Six Sigma Approach To Problem Solving

Smith, in response to a management proclamation that “the real problem at Motorola is that our quality stinks!”, developed and formalized a deterministic approach to identifying and preventing defects in production using a scripted method, statistics and traditional quality tools (Schoen, 2007).After roughly 30 years (1979–2009), Six Sigma-DMAIC, when examined as a structured, scientific–based, problem-solving methodology (and its record of providing tangible, bottom-line benefits) stands up to the test of time.

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During this period, the traditional (or preexisting) quality and continuous improvement initiatives, primarily TQM, were largely scrapped or simply dissolved, as was the case at Allied Signal.

Since going mainstream, legions of Six Sigma consultants and pundits continued to stretch the basic MAIC into DMAIC (Define was added to address initial setbacks), and subsequently into an all encompassing organizational business and change strategy.

Additionally, a structured Six Sigma method for creating organizational strategies and change was also tacked-on (Breyfogle, 2003).

Based on this chain of events between 19, Six Sigma’s role expanded to: the following: In the following pages, we will briefly critique Six Sigma’s performance in each of these areas, and explore the underlying reasons behind the potential successes and setbacks.

Six Sigma and the DMAIC methodology are present in the curriculum of most major academic institutions; the American Society of Quality adopted and began promoting the methodology in the late 1990s.

In general the DMAIC methodology has held its own, although there are lingering criticisms that the full use of methodology and its subsequent complexity, thoroughness and lack of speed, may only be necessary to solve only the smallest percent of complex problems; quality traditionalists stating that 90 percent of problems can be solved using basic quality tools (Kondo, 1994).But when both are woven into the DMAIC methodology, a distinction from previous problem solving techniques (such as TQM, Basic Quality Tools) occurs.After the initial criticism for the looseness with the 1.5 sigma shift of long-term data, the percent defective predictions referenced in the long-term sigma tables and other statistical extrapolations (Wheeler, 2005) subsided, the scientific, deterministic and data/fact driven basis of Six Sigma led to widespread approval in academic and industry circles.In this article, David Joecken, a Six Sigma Master-Blackbelt at a privately-owned tier-one automotive supplier, examines Six Sigma’s historical legacy and projects its future role.From Six Sigma’s initial creation and conception thirty years ago, to its widespread appeal, a potential exaggeration and overextension is postulated.The DMAIC methodology has been generally accepted as a credible and effective approach for Problem Solving since the mid-1990s.Although the deterministic nature of y=f(x) focus and structured approach of hypothesis generation and data-based testing, are basically the core of the scientific method, the DMAIC method is underpinned by an expansive toolbox of statistical methods and very accessible improvement techniques.Once the initial savings began to wane, management’s support may have waned as well, which is detrimental for the success of any long-term improvement plan.Thus, the propensity was that the subsequent savings due to Six Sigma projects in areas outside of quality were lumped together; per Jeroem de Mast: “such conceptual erosion is better avoided…economists have good reason to distinguish between product quality, process efficiency, cost and capital and not labeling it all quality” (de Mast, 2007).Because these types of potential quality cost benefits should be initially very real, management would have been very eager to adopt the approach and to include the summary savings into its regular financial reviews.Thus, the real driver of adoption of Six Sigma in many examples was this type of business case.


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