI:10.1109/WSC48552.2020.9384048

[2] Baradieh K., Zainuri M., Kamari M., Yusof Y., Abdullah H., Zaman M., and Zulkifley M., “RSO Based Optimization of Random Forest Classifier for Fault Detection and Classification in Photovoltaic Arrays,” The International Arab Journal of Information Technology, vol. 21, no. 4, pp. 636-660, 2024. https://doi.org/10.34028/iajit/21/4/8

[3] Calero Mantilla F. and Esposito A., End-of-Life Inventory Optimization during Runout Events for a Manufacturing Automotive Company, Master Thesis, Massachusetts Institute of Technology 2023. https://dspace.mit.edu/handle/1721.1/107234

[4] Chandrika V., A Simulation Based Inventory Optimization Heuristic for a Retail Supply Chain, Master Thesis, University of Cincinnati, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin 1439309194

[5] Chinello E., Herbert-Hansen Z., and Khalid W., “Assessment of the Impact of Inventory Optimization Drivers in a Multi-Echelon Supply Chain: Case of a Toy Manufacturer,” Computers and Industrial Engineering, vol. 141, pp. 106232, 2020. https://doi.org/10.1016/j.cie.2019.106232

[6] Dehaybe H., Catanzaro D., and Chevalier P., “Deep Reinforcement Learning for Inventory Optimization with Non-Stationary Uncertain Demand,” European Journal of Operational Research, vol. 314, no. 2, pp. 433-445, 2024. https://doi.org/10.1016/j.ejor.2023.10.007

[7] Dittrich M. and Fohlmeister S., “A Deep Q- Learning-Based Optimization of the Inventory Control in a Linear Process Chain,” Production Engineering Research and Development, vol. 15, no. 1, pp. 35-43, 2021. https://link.springer.com/article/10.1007/s11740- 020-01000-8#citeas

[8] Duc N., Tai P., and Buddhakulsomsiri J., “Inventory Positioning in Supply Chain Network: A Service-Oriented Approach,” IEEE Access, vol. 10, pp. 92986-93002, 2022. https://ieeexplore.ieee.org/abstract/document/986 9807

[9] Fernandez-Carames T., Blanco-Novoa O., Froiz- Miguez I., and Fraga-Lamas P., “Towards an Autonomous Industry 4.0 Warehouse: A UAV and Blockchain-Based System for Inventory and Traceability Applications in Big Data-Driven Supply Chain Management,” Sensors, vol. 19, no. 10, pp. 2394, 2019. https://www.mdpi.com/1424- 8220/19/10/2394

[10] Gao J., Zheng Y., and Mao X., “A Joint Decision Model of Dynamic Pricing and Dynamic Inventory in an Apparel Supply Chain with Demand Learning,” in Proceedings of the International Conference on Logistics Systems and Intelligent Management, Harbin, pp. 692-696, 2010. DOI:10.1109/ICLSIM.2010.5461329

[11] Gattinoni S., Supply Chain Planning: Inventory Optimization through the Demand Forecasting Tools and Algorithm, Master Thesis, Politecnico di Milano, 2022. https://hdl.handle.net/10589/209494

[12] Guo X., Liu C., Xu W., Yuan H., and Wang M., “A Prediction-Based Inventory Optimization Using Data Mining Models,” in Proceedings of the 7th International Joint Conference on Computational Sciences and Optimization, Beijing, pp. 611-615, 2014. https://ieeexplore.ieee.org/document/6923759

[13] Gurnani P., Hariani D., Kalani K., Mirchandani P., and CS L., Data Intelligence and Cognitive Informatics, Springer, 2022. https://link.springer.com/chapter/10.1007/978- 981-16-6460-1_41

[14] Jahani H., Jain R., and Ivanov D., “Data Science and Big Data Analytics: A Systematic Review of Methodologies Used in the Supply Chain and Logistics Research,” Annals of Operations Research, pp. 1-58, 2023. https://doi.org/10.1007/s10479-023-05390-7

[15] Kandemir B., “A Methodology for Clustering Items with Seasonal and Non-Seasonal Demand Patterns for Inventory Management,” Journal of Advanced Research in Natural and Applied 1004 The International Arab Journal of Information Technology, Vol. 21, No. 6, November 2024 Sciences, vol. 8, no. 4, pp. 753-761, 2022. https://dergipark.org.tr/en/download/article- file/2409588

[16] Keith E., “Optimizing Inventory Management through Advanced Forecasting Techniques in Supply Chains,” European Journal of Supply Chain Management, vol. 1, no. 1, pp. 22-30, 2023. https://forthworthjournals.org/journals/index.php/ EJSCM/article/view/38

[17] Kim J. and Jeong J., “Simulation Evaluation for Efficient Inventory Management Based on Demand Forecast,” in Proceedings of the 18th International Conference on Computational Science and its Applications, Melbourne, pp. 639- 650, 2018. https://link.springer.com/chapter/10.1007/978-3- 319-95162-1_44

[18] Li J., Wang T., Chen Z., and Luo G., “Machine Learning Algorithm Generated Sales Prediction for Inventory Optimization in Cross-Border E- Commerce,” International Journal of Frontiers in Engineering Technology, vol. 1, no. 1, pp. 62-74, 2019. DOI: 10.25236/IJFET.2019.010107

[19] Makarova O., “Predictive Method of Inventory Optimization in Industrial Manufacturer Performance,” E3S Web of Conferences, vol. 247, pp. 1-4, 2021. https://doi.org/10.1051/e3sconf/202124701066

[20] Mankar A. and Khan A., “A Review of Inventory Management Research in Transport and Logistics,” in Proceedings of the 14th International Conference on Computing Communication and Networking Technologies, Delhi, pp. 1-7, 2023. DOI:10.1109/ICCCNT56998.2023.10307815

[21] Mohamadi N., Niaki S., Taher A., and Shavandi A., “An Application of Deep Reinforcement Learning and Vendor-Managed Inventory in Perishable Supply Chain Management,” Engineering Applications of Artificial Intelligence, vol. 127, pp. 107403, 2024. https://doi.org/10.1016/j.engappai.2023.107403

[22] Namir K., Labriji H., and Ben Lahmar A., “Decision Support Tool for Dynamic Inventory Management Using Machine Learning, Time Series and Combinatorial Optimization,” Procedia Computer Science, vol. 198, pp. 423- 428, 2022. https://doi.org/10.1016/j.procs.2021.12.264

[23] Nasution A., Matondang N., and Ishak A., “Inventory Optimization Model Design with Machine Learning Approach in Feed Mill Company,” Jurnal Sistem Teknik Industri, vol. 24, no. 2, pp. 254-272, 2022. https://doi.org/10.32734/jsti.v24i2.8637

[24] Poi E. and Opara B., “Inventory Optimization and Customer Satisfaction of Petroleum Marketing Firms in Rivers State,” Journal of Accounting, Marketing and Information Technology, vol. 8, no. 2, pp. 118-133, 2021. https://www.researchgate.net/publication/356617 026

[25] Purnamasari D., Permadi V., Saefudin A., and Agusdin R., “Demand Forecasting for Improved Inventory Management in Small and Medium- Sized Businesses,” JANAPATI, vol. 12, no. 1, pp. 56-66., 2023. https://doi.org/10.23887/janapati.v12i1.57144

[26] Qiu R., Sun Y., and Sun M., “A Distributionally Robust Optimization Approach for Multi-Product Inventory Decisions with Budget Constraint and Demand and Yield Uncertainties,” Computers and Operations Research, vol. 126, pp. 105081, 2021. https://doi.org/10.1016/j.cor.2020.105081

[27] Sarath Kumar B. and Ramana B., “Business Intelligence and Analytics in Supply Chain Management,” Turkish Journal of Computer and Mathematics Education, vol. 10, no. 1, pp. 666- 670, 2019. https://turcomat.org/index.php/turkbilmat/article/ view/14266

[28] Seyedan M., Mafakheri F., and Wang C., “Order- up-to-Level Inventory Optimization Model Using Time-Series Demand Forecasting with Ensemble Deep Learning,” Supply Chain Analytics, vol. 3, pp. 100024, 2023. https://doi.org/10.1016/j.sca.2023.100024

[29] Shao X., Chang D., and Li M., “Optimization of Lateral Transfer Inventory of Auto Spare Parts Based on Neural Network Forecasting,” Journal of Intelligent Systems and Control, vol. 1, no. 1, pp. 1-17, 2022. https://doi.org/10.56578/jisc010102

[30] Sihotang J., “Optimization of Inventory Ordering Decision in Retail Business Using Exponential Smoothing Approach and Decision Support System,” International Journal of Mechanical Computational and Manufacturing Research, vol. 12, no. 2, pp. 46-52, 2023. https://doi.org/10.35335/computational.v12i2.121

[31] Stanelyte G., Inventory Optimization in Retail Network by Creating a Demand Prediction Model, Master Thesis, Vilnius Gedimino Technicus University, 2021. https://vb.vgtu.lt/object/elaba:80509157/

[32] Tang Y., Chau K., Lau Y., and Zheng Z., “Data- Intensive Inventory Forecasting with Artificial Intelligence Models for Cross-Border E- Commerce Service Automation,” Applied Science, vol. 13, no. 5, pp. 1-13, 2023. https://doi.org/10.3390/app13053051

[33] Teerasoponpong S. and Sopadang A., “Decision Support System for Adaptive Sourcing and Inventory Management in Small and Medium- Sized Enterprises,” Robotics and Computer- Integrated Manufacturing, vol. 73, pp. 102226, Inventory Optimization Using Data Science Technologies for Supply Chain 4.0 1005 2022. https://doi.org/10.1016/j.rcim.2021.102226

[34] Tian R., Lu M., Wang H., Wang B., and Tang Q., “IACPPO: A Deep Reinforcement Learning- Based Model for Warehouse Inventory Replenishment,” Computers and Industrial Engineering, vol. 187, pp. 109829, 2024. https://doi.org/10.1016/j.cie.2023.109829

[35] Tirkolaee E., Mahmoodkhani J., Bourani M., and Tavakkoli-Moghaddam R., “A Self-Learning Particle Swarm Optimization for Robust Multi- Echelon Capacitated Location-Allocation- Inventory Problem,” Journal of Advanced Manufacturing Systems, vol. 18, no. 4, pp. 677- 694, 2019. https://doi.org/10.1142/S0219686719500355

[36] Valencia-Cardenas M., Diaz-Serna F., and Correa-Morales J., “Multi-Product Inventory Modeling with Demand Forecasting and Bayesian Optimization,” DYNA, vol. 83, no. 198, pp. 235- 243, 2016. https://revistas.unal.edu.co/index.php/dyna/article /view/51310/57375

[37] Waller M. and Fawcett S., “Data Science, Predictive Analytics, and Big Data: A Revolution that will Transform Supply Chain Design and Management,” Journal of Business Logistics, vol. 34, no. 2, pp. 77-84, 2013. https://doi.org/10.1111/jbl.12010

[38] Wu G., De Carvalho-Servia, and Mowbray M., “Reinforcement Learning for Inventory Management in Multi-Echelon Supply Chains,” in Computer Aided Chemical Engineering, vol. 52, pp. 795-800, 2023. https://doi.org/10.1016/B978- 0-443-15274-0.50127-X

[39] Yuan X., Impact of Industry 4.0 on Inventory Systems and Optimization, IntechOpen, 2020. https://www.intechopen.com/chapters/70417

[40] Zhao Z. and Wu C., “Wheat Quantity Monitoring Methods Based on Inventory Measurement and SVR Prediction Model,” Applied Sciences, vol. 13, no. 23, pp. 12745-12745, 2023. https://doi.org/10.3390/app132312745