Machine Learning in Sap for Inventory Optimization

Abstract

Purpose: The reason of the study is to bring machine learning algorithms into the environment of inventory management in SAP systems. It deals in minimizing stock holding costs, avoiding stock outs and improving customer satisfaction by working on inventory practice. Methodology: Predictive analytics is then applied to forecast demand and make proactive inventory recommendations for the methodology. Other strategies are also explored around dynamic pricing, where inventory is turned leveraging real time data. Real time stock level assessments are proposed to be implemented with IoT sensors to avoid restocking before products are depleted. Findings: The results show that there are opportunities to continuously improve inventory management based on modifications of machine learning algorithms in response to changes in the market environment. Sensors integrated with predictive analytics can help eliminate those stockouts, optimize stock levels, all resulting in enhanced operational efficiency. Unique Contribution to Theory, Practice and Policy: Machine learning algorithms, dynamic pricing strategies, and IoT sensors are all to be integrated into SAP systems gives the study. The purpose of these technologies is suggested to improve the inventory management efficacy, lower costs and achieve the optimum stock levels to satisfy demand fluctuations. These systems should be continuously refined to keep pace with changing market conditions.