Statistical and causal analysis of inbound supply chain inefficiencies

Abstract

Given the importance of operational inefficiencies and their negative impact on the bottom line in today's competitive economy, CVS/pharmacy is very interested in implementing operational improvement initiatives across its inbound supply chain to minimize the number of non-value-added activities. Undertaking such efforts requires collaboration amongst all trade partners and a systematic approach in measuring the important performance metrics. Currently there is not a single procedure that defines the necessary metrics and the analytical tools necessary for identifying improvement opportunities. Leveraging research from the manufacturing industry, specifically supplier certification and statistical process control, this thesis aims to develop a comprehensive methodology for analyzing, monitoring and improving the operational performance of the retail industry supply chain. In this thesis, through an innovative approach to perfect order performance measurement combined with the practical application of statistical analysis methods, a complete supplier evaluation process is established. Further, by utilizing statistical sampling and based on the evaluation results, an inspection plan is provided that allows for accurate monitoring of ongoing processes with a reduction in inspection efforts. Finally through introduction of statistical process control models and root cause analysis, a complete procedure is developed for continuous evaluation and improvement, leading to efficiency gains and cost savings across the entire inbound supply chain.