Supply Chain Management Using Optimization and Machine Learning Techniques

This study focuses on developing a predictive model for late deliveries in the supply chain, using the Data Co Supply Chain dataset. It involves data cleaning, visualization, and training various machine learning algorithms, selecting the most effective model based on accuracy and recall values. The research explores challenges like transportation delays, production issues, natural disasters, and supplier disruptions. Businesses are adopting proactive supply chain and risk management strategies to enhance resilience. The paper discusses how machine learning techniques can optimize supply chain management, offering insights into mitigating risks associated with late deliveries. By identifying challenges in advance, businesses can improve operational efficiency, minimize financial setbacks, and maintain customer trust. In a dynamic business environment, this research provides valuable solutions for supply chain professionals, integrating big data and machine learning to forecast and address late deliveries. It not only aids in predicting delays but also contributes to proactive decision-making, improving overall supply chain performance amidst uncertainties.