Hostname: page-component-8448b6f56d-wq2xx Total loading time: 0 Render date: 2024-04-18T00:37:27.669Z Has data issue: false hasContentIssue false
  • English
  • Français

Change mode and effects analysis by enhanced grey relational analysis under subjective environments

Published online by Cambridge University Press:  04 May 2017

Guo-Niu Zhu ,
Jie Hu ,
Jin Qi ,
Tao He  and
Ying-Hong Peng
Guo-Niu Zhu
Affiliation:
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
Jie Hu*
Affiliation:
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
Jin Qi
Affiliation:
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
Tao He
Affiliation:
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
Ying-Hong Peng
Affiliation:
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
*
Reprint requests to: Jie Hu, Mechanical Building A, Room 735, Shanghai Jiao Tong University, No. 800 Dongchuan Road, Minhang District, Shanghai 200240, People's Republic of China. E-mail: hujie@sjtu.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

Change mode and effects analysis (CMEA) is a powerful technique for measuring product flexibility toward future changes and diminishing the cost of redesign as well as shortening time to market. As a systematic methodology, it provides an in-depth view for the investigation of potential changes, causes, and effects in designs, products, and processes. Traditional CMEA determines the risk priorities of change modes by using change potential number, which requires the risk factors of design flexibility, occurrence, and readiness to be precisely evaluated. However, this is not always possible in real applications due to the uncertainty and subjectivity involved in the early design stages. It has been criticized much for its deficiencies in criteria weighting of the risk factors, change potential number calculation, and risk priorities determination of the change modes. This paper presents a systematic evaluation approach for determining a more rational rank of change modes by combining with the entropy weight method, rough number, and grey relational analysis. In this study, the entropy weight method is adopted to calculate the relative importance of risk factors. Rough number is presented to aggregate individual weights and preferences, and to manipulate the vagueness in the evaluation process. Then a rough number enhanced grey relational analysis is proposed to evaluate the risk ranking of change modes. Finally, a practical example is put forward to validate the performance of the proposed method. The result shows that the proposed change mode evaluation method can effectively overcome the shortcomings of traditional CMEA and strengthen the objectivity of product flexibility measurement.

Keywords

Change Mode and Effects AnalysisGrey Relational AnalysisProduct Flexibility MeasurementRough NumberUncertainty and Subjectivity
Type
Special Issue Articles
Information
AI EDAM , Volume 31 , Special Issue 2: Uncertainty Quantification for Engineering Design , May 2017 , pp. 207 - 221
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Ben-Daya, M. (2009). Failure mode and effect analysis. In Handbook of Maintenance Management and Engineering, pp. 75–90. London: Springer.Google Scholar
Bischof, A. (2010). Developing flexible products for changing environments . PhD Thesis. Technische Universitaet Berlin.Google Scholar
Chai, J., & Liu, J.N. (2014). A novel believable rough set approach for supplier selection. Expert Systems With Applications 41(1), 92–104.Google Scholar
Chang, T., & Lin, S.J. (1999). Grey relation analysis of carbon dioxide emissions from industrial production and energy uses in Taiwan. Journal of Environmental Management 56(4), 247–257.Google Scholar
Daims, H., & Wagner, M. (2007). Quantification of uncultured microorganisms by fluorescence microscopy and digital image analysis. Applied Microbiology and Biotechnology 75(2), 237–248.Google Scholar
Deng, J.-L. (1989). Introduction to grey system theory. Journal of Grey System 1(1), 1–24.Google Scholar
Dykstra, M.J., & Reuss, L.E. (2011). Biological Electron Microscopy: Theory, Techniques, and Troubleshooting. New York: Springer Science & Business Media.Google Scholar
Jara-Lazaro, A.R., Thamboo, T.P., Teh, M., & Tan, P.H. (2010). Digital pathology: exploring its applications in diagnostic surgical pathology practice. Pathology 42(6), 512–518.CrossRefGoogle ScholarPubMed
Jarratt, T., Eckert, C.M., Caldwell, N., & Clarkson, P.J. (2011). Engineering change: an overview and perspective on the literature. Research in Engineering Design 22(2), 103–124.Google Scholar
Keese, D.A., Seepersad, C.C., & Wood, K.L. (2009). Product flexibility measurement with enhanced change modes and effects analysis (CMEA). International Journal of Mass Customisation 3(2), 115–145.Google Scholar
Keese, D.A., Takawale, N.P., Seepersad, C.C., & Wood, K.L. (2006). An enhanced change modes and effects analysis (CMEA) tool for measuring product flexibility with applications to consumer products. Proc. ASME 2006 Int. Design Engineering Technical Conf./Computers and Information in Engineering Conf., pp. 873–888. New York: ASME.Google Scholar
Kutlu, A.C., & Ekmekcioglu, M. (2012). Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Systems With Applications 39(1), 61–67.Google Scholar
Lee, C., Lee, H., Seol, H., & Park, Y. (2012). Evaluation of new service concepts using rough set theory and group analytic hierarchy process. Expert Systems With Applications 39(3), 3404–3412.Google Scholar
Lee, W.-S., & Lin, Y.-C. (2011). Evaluating and ranking energy performance of office buildings using grey relational analysis. Energy 36(5), 2551–2556.Google Scholar
Li, Z., Cheng, Z., Feng, Y., & Yang, J. (2013). An integrated method for flexible platform modular architecture design. Journal of Engineering Design 24(1), 25–44.Google Scholar
Liu, H.-C., Fan, X.-J., Li, P., & Chen, Y.-Z. (2014). Evaluating the risk of failure modes with extended MULTIMOORA method under fuzzy environment. Engineering Applications of Artificial Intelligence 34, 168–177.Google Scholar
Liu, H.-C., Liu, L., Liu, N., & Mao, L.-X. (2012). Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment. Expert Systems With Applications 39(17), 12926–12934.Google Scholar
Liu, P., & Zhang, X. (2011). Research on the supplier selection of a supply chain based on entropy weight and improved ELECTRE-III method. International Journal of Production Research 49(3), 637–646.Google Scholar
Liu, Y., Lim, S.C.J., & Lee, W.B. (2013). Product family design through ontology-based faceted component analysis, selection, and optimization. Journal of Mechanical Design 135(8), 081007.Google Scholar
Pawlak, Z. (1997). Rough set approach to knowledge-based decision support. European Journal of Operational Research 99(1), 48–57.CrossRefGoogle Scholar
Qureshi, A., Murphy, J.T., Kuchinsky, B., Seepersad, C.C., Wood, K.L., & Jensen, D.D. (2006). Principles of product flexibility. Proc. ASME 2006 Int. Design Engineering Technical Conf., Computers and Information in Engineering Conf., pp. 295–325. New York: ASME.Google Scholar
Rajan, P.P., Van Wie, M., Campbell, M., Otto, K., & Wood, K. (2003). Design for flexibility-measures and guidelines. DS 31: Proc. ICED 03, the 14th Int. Conf. Engineering Design. Stockholm: ICED.Google Scholar
Rajan, P.P., Van Wie, M., Campbell, M.I., Wood, K.L., & Otto, K.N. (2005). An empirical foundation for product flexibility. Design Studies 26(4), 405–438.Google Scholar
Rao, R.V. (2012). Decision Making in Manufacturing Environment Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods, Vol. 2. New York: Springer Science & Business Media.Google Scholar
Shannon, C.E., & Weaver, W. (2015). The Mathematical Theory of Communication. Champaign, IL: University of Illinois Press.Google Scholar
Singh, R.K., & Benyoucef, L. (2011). A fuzzy TOPSIS based approach for e-sourcing. Engineering Applications of Artificial Intelligence 24(3), 437–448.Google Scholar
Song, W., Ming, X., Han, Y., & Wu, Z. (2013). A rough set approach for evaluating vague customer requirement of industrial product-service system. International Journal of Production Research 51(22), 6681–6701.Google Scholar
Song, W., Ming, X., Wu, Z., & Zhu, B. (2013). Failure modes and effects analysis using integrated weight-based fuzzy TOPSIS. International Journal of Computer Integrated Manufacturing 26(12), 1172–1186.CrossRefGoogle Scholar
Song, W., Ming, X., Wu, Z., & Zhu, B. (2014). A rough TOPSIS approach for failure mode and effects analysis in uncertain environments. Quality and Reliability Engineering International 30(4), 473–486.Google Scholar
Suh, E.S., De Weck, O.L., & Chang, D. (2007). Flexible product platforms: framework and case study. Research in Engineering Design 18(2), 67–89.Google Scholar
Tilstra, A.H., Backlund, P.B., Seepersad, C.C., & Wood, K.L. (2008). Industrial case studies in product flexibility for future evolution: an application and evaluation of design guidelines. Proc. ASME 2008 Int. Design Engineering Technical Conf./Computers and Information in Engineering Conf., pp. 217–230. New York: ASME.Google Scholar
Tseng, M.M., & Hu, S.J. (2014). Mass customization. CIRP Encyclopedia of Production Engineering, pp. 836–843. London: Springer.Google Scholar
Weaver, J., Wood, K., Crawford, R., & Jensen, D. (2010). Transformation design theory: a meta-analogical framework. Journal of Computing and Information Science in Engineering 10(3), 031012.Google Scholar
Xia, M., & Xu, Z. (2012). Entropy/cross entropy-based group decision making under intuitionistic fuzzy environment. Information Fusion 13(1), 31–47.Google Scholar
Ye, J. (2010). Fuzzy decision-making method based on the weighted correlation coefficient under intuitionistic fuzzy environment. European Journal of Operational Research 205(1), 202–204.Google Scholar
Zhai, L.-Y., Khoo, L.-P., & Zhong, Z.-W. (2008). A rough set enhanced fuzzy approach to quality function deployment. International Journal of Advanced Manufacturing Technology 37(5–6), 613–624.Google Scholar
Zhai, L.-Y., Khoo, L.-P., & Zhong, Z.-W. (2009). A rough set based QFD approach to the management of imprecise design information in product development. Advanced Engineering Informatics 23(2), 222–228.CrossRefGoogle Scholar
Zheng, K., Hu, J., Zhan, Z., Ma, J., & Qi, J. (2014). An enhancement for heuristic attribute reduction algorithm in rough set. Expert Systems With Applications 41(15), 6748–6754.Google Scholar
Zhu, G.-N., Hu, J., Qi, J., Gu, C.-C., & Peng, Y.-H. (2015). An integrated AHP and VIKOR for design concept evaluation based on rough number. Advanced Engineering Informatics 29, 408–418.CrossRefGoogle Scholar
Zhu, G.-N., Hu, J., Qi, J., Ma, J., & Peng, Y.-H. (2015). An integrated feature selection and cluster analysis techniques for case-based reasoning. Engineering Applications of Artificial Intelligence 39, 14–22.Google Scholar
Zimic, M., Velazco, A., Comina, G., Coronel, J., Fuentes, P., Luna, C.G., Sheen, P., Gilman, R.H., & Moore, D. (2010). Development of low-cost inverted microscope to detect early growth of Mycobacterium tuberculosis in MODS culture. PLOS ONE 5(3), e9577.Google Scholar