Simulation-based Six Sigma value function for system-level performance assessment and improvement

Abstract

Purpose

The purpose of this paper is to develop a simulation-based value function (VF) that combines multiple key performance indicators (KPIs) into a unified Sigma rating (SR) for system-level performance assessment and improvement.

Design/methodology/approach

Simulation is used as a platform for assessing the multiple KPIs at the system level. A simple additive VF is formed to combine the KPIs into a unified SR using the analytical hierarchy process and the entropy method. Value mapping is utilized to resolve the conflict among KPIs and generate a unified value. These methods are integrated into the standard Six Sigma define-measure-analyze-improve-control (DMAIC) process.

Findings

Simulation results provided the Six Sigma DMAIC process with system-level performance measurement and analysis based on multiple KPIs. The developed VF successfully generated unified SRs that were used to assess various performance improvement plans.

Research limitations/implications

The accuracy and credibility of the results obtained from using the proposed VF are highly dependent on the availability of pertinent data and the accuracy of the developed simulation model.

Practical implications

The proposed approach provides Six Sigma practitioners and performance mangers with a mechanism to assess and improve the performance of production and service system based on multiple KPIs when conducting Six Sigma studies.

Originality/value

This paper contributes to the previous research by handling multiple KPIs in Six Sigma studies conducted at the system level using simulation and VF. The research also provides guidelines for using the different methods of weights assessment to form the VF within the DMAIC process.

Citation

Al-Aomar, R. and Chaudhry, S. (2018), "Simulation-based Six Sigma value function for system-level performance assessment and improvement", International Journal of Productivity and Performance Management, Vol. 67 No. 1, pp. 66-84. https://doi.org/10.1108/IJPPM-01-2016-0007