Document Type : Research Paper


Department of Industrial Engineering and Management, Faculty of Mechanical Engineering, Khulna University of Engineering and Technology, Khulna, Bangladesh.


In the statistical process control when the process is very sensitive and control limit shifts are the prime concerns, there fuzzy control charts can be a better solution. In decision making, extra “rather in control” and “rather out of control” decisions facilitate to find out the slight changes in the control chart. The automation of fuzzy control chart in the Excel VBA makes the data input and decisions making process faster. The vagueness of the data is removed as the charts deal with the triangular or trapezoidal area rather than some points in the control limits. Alongside the fuzzy control charts, Marcucci approach has been followed to find out the goodness-of-fit of the samples and to find out the effectiveness of fuzzy control charts.


Main Subjects

[1]     Erginel, N. (2014). Fuzzy rule-based $tilde p $ and $ ntilde p $ control charts. Journal of intelligent & fuzzy systems27(1), 159-171.
[2]      Taleb, H., & Limam, M. (2002). On fuzzy and probabilistic control charts. International journal of production research40(12), 2849-2863.
[3]      El-Shal, S. M., & Morris, A. S. (2000). A fuzzy rule-based algorithm to improve the performance of statistical process control in quality systems. Journal of intelligent & fuzzy systems9(3, 4), 207-223.
[4]     Paul, A. K., Shill, P. C., Rabin, M. R. I., & Murase, K. (2018). Adaptive weighted fuzzy rule-based system for the risk level assessment of heart disease. Applied intelligence48(7), 1739-1756.
[5]     Hsu, H. M., & Chen, Y. K. (2001). A fuzzy reasoning based diagnosis system for X control charts. Journal of intelligent manufacturing12(1), 57-64.
[6]     [6] Yang, X., Wang, Z., & Zi, X. (2017). Goodness-of-fit-based outlier detection for Phase I monitoring. Communications in Statistics - Simulation and Computation, 1–13.
[7]     Sousa, S., Rodrigues, N., & Nunes, E. (2017). Application of SPC and quality tools for process improvement. Procedia manufacturing11, 1215-1222.
[8]     Kaya, I., Erdoğan, M., & Yıldız, C. (2017). Analysis and control of variability by using fuzzy individual control charts. Applied soft computing51, 370-381.
[9]     Fadaei, S., & Pooya, A. (2018). Fuzzy U control chart based on fuzzy rules and evaluating its performance using fuzzy OC curve. The TQM journal30(3), 232-247.
[10]  Zhang, B., Yang, C., Zhu, H., Shi, P., & Gui, W. (2018). Controllable-domain-based fuzzy rule extraction for copper removal process control. IEEE transactions on fuzzy systems26(3), 1744-1756.
[11]  Naik, N., Diao, R., & Shen, Q. (2018). Dynamic fuzzy rule interpolation and its application to intrusion detection. IEEE transactions on fuzzy systems26(4), 1878-1892.
[12]  Keivanpour, S., Ait-Kadi, D., & Mascle, C. (2017). Automobile manufacturers’ strategic choice in applying green practices: joint application of evolutionary game theory and fuzzy rule-based approach. International journal of production research55(5), 1312-1335.
[13]  Cheng, C. B. (2005). Fuzzy process control: Construction of control charts with fuzzy numbers. Fuzzy sets and systems154(2), 287-303.
[14]  Faraz, A., & Moghadam, M. B. (2007). Fuzzy control chart a better alternative for Shewhart average chart. Quality & quantity41(3), 375-385.
[15]  Erginel, N. (2008). Fuzzy individual and moving range control charts with α-cuts. Journal of intelligent & fuzzy systems, 19(4, 5), 373-383.
[16]  Colubi, A. (2009). Statistical inference about the means of fuzzy random variables: Applications to the analysis of fuzzy-and real-valued data. Fuzzy sets and systems, 160(3), 344-356.
[17]  Kanagawa, A., Tamaki, F., & Ohta, H. (1993). Control charts for process average and variability based on linguistic data. The international journal of production research, 31(4), 913-922.
[18]  Kaya, İ., & Kahraman, C. (2011). Process capability analyses based on fuzzy measurements and fuzzy control charts. Expert systems with applications, 38(4), 3172-3184.
[19]  Faraz, A., & Shapiro, A. F. (2010). An application of fuzzy random variables to control charts. Fuzzy sets and systems, 161(20), 2684-2694.
[20]  Laviolette, M., Seaman, J. W., Barrett, J. D., & Woodall, W. H. (1995). A probabilistic and statistical view of fuzzy methods. Technometrics37(3), 249-261.
[21]  Marcucci, M. (1985). Monitoring multinomial processes. Journal of quality technology17(2), 86-91.
[22]  Saaty, T. L. (1974). Measuring the fuzziness of sets. Taylor & Francis, 4(4), 53-61.
[23]  Wang, J. H., & RAZ, T. (1990). On the construction of control charts using linguistic variables. The international journal of production research28(3), 477-487.
[24]  Woodall, W. H. (1997). Control charts based on attribute data: Bibliography and review. Journal of quality technology29(2), 172-183.
[25]  Woodall, W. H., Tsui, K. L., & Tucker, G. R. (1997). A review of statistical and fuzzy quality control charts based on categorical data. Frontiers in statistical quality control (pp. 83-89). Physica, Heidelberg.
[26]  Montgomery, D. C. (2009). Introduction to statistical quality control. John Wiley & Sons (New York).