Change mode and effects analysis by enhanced grey relational analysis under subjective environments. Issue 2 (4th May 2017)

• Record Type: Journal Article
• Authors: Zhu, Guo-Niu; Hu, Jie; Qi, Jin; He, Tao; Peng, Ying-Hong
• Editors: Ghanem, Roger; Du, Xiaoping
• Is Part Of: AI EDAM. Volume 31:Issue 2(2017)
• Page Start: 207
• Page End: 221
• Publication Date: 2017-05-04
• Subjects: Change Mode and Effects Analysis, -- Grey Relational Analysis, -- Product Flexibility Measurement, -- Rough Number, -- Uncertainty and Subjectivity; Engineering design -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; 620.00420285
• DOI: 10.1017/S0890060417000099
• Languages: English
• ISSNs: 0890-0604

Abstract

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.