Using Statistical Thinking and Designed Experiments to Understand Process Operation

Leitnaker, Mary G.; Cooper, A.H.

doi:10.1081/qen-200056477

Cited by 9 publications

(6 citation statements)

References 8 publications

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“…However, caution is warranted when drawing conclusions from only one batch as there may be batch to batch variation present. 107 Likewise, accepting the first evidence that supports a root cause could lead to a mistake, especially when contradictory evidence exists. 65 Observations may consist of physical evidence such as "parts, residues, and chemical samples" 108 (p. 104).…”

Section: (P 705)mentioning

confidence: 99%

“…Evidence pointing towards a root cause includes observations such as a change in a specific week or a difference between two products 40 such as when measurement values suddenly increase or two products should be experiencing a failure, but only one is. However, caution is warranted when drawing conclusions from only one batch as there may be batch to batch variation present 107 . Likewise, accepting the first evidence that supports a root cause could lead to a mistake, especially when contradictory evidence exists 65…”

Section: Literature Reviewmentioning

confidence: 99%

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Criteria for the prioritization of hypotheses in root cause analysis

Barsalou

2022

Quality & Reliability Eng

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Explanatory hypotheses are formed and evaluated in root cause analysis. However, prior to investigation, the hypotheses must be prioritized. Often, methods such as nominal group technique, multi‐voting, and simple voting are used to decide which to investigate first. This research seeks to provide concrete criteria for the prioritization of hypotheses using three levels of prioritization based on the strength of the available evidence. A quality leadership email distribution list was used to distribute a survey to quality departments. Respondents were asked to rate various scenarios as confirmed, strong, moderate, and weak evidence towards supporting a hypothesis. Only 2 of 13 scenarios did not have statistically significant results and these results can be used by quality departments to prioritize hypotheses to investigate during root cause analysis.

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Section: (P 705)mentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Criteria for the prioritization of hypotheses in root cause analysis

Barsalou

2022

Quality & Reliability Eng

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“…The review noted several Six Sigma case illustrations employing full and factorial experimental design for process optimization (Vinodh et al , 2014; Gijo et al , 2014; Zhang et al , 2015; Gijo and Scaria, 2010; Chen and Lyu, 2009; Lee et al , 2009; Johnson et al , 2006a, b, 2012; Leitnaker and Cooper, 2005; Gijo and Rao, 2005). Many of these industrial experimentation studies used Taguchi OA and signal to noise (S/N) ratio for reducing variation in the processes (Vinodh et al , 2014; Saravanan et al , 2012; Gijo and Scaria, 2010; Singh et al , 2007; Gijo, 2005).…”

Section: Literature Reviewmentioning

confidence: 99%

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

Prashar

2017

IJQRM

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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.

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“…There are plenty of researches on process control and the optimization [6] [7], but few researches are focus on systematic study of PVI using data driven method for process optimization. Inspired by the biological evolution mechanism, this paper attempt to realize the process parameter adaptable optimization through analysis of process variation.…”

Section: Introductionmentioning

confidence: 99%

Utilization of Process Variation for System Evolution

Zhang

Zeng

Huang

et al. 2009

2009 International Workshop on Intelligent Systems and Applications

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The basic method of statistic process control is to eliminate variation and put process under statistic control. The negative effect of the method is that it leads to the neglect of the valuable information contained in process variation, thus missing the opportunity for process optimization. But variation is not necessarily harmful, from the biological evolution point of view, variation is the driving force of evolution. Inspired by biological evolution mechanism, a method of adaptive optimization based on process variation is proposed. A data mining technologyassociation rules mining technology is adopted to analyze the SPC data and excavate association rules in the quality parameters. Then the association rules are evaluated and utilized to improve production processes. The method expand the stability-oriented quality control system to stability-optimizationcombined system and continual quality management improvement is realized by iteration of this data driven process. A case study of injection molding process optimization is provided to illustrate application of the presented method.

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Using Statistical Thinking and Designed Experiments to Understand Process Operation

Cited by 9 publications

References 8 publications

Criteria for the prioritization of hypotheses in root cause analysis

Criteria for the prioritization of hypotheses in root cause analysis

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

Utilization of Process Variation for System Evolution

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