Journal of Intelligent Manufacturing

, Volume 20, Issue 2, pp 139–149 | Cite as

Construction of quality control charts by using probability and fuzzy approaches and an application in a textile company

  • İrfan Ertuğrul
  • Esra Aytaç


A method that uses statistical techniques to monitor and control product quality is called statistical process control (SPC), where control charts are test tools frequently used for monitoring the manufacturing process. In this study, statistical quality control and the fuzzy set theory are aimed to combine. As known, fuzzy sets and fuzzy logic are powerful mathematical tools for modeling uncertain systems in industry, nature and humanity; and facilitators for common-sense reasoning in decision making in the absence of complete and precise information. In this basis for a textile firm for monitoring the yarn quality, control charts proposed by Wang and Raz are constructed according to fuzzy theory by considering the quality in terms of grades of conformance as opposed to absolute conformance and nonconformance. And then with the same data for textile company, the control chart based on probability theory is constructed. The results of control charts based on two different approaches are compared. It’s seen that fuzzy theory performs better than probability theory in monitoring the product quality.


Statistical quality control Fuzzy logic Quality control charts Fuzzy quality control charts 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Anderson D.R., Sweeney D.J., Williams T.A. (1996) Statistics for business and economics. West Publishing Company, St. PaulGoogle Scholar
  2. Bayrak M.Y., Çelebi N., Taskın H. (2007) A fuzzy approach method for supplier selection. Production Planning and Control 18(1): 54–63CrossRefGoogle Scholar
  3. Berenson M.L., Levine D.M. (1999) Basic business statistics. New Jersey, Prentice HallGoogle Scholar
  4. Bojadziev G., Bojadziev M. (1991) Fuzzy sets, fuzzy logic, applications. World Scientific, LondonGoogle Scholar
  5. Bradshaw C.W. (1983) A fuzzy set theoretic interpretation of economic control limits. European Journal of Operational Research 13: 403–408CrossRefGoogle Scholar
  6. Cheng C. (2005) Fuzzy process control: Construction of control charts with fuzzy numbers. Fuzzy Sets and Systems 154(2): 287–303CrossRefGoogle Scholar
  7. Ertuğrul İ. (2004) Toplam kalite kontrol ve teknikleri. Bursa, Ekin KitabeviGoogle Scholar
  8. Franceschini F., Romano D. (1999) Control chart for linguistic variables: A method based on the use of linguistic quantifiers. International Journal of Production Research 37(16): 3791–3801CrossRefGoogle Scholar
  9. Franceschini F., Galetto M., Varetto M. (2005) Ordered samples control charts for ordinal variables. Quality and Reliability Engineering International 21: 177–195CrossRefGoogle Scholar
  10. Guiffrida A.L., Nagi R. (1998) Fuzzy set theory application in production management research: A literature survey. Journal of Intelligent Manufacturing. 9(1): 39–56CrossRefGoogle Scholar
  11. Gülbay M., Kahraman C., Ruan D. (2004) α-cut fuzzy control charts for linguistic data. International Journal of Intelligent Systems 19: 1173–1195CrossRefGoogle Scholar
  12. Gülbay M., Kahraman C. (2007) An alternative approach to fuzzy control charts: Direct fuzzy approach. Information Sciences 177: 1463–1480CrossRefGoogle Scholar
  13. Kahraman, C., Tolga, E., & Ulukan, Z. (1995). Using triangular fuzzy numbers in the tests of control charts for unnatural patterns. In Proceedings of INRIA/IEEE conference on emerging technologies and factory automation (Vol. 3, pp. 291–298). October 10–13, Paris, France.Google Scholar
  14. Kanagawa A., Tamaki F., Ohta H. (1993) Control charts for process average and variability based on linguistic data. International Journal of Production Research 31(4): 913–922CrossRefGoogle Scholar
  15. Kandel A. (1986) Fuzzy mathematical techniques with applications. Addison-Wesley Publishing Company, BostonGoogle Scholar
  16. Korvin A., Shipley M.F. (2001) Sample size: Achieving quality and reducing financial loss. International Journal of Quality & Reliability Management 18(7): 678–691CrossRefGoogle Scholar
  17. Krajewski L.J., Ritzman L.P. (1998) Operations management/strategy and analysis. Addison-Wesley Publishing Company, GlenviewGoogle Scholar
  18. Martinich J.S. (1997) Production and operations management: An applied modern approach. Wiley, USAGoogle Scholar
  19. Montgomery D.C. (1991) Introduction to statistical quality control. Wiley, New YorkGoogle Scholar
  20. Raz T., Wang J.H. (1990) Probabilistic and membership approaches in the construction of control charts for linguistic data. Production Planning & Control 1: 147–157CrossRefGoogle Scholar
  21. Rowlands H., Wang L.R. (2000) An approach of fuzzy logic evaluation and control in SPC. Quality & Reliability Engineering International 16: 91–98CrossRefGoogle Scholar
  22. Stevenson W. (1993) Production/operation management. Homewood, IrwinGoogle Scholar
  23. Taleb H., Limam M. (2002) On fuzzy and probabilistic control charts. International Journal of Production Research 40(12): 2849–2863CrossRefGoogle Scholar
  24. Taleb, H., & Limam, M. (2005). Fuzzy multinominal control charts. In AI*IA 2005: 9th Congress of the Italian Association for Artificial Intelligence (Vol. 3673, pp. 553–563). September 21–23, Milan, Italy.Google Scholar
  25. Wang C.R., Chen C.H. (1995) Economic statistical np control chart designs based on fuzzy optimization. International Journal of Quality & Reliability Management 12(1): 82–92CrossRefGoogle Scholar
  26. Wang J.H., Raz T. (1990) On the construction of control charts using linguistic variables. International Journal of Production Research 28(3): 477–487CrossRefGoogle Scholar
  27. Yen J., Langari R. (1999) Fuzzy logic, intelligence, control and information. New Jersey, Prentice HallGoogle Scholar
  28. Zadeh L.A., Kacprzyk J. (1992) Fuzzy logic for the management of uncertainty. Wiley, New YorkGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.The Faculty of Economic and Administrative Sciences, Department of Bussiness AdministrationUniversity of PamukkaleDenizliTurkey

Personalised recommendations