AI-DRIVEN PREDICTIVE ANALYTICS FOR SUPPLIER LEAD TIME AND PERFORMANCE FORECASTING
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Abstract
The effective management of supplier lead times and performance is a cornerstone of supply chain efficiency. Traditional methods for forecasting supplier performance often rely on historical data and simplistic models, which fail to account for the complex, dynamic nature of modern supply chains. Artificial Intelligence (AI) and predictive analytics have the potential to significantly improve the accuracy and reliability of supplier lead time predictions and performance forecasting by leveraging real-time data, advanced machine learning algorithms, and statistical models. This research article explores the role of AI-driven predictive analytics in optimizing supplier lead time and performance forecasting. It covers the key AI techniques used, the benefits of adopting these technologies in supply chain management, and the challenges associated with their implementation. Through case studies and practical applications, this article highlights how AI and predictive analytics can enable more proactive, data-driven decision-making in supplier management and contribute to enhanced supply chain resilience.
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References
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