Six sigma project selection using data envelopment analysis

Abstract

Purpose

The evolution of six sigma has morphed from a method or set of techniques to a movement focused on business‐process improvement. Business processes are transformed through the successful selection and implementation of competing six sigma projects. However, the efforts to implement a six sigma process improvement initiative alone do not guarantee success. To meet aggressive schedules and tight budget constraints, a successful six sigma project needs to follow the proven define, measure, analyze, improve, and control methodology. Any slip in schedule or cost overrun is likely to offset the potential benefits achieved by implementing six sigma projects. The purpose of this paper is to focus on six sigma projects targeted at improving the overall customer satisfaction called Big Q projects. The aim is to develop a mathematical model to select one or more six sigma projects that result in the maximum benefit to the organization.

Design/methodology/approach

This research provides the identification of important inputs and outputs for six sigma projects that are then analyzed using data envelopment analysis (DEA) to identify projects, which result in maximum benefit. Maximum benefit here provides a Pareto optimal solution based on inputs and outputs directly related to the efficiency of the six sigma projects under study. A sensitivity analysis of efficiency measurement is also carried out to study the impact of variation in projects' inputs and outputs on project performance and to identify the critical inputs and outputs.

Findings

DEA, often used for relative efficiency analysis and productivity analysis, is now successfully constructed for six sigma project selection.

Practical implications

Provides a practical approach to guide the selection of six sigma projects for implementation, especially for companies with limited resources. The sensitivity analysis discussed in the paper helps to understand the uncertainties in project inputs and outputs.

Originality/value

This paper introduces DEA as a tool for six sigma project selection.

Citation

Dinesh Kumar, U., Saranga, H., Ramírez‐Márquez, J.E. and Nowicki, D. (2007), "Six sigma project selection using data envelopment analysis", The TQM Magazine, Vol. 19 No. 5, pp. 419-441. https://doi.org/10.1108/09544780710817856