Leveraging Machine Learning Techniques to Forecast and Enhance Supplier Reliability in Supply Chain Management

Abstract

In today's competitive market, selecting reliable suppliers is crucial to ensure supply chain efficiency. In fact, anticipating supplier behavior plays a vital role in effectively managing the risk of disruptions, enabling companies to develop proactive strategies to mitigate potential supply chain interruptions. The complexity of supplier management has driven companies to adopt artificial intelligence (AI) and machine learning, to enhance decision-making in the upstream supply chain. By analyzing historical data, machine learning models help predict risks and improve supplier reliability. These predictive capabilities allow businesses to identify vulnerabilities early, ensuring better risk preparedness and supply chain resilience. This article examines the application of AI to tackle supplier selection challenges, emphasizing its role in transforming supply chains into agile, data-driven, and predictive systems while addressing the critical need to manage disruption risks effectively. Key words: Artificial intelligence, Machine learning, Supplier reliability, Logistic Regression.