On optimal estimation techniques for supply chain management with stratified sampling - Verma, Mukesh Kumar; Varshney, Rahul; Yadav, Subhash Kumar; Gangele, Raj K. (Brazilian Journal of Biometrics, 2026)
In this study, improving the estimation of the population mean is critical in supply chain management for optimizing resource allocation, determining the optimum sample size, and enhancing operational efficiency. This study proposes an improved method for estimating the population mean within a stratified random sampling framework by incorporating an auxiliary variable to increase the precision of delivery time estimates and minimize costs incurred. Population data are generated under a bivariate normal distribution across four stratified regions, using shipment volume as an auxiliary variable correlated with delivery time. A first-order error approximation is employed to derive an efficient estimator, which reduces bias and improves accuracy. A simulation study with a proportionally allocated stratified sample is conducted to evaluate the performance of the proposed estimator. The results demonstrate reduced variation and increased estimation efficiency, and cost-effectiveness compared to traditional mean estimators. This approach provides robust statistical insights for logistics companies, enabling data-driven decision-making and enhanced supply chain performance.