Simulated Six Sigma: A multi response optimization

Abstract

Six Sigma is widely used in many industries for the purpose of improving process performance. Typical improvements sought in Six Sigma projects include decreased operational cost, increased customer satisfaction, decreased cycle time, and enhanced profits, but regardless of the nature of the improvement, all projects follow the “Define-Measure-Analyze-Improve-Control” (DMAIC) methodology.This paper proposes the use of simulation and multi response optimization in addition to other typical Six Sigma tools in order to arrive at optimum performance at the end of each project through an established optimization framework. The framework is used in a real case study performed at a global logistics company.The case study is concerned with an e-commerce logistics station located at John F. Kennedy airport (JFK) in New York; a station that suffers from low on-time delivery performance and high operational cost. As result, a Six Sigma project was implemented and the process was simulated to reveal various scenarios suggested by the Six Sigma team in order to optimize the process.The project resulted in 65 percent reduction in labor, 41 percent reduction in work in process (WIP) and 55 percent reduction in average time packages spend at the station. These improvements highly support the effectiveness of implementing Six Sigma projects in the format suggested.