Leveraging Predictive Analytics in Supply Chain Optimization: A Machine Learning Approach

Authors

Shafeeq Ur Rahaman

Abstract

The Predictive analytics, fueled by the rapid changes in supply chain management, is increasingly becoming a tool to bring improved operational efficiency, especially in the sphere of inventory management and cost optimization. This article examines the use of predictive models for performance enhancement in supply chains related to demand fluctuation, fluctuating inventory levels, and operational bottlenecks through prediction. These algorithms identify trending patterns and make pretty accurate predictions based on historical data combined with real-time input, thus facilitating much better decisions on inventory management to avoid overstocking or stock outs and operational costs. This article also goes on to explore how predictive analytics will ease procurement strategies, optimized delivery schedules, and better supply chain visibility, bringing more responsiveness and cost efficiency into the system. Predictive analytics, therefore, embedded in supply chains not only ensures accuracy but also gives companies a competitive advantage as it pushes efficiencies toward proactive management of probable disruptions.

Keywords

Predictive analytics, machine learning, supply chain optimization, inventory management, cost reduction, demand forecasting, operational efficiency, procurement strategy, and delivery scheduling, ensuring visibility of the supply chain

Leveraging Predictive Analytics in Supply Chain Optimization: A Machine Learning Approach. Shafeeq Ur Rahaman. 2019. IJIRCT, Volume 5, Issue 3. Pages 1-9. https://www.ijirct.org/viewPaper.php?paperId=2412029