Artificial Intelligence and Machine Learning as an Antifragile Driver in the Supply Chain

This paper explores the critical role of Artificial Intelligence (AI) and Machine Learning (ML) in driving antifragility within the supply chain domain. With the increasing complexity, volatility, and uncertainty in the global business environment, organizations are seeking resilient and adaptive supply chain solutions. AI and ML technologies have demonstrated immense potential in enhancing supply chain operations by enabling real-time analysis, predictive capabilities, and process automation. This paper evaluates the inherent characteristics of AI and ML in fostering antifragility within the supply chain, highlighting their contributions in areas such as demand forecasting, inventory management, logistics optimization, and risk mitigation. Furthermore, challenges and ethical implications related to the adoption of AI and ML in the supply chain are also discussed, along with recommendations and future directions for leveraging these technologies to build robust and agile supply chains.This paper explores the critical role of Artificial Intelligence (AI) and Machine Learning (ML) in driving antifragility within the supply chain domain. With the increasing complexity, volatility, and uncertainty in the global business environment, organizations are seeking resilient and adaptive supply chain solutions. AI and ML technologies have demonstrated immense potential in enhancing supply chain operations by enabling real-time analysis, predictive capabilities, and process automation. This paper evaluates the inherent characteristics of AI and ML in fostering antifragility within the supply chain, highlighting their contributions in areas such as demand forecasting, inventory management, logistics optimization, and risk mitigation. Furthermore, challenges and ethical implications related to the adoption of AI and ML in the supply chain are also discussed, along with recommendations and future directions for leveraging these technologies to build robust and agile supply chains.

Keywords:

Artificial Intelligence , Machine Learning , Supply Chain , Antifragility , Demand Forecasting , Inventory Management , Logistics Optimization , Risk Mitigation