A hybrid MCDM-based FMEA model for identification of critical failure modes in manufacturing
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METHODOLOGIES AND APPLICATION
A hybrid MCDM-based FMEA model for identification of critical failure modes in manufacturing Huai-Wei Lo1 • William Shiue2 • James J. H. Liou3 • Gwo-Hshiung Tzeng4
Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The effective identification of critical failure modes of individual equipment components or processes and the development of plans for improvement are crucial for the manufacturing industry. Recently, the failure modes and effects analysis (FMEA) approach based on multiple criteria decision making (MCDM) has been utilized effectively for the assessment of primary failure modes and risks. However, the ranking results of failure modes produced by different MCDM methods might be different. This study proposes an integrated risk assessment model where several techniques are combined to produce an FMEA model for the generation of comprehensive failure mode ranking. First, the anticipated costs and environmental protection indicators are included in the FMEA model to enhance the comprehensiveness of assessment. Then, an influential network relationship map of risk factors is obtained by using the decision-making trial and evaluation laboratory (DEMATEL) technique to assist in identifying the critical factors. Finally, the ranking of the failure modes is identified using the four integrated MCDM methods, based on the technique for order preference by similarity to ideal solution (TOPSIS) concept. In addition, data from a machine tool manufacturing company survey are applied to demonstrate the effectiveness and robustness of the proposed model. Keywords Multiple criteria decision making (MCDM) Failure mode and effects analysis (FMEA) Risk assessment Manufacturing
1 Introduction In today’s fast-changing and dynamic business environment, organizations are being forced to use intelligent production systems to increase production efficiency, optimize product and service quality, and improve environmental protection (Stock and Seliger 2016; Lu 2017; Moktadir et al. 2018). The incorporation of automation and
Communicated by V. Loia. & James J. H. Liou [email protected] 1
Graduate Institute of Industrial and Business Management, National Taipei University of Technology, Taipei, Taiwan
2
King’s Business School, King’s College London, London, UK
3
Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan
4
Graduate Institute of Urban Planning, College of Public Affairs, National Taipei University, New Taipei City, Taiwan
data exchange into manufacturing processes leading to the trend of Industry 4.0 has affected the whole supply chain system, including the design, manufacturing, sales, and after-sales services of products. Reliability has become a crucial factor, and equipment failure must be reduced to improve production. Consequently, robust production equipment is the essential for manufacturers in Industry 4.0 (Kamp et al. 2017). From the perspectiv
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