Inventory Management Automation in SAP using Machine Learning Algorithm

Authors

Ravi Kumar Perumallapalli

Abstract

In today's dynamic business environment, effective inventory management is critical for organizations to maintain operational efficiency and meet customer demands. Traditional methods of inventory management, while reliable, often lack the agility needed to process large volumes of data and adapt to real-time market fluctuations. This paper explores an automated inventory management solution integrated into SAP systems, leveraging machine learning algorithms to enhance decision-making, accuracy, and efficiency. The proposed model utilizes predictive analytics to forecast demand patterns, optimize stock levels, and reduce operational costs. Key features include automated stock replenishment, anomaly detection, and adaptive reordering mechanisms, all aimed at minimizing stockouts and overstock scenarios. Our approach employs time-series forecasting and classification models, supported by SAP's embedded analytics, to streamline inventory processes and deliver ac-tionable insights. Case studies demonstrate significant improvements in inventory turnover and re-duced holding costs, confirming the value of machine learning in advancing inventory management practices within SAP ecosystems. This research underscores the potential of machine learning-driven automation to transform traditional inventory workflows, offering a scalable solution adaptable across industries with varying supply chain complexities.

Keywords

Inventory Management Automation, SAP Integration, Machine Learning in Inventory, Predictive Analytics, Stock Optimization

Inventory Management Automation in SAP using Machine Learning Algorithm. Ravi Kumar Perumallapalli. 2015. IJIRCT, Volume 1, Issue 1. Pages 1-8. https://www.ijirct.org/viewPaper.php?paperId=2411011