The apparel manufacturing industry operates within a highly dynamic environment defined by fast-changing consumer preferences, short product lifecycles, and globally distributed supply chains. These factors create substantial challenges in decision-making, often requiring responses within hours rather than days. Traditional methods relying on historical data and periodic reporting fail to deliver the speed and precision needed to remain competitive. This study explores the role of Supply Chain Analytics (SCA) in enabling real-time decision-making in apparel manufacturing by integrating predictive modeling, machine learning, and real-time data streams. We propose a data-driven framework that leverages real-time analytics across four critical functions: sourcing, production planning, inventory optimization, and distribution. The model is validated using a case study simulation of a mid-sized apparel manufacturer, where key performance indicators such as forecast accuracy, inventory turnover, and order fulfillment show significant improvement post-implementation. The findings highlight that SCA empowers apparel firms with end-to-end visibility, enabling proactive and agile operations. Real-time dashboards and data integration tools allow stakeholders to make informed decisions in response to disruptions, demand variability, or supplier delays. While challenges such as data silos, technological investment, and employee upskilling exist, the long-term strategic benefits outweigh the initial barriers. This paper concludes that adopting real-time supply chain analytics is essential for building resilient, efficient, and customer-centric apparel manufacturing systems.
Md Ashraful Azad (Thu,) studied this question.