ROLE OF TECHNOLOGY IN IMPLEMENTING LEAN IN WAREHOUSE OPERATIONS

Year 2025, Volume: 12 Issue: 2, 70 - 79, 31.12.2025

Naveen Kukkala

https://izlik.org/JA69WG52ZB

Abstract

Purpose- The study explores the role of machine learning and reinforcement learning in optimising lean warehousing practices, focusing on
demand forecasting, inventory optimisation, and stock prioritisation. Lean warehousing aims to reduce waste, cut costs, and maintain
efficient stock levels through data-driven strategies.
Methodology- Three models were applied: Long Short-Term Memory (LSTM) for demand forecasting, reinforcement learning (RL) with
placeholder costs for dynamic inventory management, and K-means clustering for inventory prioritisation. Performance metrics included
RMSE, MAE, total reward, and Silhouette Score to evaluate effectiveness.
Findings- The LSTM model produced accurate demand forecasts with low RMSE (0.048) and MAE (0.025), aligning stock levels with actual
demand. RL recorded a negative reward of −1511.83, highlighting the importance of integrating real-time cost data for better inventory
decisions. K-means achieved a strong Silhouette Score (0.935), effectively supporting ABC inventory classification.
Conclusion- The study demonstrates that machine learning and reinforcement le

Keywords

lean warehousing , machine learning , reinforcement learning , demant forecasting , inventory optimisation , LSTM model , ABC classification , data-driven inventory management

References

• Cagliano, A. C., Mangano, G., Rafele, C., & Grimaldi, S. (2022). Classifying healthcare warehouses according to their performance. A Cluster Analysis-based approach. The International Journal of Logistics Management, 33(1), 311-338.
• Du Plessis, M. (2020). Reinforcement learning for inventory management in information-sharing pharmaceutical supply chains (MEng thesis). Stellenbosch University.
• Falatouri, T., Darbanian, F., Brandtner, P., & Udokwu, C. (2022). Predictive analytics for demand forecasting–a comparison of SARIMA and LSTM in retail SCM. Procedia Computer Science, 200, 993-1003.
• García-Cutrín, J., & Rodríguez-García, C. (2024). Enhancing Corporate Sustainability through Just-In-Time (JIT) Practices: A Meta-Analytic Examination of Financial Performance Outcomes. Sustainability, 16(10), 4025-4039.
• Goncalves, J. N., Cortez, P., & Carvalho, M. S. (2021). K-means clustering combined with principal component analysis for material profiling in automotive supply chains. European Journal of Industrial Engineering, 15(2), 273-294.
• Hajdu, N. (2024). Advancing organizational analytics: A strategic roadmap for implementing machine learning in warehouse management system (Master’s thesis). University of Oulu, Finland. https://urn.fi/URN:NBN:fi:oulu-202406254928
• Jahin, M. A., Shahriar, A., & Amin, M. A. (2024). MCDFN: Supply Chain Demand Forecasting via an Explainable Multi-Channel Data Fusion Network Model Integrating CNN, LSTM, and GRU. arXiv preprint arXiv:2405.15598.
• Khedr, A. M. (2024). Enhancing supply chain management with deep learning and machine learning techniques: A review. Journal of Open Innovation: Technology, Market, and Complexity, 24, 100379.
• Liljeqvist, L. (2021). Using lean practices to develop improvement suggestions for a warehouse – A case study of a company in the perishables industry (Master’s thesis). Lund University. Lund University Publications (LUP). https://lup.lub.lu.se/student-papers/record/9058166
• Maryani, E., Purwanto, A., Kartika, H., Haris, M., Ihsan, N., Fatah, K. M. A., & Pramono, R. (2020). Do Gemba Kaizen and 5S Reinforce Medical Equipment Manufacturing Performance. European Journal of Molecular & Clinical Medicine, 7(7), 41-57.
• Mumani, A. A., Magableh, G. M., & Mistarihi, M. Z. (2021). Decision making process in lean assessment and implementation: a review. Management Review Quarterly, 21, 1-40.
• Cathiyavadi Grahamspet Narendran, V., Seetharaman, A., & Madduletty, K. (2024). Adoption of artificial intelligence techniques for inventory management: A case study in the aviation sector. American Journal of Industrial and Business Management, 14, 783–799. https://doi.org/10.4236/ajibm.2024.145040
• Nielsen, S. (2022). Management accounting and the concepts of exploratory data analysis and unsupervised machine learning: a literature study and future directions. Journal of Accounting & Organizational Change, 18(5), 811-853.
• Olaleye, O. (2024). Machine learning and stochastic simulation for inventory management (Master’s thesis). Massachusetts Institute of Technology. https://dspace.mit.edu/handle/1721.1/155988
• Orelma, K. (2024). Streamlining inventory control through item classification (Master’s thesis). Lappeenranta–Lahti University of Technology. LUT Institutional Repository
• Pacella, M., & Papadia, G. (2021). Evaluation of deep learning with long short-term memory networks for time series forecasting in supply chain management. Procedia CIRP , 99, 604-609.
• Rahman, M. H., Shahariar, M. M., Tanvir, M. S. H., & Roy, J. (2023). Lean Thinking (5S System) in E-commerce Supply Chains: A Path to Inventory Optimization and Work Management. Supply Chain Insider| ISSN: 2617-7420 (Print), 2617-7420 (Online), 9(1).
• Rizky, Y. A. (2023). The Influence of Cluttering, Work Environment, and Implementation of the 5-S Methods on the Performance of Coal Mining Employees. Technium Social Sciences Journal, 52, 212-225.
• Schiro, D., & Rubin, L. L. (2023). Inventory management: A high-level analysis of selected process elements, and factors impacting plan performance (Master’s thesis). Lund University. https://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=9123892&fileOId=9127921
• Shakya, M. (2024). Deep reinforcement learning solutions for multi-period inventory replenishment optimization (Working paper, [Department/Research Center]). Nanyang Technological University.
• Spreitzenbarth, J. M., Bode, C., & Stuckenschmidt, H. (2024). Artificial intelligence and machine learning in purchasing and supply management: A mixed-methods review of the state-of-the-art in literature and practice. Journal of Purchasing and Supply Management, 100896.
• Suárez-Barraza, M. F., Miguel-Dávila, J. A., & Morales-Contreras, M. F. (2021). Application of Kaizen-Kata methodology to improve operational problem processes. A case study in a service organization. International Journal of Quality and service sciences, 13(1), 29-44.
• Yan, Y., Gupta, S., Licsandru, T. C., & Schoefer, K. (2022). Integrating machine learning, modularity and supply chain integration for Branding 4.0. Industrial Marketing Management, 104, 136-149.