LEVERAGING ARTIFICIAL INTELLIGENCE FOR NEXT-GENERATION SUPPLY CHAIN OPTIMIZATION: A STRATEGIC IMPLEMENTATION FRAMEWORK - Arvindan Badrinarayanan (INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND INFORMATION TECHNOLOGY (IJRCAIT), 2024)
Integrating artificial intelligence (AI) in supply chain optimization represents a transformative paradigm shift in modern business operations, fundamentally reshaping how organizations manage their supply chain networks. This comprehensive article synthesizes findings from extensive studies across manufacturing and logistics sectors, demonstrating that organizations implementing structured AI adoption frameworks achieve significantly higher returns on investment than traditional approaches. Through analysis of global enterprises over recent years, this article reveals that companies following systematic implementation methodologies experience substantial improvements in operational efficiency and decision-making accuracy. This article presents a detailed framework encompassing five critical dimensions: maturity assessment, data strategy development, pilot project implementation, enterprise-wide integration, and future considerations. Organizations implementing comprehensive data strategies demonstrate marked improvements in prediction accuracy and considerable reduction in process inefficiencies, while those adopting phased pilot implementations achieve notably faster full-scale deployment rates. This article further indicates that enterprises establishing robust governance frameworks experience fewer integration challenges and better stakeholder alignment. Integrating IoT technologies with AI frameworks significantly improves supply chain visibility and enhances real-time decision-making capabilities. This article provides actionable insights for organizations embarking on AI transformation initiatives, emphasizing the importance of a balanced approach between technological innovation and practical implementation considerations while focusing on sustainable and ethical practices. The analysis contributes to theoretical understanding and practical implementation of AI in supply chain management, offering a structured pathway from initial assessment through enterprise-wide deployment and continuous improvement.