AI-Powered Predictive Analytics for Supply Chain Optimization: A Risk-Resilient Framework
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V2I1P102Keywords:
Artificial Intelligence, Predictive Analytics, Supply Chain Optimization, Risk Management, Supply Chain Resilience, Machine Learning, Forecasting, Deep LearningAbstract
Modern supply chains are complex, dynamic, and vulnerable to disruptions. The increasing complexity of global networks, coupled with unforeseen events like pandemics and geopolitical instability, necessitates a paradigm shift from reactive to proactive risk management. This paper presents a risk-resilient framework leveraging AI-powered predictive analytics to optimize supply chain performance, mitigate risks, and enhance resilience. We explore the application of various AI techniques, including machine learning, deep learning, and natural language processing, to forecast demand, identify potential vulnerabilities, and optimize resource allocation. The framework integrates data from diverse sources, enabling real-time monitoring and adaptive decision-making. Through case studies and simulations, we demonstrate the efficacy of the proposed framework in improving forecast accuracy, reducing lead times, minimizing costs, and enhancing the overall resilience of supply chains against various disruptions. The paper also addresses the ethical considerations and challenges associated with implementing AI in supply chain management
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References
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