Optimizing supply chain costs is essential for maintaining a cost-effective yet efficient operation that can adapt to market changes. This research focuses on optimizing apparel supply chain costs by maximizing overall efficiency through the application of Data Envelopment Analysis (DEA), a linear programming (LP) approach. Yarn suppliers in the complex fabric supply chain are categorized into effective and average frontiers using DEA, providing a comprehensive evaluation of their performance. To assess independent efficiencies for each DMU, we formulated goal functions and constraints using MATLAB software allowing for a nuanced understanding of supplier performance within the selected subset. The collected data reveals crucial insights into yarn supply chain dynamics, emphasizing factors such as response time, purchase profit, availability, and purchase quantities. The identification of role models within effective frontiers, depicted as convex curves, establishes benchmarks for suppliers seeking to improve efficiency. Ineffective Decision-Making Units (DMUs) within the curve gain insights into their shortcomings and strategies for improvement. Suppliers can identify role models by determining the shortest distance from effective frontiers, promoting the adoption of best practices. These insights serve as a foundation for strategic decision-making, empowering businesses to optimize supplier relationships and enhance overall supply chain efficiency in a cost-effective manner. Furthermore, this study highlights the importance of advanced computational tools in analyzing complex supply chain networks.