Proposing a new model of failure mode and effect analysis for clustering and ranking of manufacturing process

Farshad Faezy Razi and Ehsan Hoseini

International Journal of Productivity and Quality Management, 2017, vol. 21, issue 1, 45-71

Abstract: This article proposes a new framework for the failure mode and effect analysis (FMEA) in the manufacturing process. This paper will argue that how a new approach offers a different analysis of the risk of failure compared with the traditional approach in the FMEA model. Through a case study in the Paper Machine Type: Andritz DC 100, it was tried to examine the effectiveness of the new approach. The method used in this study is descriptive and mathematical modelling. The implementation results of KOHNEN algorithm showed that all failure modes in the studied machine do not fall in one cluster and that is why each cluster of failure modes must be evaluated independently and separately from other clusters. Unlike other FMEA models, the present paper initially clusters failure modes through the KOHNEN neural network. Failure modes classified in each cluster will be evaluated independently of any other cluster through the SBM-DEA model.

Keywords: failure mode and effects analysis; FMEA; KOHNEN neural networks; slack-based measure; SBM; data envelopment analysis; DEA; clustering; ranking; manufacturing processes; case study; mathematical modelling. (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=83281 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijpqma:v:21:y:2017:i:1:p:45-71

Access Statistics for this article

More articles in International Journal of Productivity and Quality Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().