Data-Driven Optimization of Apparel Supply Chain to Reduce Lead Time and Improve On-Time Delivery

The apparel industry faces constant challenges with lead time variability and delayed deliveries, often impacting customer satisfaction and profit margins. This research presents a data-driven optimization model aimed at reducing lead time and enhancing on-time delivery within the apparel supply chain. By leveraging advanced analytics and machine learning techniques, the study identifies key inefficiencies and develops predictive models to improve the decision-making process in supply chain operations. The proposed solution integrates historical data, demand forecasting, production scheduling, and inventory management to enhance the responsiveness of the supply chain, ultimately leading to a more reliable and efficient system. The results demonstrate significant improvements in both lead time reduction and on-time delivery performance.