Knowledge Management Framework for Six Sigma Performance Level Assessment

  • Jevgeni Sahno
  • Eduard Sevtsenko
  • Tatjana Karaulova
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 206)


With the rapid growth of competition in the market, the companies have to guarantee customers a reliable, sustainable and quality proofing production system. In this paper we consider a KM framework that extracts “Six Sigma” knowledge on the basis of the data gathered from production facilities. The KM framework enables to assess the performance of a company’s production and quality system by sigma value. The result will help the company to select a new development strategy in order to increase the profitability and customer satisfaction. KM framework includes well known tools like PDM, ERP system, PDM-ERP middleware and DM. The core of our framework is the DM that combines production route card data, Faults Classification standard DOE-NE-STD-1004-92 and the data from FMEA table. The combination and application of different tools and methods in the general KM framework allows the data flow between different systems, analysis of production operation and the failures occurring in the production process.


Knowledge Management (KM) Product Data Management (PDM) Enterprise Resource Management (ERP) PDM-ERP middleware Data Mart (DM) Production Route (PR) Faults Classification Failure Mode and Effect Analysis (FMEA) Six Sigma Sigma performance level 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Koch, R.: Living the 80/20 Way: Work Less, Worry Less, Succeed More, Enjoy More illustrated edition. Nicholas Brealey Publishing (2004)Google Scholar
  2. 2.
    Matzler, K., Hinterhuber, H.H.: How to make product development projects more successful by integrating Kano’s model of customer satisfaction into quality function deployment. Technovation 18(1), 25–38 (1998)CrossRefGoogle Scholar
  3. 3.
    Gunasekaran, A.: Agile manufacturing: enablers and an implementation framework. Int. J. Prod. Res. 36(5), 1223–1247 (1998)MATHCrossRefGoogle Scholar
  4. 4.
    Lõun, K., Riives, J., Otto, T.: Evaluation of the operation expedience of technological resources in a manufacturing network. Estonian Journal of Engineering, 51–65 (2011)Google Scholar
  5. 5.
    Teece, D.J.: Capturing Value from Knowledge Assets. California Management Review 40(3), 55–79 (1998)CrossRefGoogle Scholar
  6. 6.
    Becerra-Fernandez, I., Gonzalez, A., Sabherwal, R.: Knowledge Management. Pearson Education, Inc., Upper Saddle River (2004)Google Scholar
  7. 7.
    Davenport, T.H., De Long, D.W., Beers, M.C.: Successful Knowledge Management Projects. Sloan Management Review, 43–57 (1998)Google Scholar
  8. 8.
    O’Brien, J.A., Marakas, G.M.: Management Information Systems. The McGraw-Hill Companies, Inc. (2008)Google Scholar
  9. 9.
    Blake, I., Gerard, P.L.: The information system as a competitive weapon. Communications of the ACM - Special Section on Management of Information Systems 27(12), 1193–1201 (1984)Google Scholar
  10. 10.
    Saaksvuori, A., Immonen, A.: Product Lifecycle Management, 3rd edn. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Shevtshenko, E., Karaulova, T., Kramarenko, S., Wang, Y.: IDSS used as a framework for collaborative projects in conglomerate enterprises. Journal of Achievements in Materials and Manufacturing Engineering, 89–92 (2007)Google Scholar
  12. 12.
    PDM-ERP Middleware (December 2012),
  13. 13.
    Berson, A., Smith, S.J.: Data Warehousing, Data Mining & OLAP. McGraw Hill (1997)Google Scholar
  14. 14.
    Arnrich, B., Walter, J., Albert, A., Ennker, J., Ritter, H.: Data Mart based research in heart surgery: challenges and benefits, September 7-11. MedInfo, San Francisco (2004)Google Scholar
  15. 15.
    Wang, K.: Applying data mart to manufacturing: the nature and implications. Journal of Intelligent Manufacturing 18, 487–495 (2007)CrossRefGoogle Scholar
  16. 16.
  17. 17.
    Sharon, K.J.: Combing QFD and FMEA to optimize performance. ASQC Quality Congress 52, 564–575 (1998)Google Scholar
  18. 18.
    Mcdermott, R.E., Mikulak, R.J., Beauregard, M.R.: The basics of FMEA. Resources Engineering, Inc., USA (1996)Google Scholar
  19. 19.
    Stamatis, D.H.: Failure mode and effect analysis: FMEA from theory to execution, 2nd edn. ASQ Quality Press (2003)Google Scholar
  20. 20.
    DOE-NE-STD-1004-92, Root Cause Analysis Guidance Document, US (1992), (November 2012)
  21. 21.
    Anbari, F.T.: Six Sigma Method and Its Applications in Project Management. In: Proceedings of the Project Management Institute Annual Seminars and Symposium, San Antonio, Texas. Project Management Institute, Newtown Square (2002)Google Scholar
  22. 22.
    Rancour, T., McCracken, M.: Applying six sigma methods for breakthrough safety performance, pp. 31–34. American Society of Safety Engineers (2000)Google Scholar
  23. 23.
    Antony, J.: Some pros and cons of Six Sigma: an academic perspective. The TQM Magazine 16(4), 303–306 (2004)CrossRefGoogle Scholar
  24. 24.
    Sahno, J., Opik, R., Kostina, M., Paavel, M., Shevtshenko, E., Wang, Y.: Knowledge Management Framework for Production Route Selection in Manufacturing Enterprises. In: 8th International DAAAM Baltic Conference Industrial Engineering, pp. 567–572 (2012)Google Scholar
  25. 25.
    Kimball, R., Ross, M.: The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2nd edn. John Wiley & Sons, New York (2002)Google Scholar
  26. 26.
    Antony, J., Banuelas, R.: Key ingredients for the effective implementation of six sigma program. Measuring Business Excellence 6, 20–27 (2002)CrossRefGoogle Scholar
  27. 27.
    Gregory, H.W.: Six Sigma for Business Leaders, 1st edn. Business Systems Solutions, Inc., USA (2004)Google Scholar
  28. 28.
    McClusky, R.: The Rise, fall, and revival of six sigma. Measuring Business Excellence 4, 6–17 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jevgeni Sahno
    • 1
  • Eduard Sevtsenko
    • 1
  • Tatjana Karaulova
    • 1
  1. 1.Department of Mechanical EngineeringTallinn University of TechnologyTallinnEstonia

Personalised recommendations