Dorian Shainin's Variables Search Procedure: A Critical Assessment

Ledolter, Johannes; Swersey, Arthur J.

doi:10.1080/00224065.1997.11979766

Cited by 22 publications

(9 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A comparison between Taguchi and Shainin techniques in an aerospace environment was offered by Thomas and Anthony (2005). A few other authors who have studied these techniques are Traver (1995), Ledolter and Swersey (1997), Does, Roes and Trip (1999), De Mast, et al (2000) and Steiner and MacKay (2005).…”

Section: Shainin Doe Approachmentioning

confidence: 99%

“…This technique can be used to identify factors of variation related to the process parameters and is especially useful in case the sources of variation are related to input material dimensions which are subject to change during processing. Ledolter and Swersey (1997) have given a critical evaluation of variables search as an experimental design method for uncovering important factors. Anthony (1999) has given a brief description of this method, showing with the help of an experiment that it is a powerful tool for identifying key process variables and therefore recommending its utilization by the engineering fraternity in manufacturing organizations as a problem-solving tool.…”

Section: Product/process Search or Variable Searchmentioning

confidence: 99%

See 1 more Smart Citation

Simplifying the Six Sigma Toolbox through Application of Shainin DOE Techniques

Sharma¹,

Chetiya²

2009

Vikalpa

View full text Add to dashboard Cite

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.

show abstract

Section: Shainin Doe Approachmentioning

confidence: 99%

Section: Product/process Search or Variable Searchmentioning

confidence: 99%

Simplifying the Six Sigma Toolbox through Application of Shainin DOE Techniques

Sharma¹,

Chetiya²

2009

Vikalpa

View full text Add to dashboard Cite

show abstract

“…If this condition is not satisfied, then either the list does not yet contain the critical or key process variables or else the settings of one or more variables must be reversed. The results from this phase are used to determine test criteria for later experiments (Ledolter and Swersey, 1997). The replications are used to estimate the experimental error (i.e.…”

Section: R R B R W 2mentioning

confidence: 99%

Training for Shainin’s approach to experimental design using a catapult

Antony

Cheng

2003

Journal of European Industrial Training

View full text Add to dashboard Cite

In the quest for continuous quality improvement of both products and processes, experimental design (ED) based on Shainin’s variables search method is still playing a major role in the manufacturing community. This article presents a simple methodology in a step‐by‐step manner for Shainin’s variables search method, in terms of uncovering the key process variables or factors which influence a response or quality characteristic of interest. In order to illustrate the potential of this powerful methodology as a problem solving tool, a simple catapult experiment was carried out. The authors believe that this simple experiment can be used in the classroom to train engineers and scientists on variables search method in order to identify the most critical variables which influence the process or product performance.

show abstract

“…All these statistical experimental design approaches (classical, Taguchi, and Shainin) have their proponents and opponents, and there is sufficient literature that debates the advantage of one over the other for industrial experimentation (Tanco et al , 2008; Thomas and Antony, 2005; Verma et al , 2004; Ledolter and Swersey, 1997a, b). Nevertheless, instead of approving one experimental design approach over the other, these powerful tools needs be integrated to achieve cost effective experimentation (Tanco et al , 2009; Thompson and Barton, 2006; Schmidt and Lausnby, 2005; De Mast et al , 2000).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, these tools help in saving cost and time substantially. Although the merits and demerits of the different approaches of designing experiments are extensively researched (Tanco et al , 2008; Thomas and Antony, 2005; Verma et al , 2004; Ledolter and Swersey, 1997a, b), there are hardly any studies on disciplined integration of the different DOE tools into a functional framework (Thompson and Barton, 2006).…”

Section: Introductionmentioning

confidence: 99%

Integration of Taguchi and Shainin DOE for Six Sigma improvement: an Indian case

Prashar

2017

IJQRM

View full text Add to dashboard Cite

Purpose The purpose of this paper is to demonstrate the application of Six Sigma/design of experiments (DOE) hybrid framework for improving damping force (DF) generation process in a shock absorber assembly unit. Design/methodology/approach The study adopted a case study research method with single case (holistic) design. This research design was found to be appropriate for testing the projected framework for integrating DOE approaches within Six Sigma define-measure-analyze-improve-control (DMAIC) cycle. In the proposed framework, Shainin’s component search technique (CST) was deployed at the “analysis” phase of DMAIC for the first stage filtering of process parameters, followed by the use of Taguchi orthogonal arrays (OA) at the “improve” phase for identifying the optimal setting of the parameters. Findings The application of Shanin CST facilitated in ascertaining that assembly component (piston with rebound stopper) was causing the variation and not the assembly process. Further, the use of Taguchi OA at the improve phase allowed the collection of necessary data to determine the significant piston parameters with minimum experimentation (eight experimental runs in this case as opposed to the expected 64) and analysis of variance on the collected data facilitated the selection of parameter settings to optimize the “critical to quality”, i.e. rebound DF. Originality/value This study provided a stimulus for wider application of integrated DOE approaches by the engineering community in the problem solving and the identification of parameters responsible for poor performance of the process.

show abstract

Dorian Shainin's Variables Search Procedure: A Critical Assessment

Cited by 22 publications

References 2 publications

Simplifying the Six Sigma Toolbox through Application of Shainin DOE Techniques

Simplifying the Six Sigma Toolbox through Application of Shainin DOE Techniques

Training for Shainin’s approach to experimental design using a catapult

Integration of Taguchi and Shainin DOE for Six Sigma improvement: an Indian case

Contact Info

Product

Resources

About