Smart Inventory Optimization using Machine Learning Algorithms

Industries increasingly rely on efficient inventory management, logistics, and supply chain operations, and ML-based intelligence frameworks have emerged as indispensable tools. In this study, ML algorithms are tailored for intelligent inventory management. This research centers around the application of ML techniques within the framework of the ABC Inventory Classification methodology. To assess the effectiveness of these algorithms, a comprehensive training and testing was conducted, followed by model classification. The five ML algorithms examined in this study include Decision Tree Algorithm, Support Vector Machines (SVM), Random Forest Classifier, Naïve Bayes Classifier and K-nearest neighbors (KNN). The goal is to shed light on how these ML algorithms perform in comparison to one another when used for inventory management. Through analysis using inventory dataset, the inference made was that Random Forest Classifiers provides the highest accuracy at 93%. Improving the effectiveness, precision, and financial performance of inventory management practices across various industries and supply chain segments by leveraging the power of ML is an important step of this research. This research study makes significant progress towards integrating intelligent ML-driven solutions into the intricate world of supply chain operations.