On the optimal selection of process alternatives in a Six Sigma implementation

U. Dinesh Kumar, David Nowicki, José Emmanuel Ramírez-Márquez, Dinesh Verma

Research output: Contribution to journalArticlepeer-review

108 Scopus citations

Abstract

Six Sigma is at the top of the agenda for many companies that try to reduce cost and improve productivity. Many of the top manufacturing companies implement thousands of Six Sigma projects every year and this implementation demands a significant investment of capital that requires a careful analysis to make sure that the benefits obtained are much higher than the actual investment. This cost benefit analysis is crucial, especially for companies whose products have a small profit margin. In this paper, two optimization models that will assist management to choose process improvement opportunities are presented. These models consider a multi-stage, asynchronous manufacturing process with the opportunity to improve quality (scrap and rework rates) at each of the stages. The first model is to maximizing the sigma quality level of a process under cost constraint while the selection of Six Sigma alternatives to maximize process returns is considered by the second model. Process quality improvement usually results in costs associated with the purchase of new technology, modification of existing equipment, training employees, hiring new employees and investment in information technology infrastructure. The proposed models recognize that a company competes for funds and that benefits can result in either improved revenue or reduction in costs. An example illustrates the application of the optimization models developed and results show that in some scenarios implementing Six Sigma may not be financially beneficial.

Original languageEnglish
Pages (from-to)456-467
Number of pages12
JournalInternational Journal of Production Economics
Volume111
Issue number2
DOIs
StatePublished - Feb 2008

Keywords

  • Mathematical programming
  • Optimal allocation
  • Process improvement
  • Six Sigma

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