Optimizing Multi-Echelon Inventory in SAP Supply Chains with AI and Machine Learning
DOI:
https://doi.org/10.15662/IJARCST.2023.0604003Keywords:
ulti-Echelon Inventory Optimization, AI, Machine Learning, SAP Supply Chains, Predictive Analytics, Reinforcement Learning, Inventory Management, Supply Chain Resilience, ERP, Demand ForecastingAbstract
Multi-echelon inventory optimization (MEIO) plays a critical role in enhancing the efficiency and responsiveness of supply chains by coordinating inventory management across multiple stages, from suppliers to distribution centers to end customers. Traditional approaches to MEIO often struggle to cope with the complexity and uncertainty inherent in modern global supply chains. This paper explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques within SAP-enabled supply chains to optimize multi-echelon inventory management. Leveraging SAP’s advanced ERP and supply chain modules alongside AI/ML algorithms facilitates improved demand forecasting, inventory visibility, and dynamic replenishment strategies. The study investigates how AI-driven predictive analytics and reinforcement learning can reduce inventory holding costs, minimize stockouts, and improve service levels across multiple echelons. Using a mixed-method research approach involving case studies, simulation modeling, and data analysis of SAP-integrated supply chains, the paper identifies key benefits and challenges of AI/ML adoption for MEIO. Findings demonstrate that AI-enhanced MEIO leads to significant improvements in inventory turnover and reduced bullwhip effect, fostering supply chain resilience. However, challenges such as data integration complexity, computational resource requirements, and change management remain critical. The paper concludes with recommendations for practitioners and researchers on best practices for implementing AI and ML in SAP environments to optimize inventory across multiple supply chain tiers.
References
1. Graves, S. C., & Willems, S. P. (2008). Optimization of multi-echelon inventory systems: A review. Manufacturing & Service Operations Management, 10(2), 253–277. https://doi.org/10.1287/msom.1070.0160
2. Chandra Shekhar, Pareek (2021). Next-Gen Test Automation in Life Insurance: Self-Healing Frameworks. International Journal of Scientific Research in Engineering and Management 5 (7):1-13
3. Sahaj Gandhi, Behrooz Mansouri, Ricardo Campos, and Adam Jatowt. 2020. Event-related query classification with deep neural networks. In Companion Proceedings of the 29th International Conference on the World Wide Web. 324–330.
4. Sugumar R (2014) A technique to stock market prediction using fuzzy clustering and artificial neural networks. Comput Inform 33:992–1024
5. Devaraju, S., & Boyd, T. (2021). AI-augmented workforce scheduling in cloud-enabled environments. World Journal of Advanced Research and Reviews, 12(3), 674-680.
6. Chen, F., Drezner, Z., Ryan, J. K., & Simchi-Levi, D. (2000). Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information. Management Science, 46(3), 436–443. https://doi.org/10.1287/mnsc.46.3.436.12061
7. Du, T., & Xie, C. (2021). Machine learning for demand forecasting in multi-echelon supply chains. European Journal of Operational Research, 289(3), 1120–1132. https://doi.org/10.1016/j.ejor.2020.07.009
8. Kelle, P., & Silver, E. A. (2004). Forecasting and inventory planning in multi-echelon supply chains: Theory and practice. International Journal of Production Economics, 88(1), 81–94. https://doi.org/10.1016/S0925-5273(03)00127-7
9. S. Devaraju, HR Information Systems Integration Patterns, Independently Published, ISBN: 979-8330637850, DOI: 10.5281/ZENODO.14295926, 2021.
10. Lee, H. L., Padmanabhan, V., & Whang, S. (1997). The bullwhip effect in supply chains. Sloan Management Review, 38(3), 93–102.
11. Wang, G., Gunasekaran, A., Ngai, E. W. T., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98–110. https://doi.org/10.1016/j.ijpe.2016.03.014
12. CHAITANYA RAJA HAJARATH, K., & REDDY VUMMADI, J. . (2023). THE RISE OF INFLATION: STRATEGIC SUPPLY CHAIN COST OPTIMIZATION UNDER ECONOMIC UNCERTAINTY. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 14(2), 1115–1123. https://doi.org/10.61841/turcomat.v14i2.15247
13. Devaraju, S., Katta, S., Donuru, A., & Devulapalli, H. Comparative Analysis of Enterprise HR Information System (HRIS) Platforms: Integration Architecture, Data Governance, and Digital Transformation Effectiveness in Workday, SAP SuccessFactors, Oracle HCM Cloud, and ADP Workforce Now.
14. Yu, Y., Huang, G. Q., & Mak, K. L. (2021). A machine learning approach for multi-echelon inventory optimization with demand uncertainty. Computers & Industrial Engineering, 157, 107314. https://doi.org/10.1016/j.cie.2021.107314
