Leveraging Predictive Analytics and AI Techniques to Enhance the Efficiency in Supply Chain Management: A Case Study to Optimize Supply Chain Characteristics

This study explores applying predictive analytics and Machine learning techniques in optimizing supply chain management (SCM) within the retail industry. As retailers face increasing complexities due to fluctuating consumer demand and competitive pressures, integrating advanced data-driven methodologies becomes essential. This research employs machine learning algorithms and statistical modelling to forecast demand patterns, utilizing key features such as shipping, order status, and customer segment analysis to enhance inventory management and streamline logistics operations [1]. By analyzing historical sales data, including sales per customer and order profit per order, and leveraging real time insights, the study aims to improve operational efficiency and decision-making processes. Key performance indicators (KPIs) such as inventory turnover rates and late delivery risk will be evaluated to assess the impact of predictive analytics on SCM performance. Ultimately, this research highlights the transformative potential of leveraging predictive analytics in the retail sector, paving the way for more agile and responsive supply chain strategies that can significantly enhance competitive advantage over adversaries in the market [2].