An Integrated Approach of Ant Colony Optimization (ACO), Machine Learning (ML), and Fuzzy Logic for Revolutionizing Inventory Management in Modern Supply Chains

Abstract

This article presents a groundbreaking approach aimed at addressing the limitations of conventional inventory management practices in contemporary supply chains. The principal objective is to revolutionize inventory management by harnessing the synergistic potential of Ant Colony Optimization (ACO), Machine Learning (ML), and Fuzzy Logic. This integrated framework seeks to elevate demand forecasting, optimize ordering strategies, and enhance inventory control processes. Methods: The methodology encompasses the amalgamation of three potent techniques: ACO for the optimization of reorder points and quantities, ML for precise demand forecasting through the analysis of historical data and external variables, and Fuzzy Logic for managing imprecise and linguistic factors to facilitate adaptable decision-making. This fusion minimizes overall inventory costs while refining inventory-related choices. Findings: The fusion of ACO, ML, and Fuzzy Logic represents a pragmatic solution for contemporary inventory management. Businesses that embrace this approach can attain adaptability, data-driven precision, and flexibility, resulting in improved demand forecasting, optimized ordering strategies, and more efficient inventory management processes. An illustrative real-world case demonstrates that this integrated approach leads to cost-effective and responsive solutions, with the potential to revolutionize inventory management, translating into cost savings, heightened customer satisfaction, and enhanced operational efficiency. Novelty: The novelty of this integrated approach lies in its distinctive amalgamation of ACO, ML, and Fuzzy Logic within the inventory management context. While these techniques are well-established in their own right, their integration signifies an innovative response to an enduring challenge. This approach enables adaptability to shifting conditions, precise demand forecasting, and flexible decision-making, which were arduous to achieve using traditional methodologies.