OPTIMIZING SUPPLY CHAIN PERFORMANCE USING MANAGEMENT SCIENCE MODELS

Abstract

Efficient supply chain management has become increasingly important as organizations operate within complex and highly competitive global markets. Inefficiencies in forecasting, inventory control, supplier reliability, and cost management can significantly reduce operational performance and increase overall logistics costs. This study examines how management science models can be applied to optimize supply chain performance by analyzing the interaction between forecasting accuracy, supplier performance, operational costs, and service level outcomes. A quantitative research design was employed using a structured supply chain dataset containing operational variables such as forecast demand, actual demand, inventory levels, supplier lead time, transportation cost, procurement cost, and service performance indicators. Descriptive statistical analysis, efficiency frontier evaluation, cost structure decomposition, and supplier performance assessment were used to examine supply chain operational dynamics. The results indicate that forecasting accuracy plays a crucial role in reducing supply chain costs and improving service reliability. Furthermore, supplier delivery performance and lead time stability significantly influence inventory efficiency and overall operational effectiveness. The findings highlight that integrated analytical models can provide valuable insights for improving decision-making within supply chain systems. The study contributes to the literature by demonstrating how management science approaches can support comprehensive supply chain optimization strategies.