Abstract
The success of a Six Sigma programme in an organization depends to a large extent on the success of the Six Sigma projects, which in turn depends on how the team handles the problem and whether the right combination of tools is being applied to address the root cause. The Six Sigma toolbox consists of a wide range of tools comprising, on the one hand, simple and commonly used tools like flow charts, Pareto analysis, and cause-and-effect diagram and the more advanced statistical tools like design of experiments, regression analysis and many more, on the other hand. While the simple tools are easy to apply, understand, and analyse, engineers perceive the more advanced tools difficult to comprehend. Design of experiments (DOE) is one such tool. Two well-known approaches of design of experiments are the Classical DOE, pioneered by Sir Ronald A Fisher and the Taguchi approach, pioneered by Dr Genichii Taguchi.
A third approach to experimental design—the Shainin DOE techniques, offered by Dr Dorian Shainin—can be considered as a very good alternative to the other approaches. They are much simpler than the factorial designs, response surface designs, and orthogonal arrays of the conventional approaches of DOE, but at the same time are recognized as being very powerful and effective in solving the chronic quality problems that plague most manufacturers. Shainin DOE basically works at eliminating suspected process variables by mostly using seven different tools, viz.,
Multi-Vari Charts Component Search Paired Comparison Variable Search Full Factorials B vs. C (Better vs. Current) Analysis Scatter Plots or Realistic Tolerance Parallelogram Plots.
Though not very well documented, these tools have proved to be the key drivers in the success of many companies, e.g., Motorola.
This article examines two projects of a leading automotive and general lighting lamp manufacturing company, in which a combination of the standard Six Sigma tools and Shainin tools has been successfully used to address the root cause of the problems. The advantage of using Shainin tools is that:
Very small sample sizes are required to analyse the problem. Often samples as small as 2 or 3 are enough to make statistically valid conclusions. Statistical software is not required to analyse the data. In fact, Shainin DOE does not even require knowledge of complex statistical tools. It involves employees at all levels, including workers and junior staff in problem solving that was hitherto a domain of senior technical experts. Also, the success of the projects had a very positive effect on the morale of the employees in terms of convincing them that Six Sigma is not all about using complex statistical tools.