Abstract

Purpose – The purpose of this paper is to provide Lean Six Sigma deployment champions with a structured approach to identify and prioritize parts of their business that are conducive to the Lean Six Sigma methodology. Design/methodology/approach – A five-step approach to Lean Six Sigma project identification is presented in this paper. The approach utilizes a clustering technique to group similar processes based on seven process characteristics. The clusters formed are then evaluated and prioritized for their compatibility to Lean Six Sigma. Findings – The clustering approach can be applied to any industry segment, including non-manufacturing, healthcare and financial-based organizations. A case study is presented in this paper in which the approach is applied to an IT based company. A total of 30 percent of the business processes were found to be Lean Six Sigma conducive. Research limitations/implications – The current model does not have provision to consider the current performance of a process as an evaluation criterion. This requires the deployment champion to use the model in conjunction with a Balanced Scorecard. Future research will address this limitation. Originality/value – There is a general lack of a mathematical approach to enable Lean Six Sigma practitioners to identify parts of their business that are conducive to the methodology. This research attempts to bridge this gap in the literature by using an unsupervised learning approach, using a clustering algorithm, to group processes based on seven process characteristics. The cluster evaluation helps the deployment champion identify key areas within the business to focus an LSS deployment.

Original languageEnglish (US)
Pages (from-to)187-205
Number of pages19
JournalInternational Journal of Lean Six Sigma
Volume3
Issue number3
DOIs
StatePublished - Jan 1 2012

Keywords

  • Clustering
  • Deployment strategy
  • Lean Six Sigma
  • Lean production
  • Project identification
  • Six sigma

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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