DOI QR코드

DOI QR Code

The Characteristics and Implementations of Quality Metrics for Analyzing Innovation Effects in Six Sigma Projects

식스시그마 프로젝트 사례에서 혁신효과 분석을 위한 품질척도의 특성 및 적용

  • Choi, Sungwoon (Department of Industrial Engineering, Gachon University)
  • 최성운 (가천대학교 산업공학과)
  • Received : 2014.01.20
  • Accepted : 2014.03.19
  • Published : 2014.03.31

Abstract

This research discusses the characteristics and the implementation strategies for two types of quality metrics to analyze innovation effects in six sigma projects: fixed specification type and moving specification type. $Z_{st}$, $P_{pk}$ are quality metrics of fixed specification type that are influenced by predetermined specification. In contrast, the quality metrics of moving specification type such as Strictly Standardized Mean Difference(SSMD), Z-Score, F-Statistic and t-Statistic are independent from predetermined specification. $Z_{st}$ sigma level obtains defective rates of Parts Per Million(PPM) and Defects Per Million Opportunities(DPMO). However, the defective rates between different industrial sectors are incomparable due to their own technological inherence. In order to explore relative method to compare defective rates between different industrial sectors, the ratio of specification and natural tolerance called, $P_{pk}$, is used. The drawback of this $P_{pk}$ metric is that it is highly dependent on the specification. The metrics of F-Statistic and t-Statistic identify innovation effect by comparing before-and-after of accuracy and precision. These statistics are not affected by specification, but affected by type of statistical distribution models and sample size. Hence, statistical significance determined by above two statistics cannot give a same conclusion as practical significance. In conclusion, SSMD and Z-Score are the best quality metrics that are uninfluenced by fixed specification, theoretical distribution model and arbitrary sample size. Those metrics also identify the innovation effects for before-and-after of accuracy and precision. It is beneficial to use SSMD and Z-Score methods along with popular methods of $Z_{st}$ sigma level and $P_{pk}$ that are commonly employed in six sigma projects. The case studies from national six sigma contest from 2011 to 2012 are proposed and analyzed to provide the guidelines for the usage of quality metrics for quality practitioners.

Keywords

References

  1. Antony J.(2003), Design of Experiments for Engineers and Scientists, Elsevier.
  2. Birmingham A. et al.(2009), "Statistical Methods for Analysis of High-Throughput RNA Interference Screens", Nature Methods, 6, 569-575. https://doi.org/10.1038/nmeth.1351
  3. Breyfogle F.W.(2003), Implementing Six Sigma: Smarter Solutions Using Statistical Methods, John Wiley & Sons.
  4. Brook Q.(2010), Lean Six Sigma and MINITAB, 3 Edition, OPEX Resources.
  5. Choi s.(2013), "Implementation of Z-Factor Statistics for Performance Evaluation of Quality Innovation in the High Throughout Process", Journal of Korea Safety Management and Science, 15(1), 293-301. https://doi.org/10.12812/ksms.2013.15.1.293
  6. Iversen P.W. et al.(2006), "A Comparison of Assay Performance Measures in Screening Assays: Signal Window, Z Factor, and Assay Variability Ratio", Journal of Biomolecular Screening, 11, 247-252. https://doi.org/10.1177/1087057105285610
  7. Koch K.R.(2010), Parameter Estimation and Hypothesis Testing in Linear Models, Springer.
  8. Lehmann E.L., Romano J.P.(2010), Testing Statistical Hypothesis, Springer.
  9. McCarty T.Jordan M., Probst D.(2011), Six Sigma for Sustainability, McGraw-Hill Professional.
  10. Quesenberry C.P.(1997), SPC Methods for Quality Improvement, John Wiley & Sons.
  11. Rauwendaal C.(2008), SPC: Statistical Process Control in Injection Modeling and Extrusion, 2 Edition, Hanser Publications.
  12. Wilcox R.R.(2012), Introduction to Robust Estimation and Hypothesis Testing, 3 Edition, Academic Press.
  13. Zhang J.H., Chung T.D.Y, Oldenburg K.R.(1999) "A Simple Statistical Parameter for Use in Evaluation and Validation of high Throughput Screening Assays", Journal of Biomolecular Screening, 4, 67-73. https://doi.org/10.1177/108705719900400206
  14. Zhang X.H.D(2007), "A Pair of New Statistical Parameters for Quality Control in RNA Interference High-Throughput Screening Assays", Genomics, 89, 552-561. https://doi.org/10.1016/j.ygeno.2006.12.014
  15. Zhang X.H.D.(2011), Optimal High -Throughput Screening: Practical Experimental Design and Data Analysis for Genome-Scale RNA Research, Cambridge University Press.
  16. www.q-korea.net

Cited by

  1. Review and Application of Creative Problem-Solving Processes for Technical and Physical Contradictions Using Cause-And-Effect Contradiction Tree and Integrated Principles of TRIZ vol.17, pp.2, 2015, https://doi.org/10.12812/ksms.2015.17.2.215