Optimizing Production Efficiency in the Garment Industry: The Role of Predictive Analytics Techniques in Sri Lanka’s Textile Sector

This research explores the significant role of predictive analytics in optimizing production efficiency within Sri Lanka’s garment industry, a key contributor to the nation’s economy. The study synthesizes current knowledge on predictive analytics, focusing on its application in improving operational decision-making and addressing challenges related to data requirements and preprocessing in garment manufacturing. Through a systematic literature review, 30 relevant studies were identified and analyzed, highlighting key factors influencing production efficiency, such as labor management, Standard Minute Value (SMV), work-in-progress (WIP), and overtime. The research underscores the potential of advanced predictive techniques like ensemble models to drive operational improvements. However, it also identifies significant challenges, particularly the reliance on short-term datasets, which limit the ability to capture seasonal patterns crucial for accurate production forecasting. The study advocates for the development of multimodel frameworks and the integration of time series analysis to enhance predictive accuracy and decision-making in the garment industry. By adopting these advanced methodologies, the industry can better navigate the complexities of production processes, ultimately improving efficiency and competitiveness in the global market.