APPLICATION OF FUZZY SETS THEORY TO QUALITY MANAGEMENT

Slavko Ivković, Aleksandar Pešić

Abstract


Quality management implies the usage of various quality control tools and methods for analyzing data related to quality performance with the aim of identifying trouble spots and the areas that need quality improvements. Evaluation of this important aspect of production management is determined by the organization's capacity for self-appraisal and manager’s capabilities to use statistical techniques and effectively assess different production and quality parameters. However, in some cases when the information available is uncertain, imprecise and/or incomplete and the assessment include human subjectivity, it is not possible to define parameters as crisp values or reasonably describe them in conventional quantitative expressions. In such situations, parameters could be expressed by fuzzy linguistic variables that successfully cope with the vagueness in linguistic expressions. Since the concept of linguistic variables plays a pivotal role in almost all applications of fuzzy sets theory, it is also used in this paper in formulating the assessment model of production and quality control. In our model, specific linguistic variable, expressed in five fuzzy triangular numbers, is used to assess key parameters. In order to illustrate the industrial application of proposed fuzzy assessment model, we conducted an empirical study that involved six major suppliers in automotive industry in Vojvodina. Empirical data were drawn from 44 senior managers using fuzzy rating scale-based questionnaires. Results are discussed and directions for future research are provided.

Keywords


Quality management, Quality control, Fuzzy sets, Linguistic variables, Fuzzy triangular numbers

Full Text:

PDF (Serbian)

References


Antony, J. (2004). Some Pros and Cons of Six Sigma: An Academic Perspective. The TQM Magazine, 16(4), 303-306.

Aslam, M., & Jun, C.H. (2010). A Double Acceptance Sampling Plan for Generalized Log-Logistic Distributions with Known Shape Parameters. Journal of Applied Statistics, 37(3), 405–414.

Bojadziev, G., & Bojadziev, M. (2007). Fuzzy Logic for Business, Finance and Management, 2nd Edition. World Scientific Publishing Co. Pte. Ltd.

Bouchereau, V., & Rowlands, H. (2000). Methods and techniques to Help Quality Function Deployment (QFD). Benchmarking, (7), 8–19.

Chen, Y.Z., & Ngai, E.W.T. ( 2008). A Fuzzy QFD Program Modelling Approach Using the Method of Imprecision. International Journal of Production Research, 46(24), 6823–6840.

Chen, L.H., & Ko, W.C. (2011). Fuzzy Nonlinear Models for New Product Development Using Four-Phase Quality Function Deployment Processes. IEEE Transactions on Systems, (41), 927-945.

Chen, L.H., & Ko, W.C., Tseng, C.Y. (2013). Fuzzy Approaches for Constructing House of Quality in QFD and its Applications: A Group Decision Making Method, IEEE Transactions on Engineering Management, 60, 77-87.

Demicco, R.V., & Klir, G.J. (2004). Fuzzy logic in geology, Elsevier Academic Press.

Duate B.P.M., & Saraiva, P.M. (2008). An Optimization Based Approach for Designing Attribute Acceptance Sampling Plans, International journal of Quality & Reliability Management, 25(8),824−841.

El-Shal, S.M., & Morris, A.S. (2000). A Fuzzy Expert System for Fault Detection in Statistical Process Control of Industrial Processes, IEEE Transactions on Systems, 30(2), 281-289.

Fernandez, A.J., & Perez-Gonzalez, C.J. (2012). Optimal Acceptance Sampling Plans for Log-Location-Scale Lifetime Models Using Average Risks, Computational Statistics and Data Analysis, 56 , 719-731.

Glushkovsky, E. A., & Florescu, R. A. (1996). Fuzzy Sets Approach to Quality Improvement, Quality and Reliability Engineering International, 12(1), 27-37.

Goetsch, D., & Davis, S.B. (2010). Quality Management for Organizational Excellence: Introduction to Total Quality Management, Sixth Edition, Pearson Higher Education

Grzegorzewski, P. (2001). Acceptance Sampling Plans by Attributes with Fuzzy Risks and Quality Levels, In: Wilrich PTh, Lenz H J (eds) Frontiers in Statistical Quality Control, 6, Springer, Heidelberg, 36−46.

Guiffrida, A., & Nagi, R. (1998). Fuzzy Set Theory Applications in Production Management Research: A Literature Survey, Journal of Intelligent Manufacturing, 9, 39-56.

Gulbay, M., & Kahraman, C. (2006). Development of Fuzzy Process Control Charts and Fuzzy Unnatural Pattern Analyses, Computational Statistics & Data Analysis, 51, 434-451.

Han, C.H., Kim, J.K., & Choi, S.H. (2004). Prioritizing Engineering Characteristics in Quality Function Deployment with Incomplete Information: A Linear Partial Ordering Approach, International Journal of Production Economics, 91, 235-249.

Hryniewicz, O. (2008). Statistics with Fuzzy Data in Statistical Quality Control, Soft Computing, 12(3), 229-234.

Hsieh, C.C., & Lu, J.T. (2013). Risk-Embedded Bayesian Acceptance Sampling Plans via Conditional Value-at Risk with Type II censoring, Computers & Industrial Engineering, 65, 551–560.

Jia, G.Z., & Bai, M. (2011). An Approach for Manufacturing Strategy Development Based on Fuzzy-QFD Computers & Indusrial Engineering, 60, 445-454.

Kanagawa, A., & Ohta, H. (1990). A Design for Single Sampling Attribute Plan Based on Fuzzy Sets Theory, Fuzzy Sets and Systems, 37(2), 173-181.

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), 913-922.

Kahraman, C. (2006). Fuzzy Applications in Industrial Engineering, Springer-Verlag Berlin Heidelberg

Kahraman, C., & Yanik, S. (2016). Intelligent Decision Making in Quality Management, Theory and Applications, Springer International Publishing Switzerland

Kahraman, C., Yavuz, M. (Eds.). (2010). Production Engineering and Management Under Fuzziness, Heidelberg: Springer-Verlag.

Karsak, E.E. (2004). Fuzzy Multiple Objective Decision-Making Approach to Prioritize Design Requirements in Quality Function Deployment, International Journal of Production Research, 42, 3957-3974.

Khoo, L. P., & Ho, N. C. (1996). Framework of a Fuzzy Quality Deployment System, International Journal of Production Research, 34(2), 299-311.

Kolli, S. (2012). Essentials of Production and Operations Management, Research & Education Association

Lee, Y.T., Wu, W., & Tzeng, G.H. (2008). An Effective Decision-Making Method Using a Combined QFD and ANP Approach, WSEAS Transactions on Business and Economics, 12(5), 541-551.

Fallahnezhad, M.S., Niaki, S.T.A. , & Abooie, M.H. (2011). A New Acceptance Sampling Plan Based on Cumulative Sums of Conforming Run-Lengths, Journal of Industrial and Systems Engineering, 4(4), 256-264.

Pastuizaca Fernandez, M.N, Carrion García, A., & Ruiz Barzola, O. (2015). Multivariate Multinomial T2 Control Chart Using Fuzzy Approach. International Journal of Production Research, 53(7), 2225-2238.

Pešić, A., Pešić, D., & Ivković, S. (2014). Linguistic Fuzzy Variables as Analysis Tool in Inventory Management, FBIM Transactions , 2(2), 258-270.

Rayan, T.P. (2011). Statistical Methods for Quality Improvement, Third Edition. John Wiley & Sons, Inc.

Turanoglu, E., Kaya, I., & Kahraman, C. (2012). Fuzzy Acceptance Sampling and Characteristics Curves, International Journal of Intelligence Systems, 5(1), 13-29.

Vallabhaneni, S.R. (2016). Wiley CIA excell Exam Review 2016: Internal Audit Knowledge Elements, John Wiley & Sons, Inc.

Wang, Y.M. (2012). A Fuzzy-Normalisation-Based Group Decision Making Approach for Prioritizing Engineering Design Requirements in QFD Under Uncertainty. International Journal of Production Research, (50), 6963-6977.

Wang, R.C., & Chen, C.H. (1995). Economic Statistical NP-Control Chart Designs Based on Fuzzy Optimization. International Journal of Quality and Reliability Management, 12(1), 82-92.

Wang, J. H., & Raz, T. (1990). On the Construction of Control Charts Using Linguistic Variables. International Journal of Production Research, 28(3), 477-487.

Wong, B.K., & Lai, V.S. (2011). A Survey of the Application of Fuzzy Set Theory in Production and Operations Management: 1998 – 2009. International Journal of Production Economics, 129, 157-168.

Yan, H.B., & Ma, T. (2015). A Group Decision-Making Approach to Uncertain Quality Function Deployment Based on Fuzzy Preference Relation and Fuzzy Majority. European Journal of Operational Research, (241), 815-829.

Zadeh, L.A. (2008). Is There a Need for Fuzzy Logic?, Information Sciences, 178, 2751-2779.


Refbacks

  • There are currently no refbacks.