Authors: Matteo Mario Savino; Alessandro Brun; Chen Xiang
Addresses: Department of Engineering, University of Sannio, Piazza Roma, 21 – 82100, Benevento, Italy ' Department of Management Engineering, Politecnico di Milano, Via Lambruschini 4/b, 20100, Milano, Italy ' Department of Electronic and Information Engineering, Zhongyuan University of Technology, No. 41 Zhongyuan Road (M), Zhengzhou, 450007, China
Abstract: This work focuses on the problem of non conformity (NC) characterisation in quality management systems (QMS) and introduces a fuzzy inference engine (FE) for NC analysis based on multi-stage quality control. The research has a twofold objective: 1) to characterise NCs based on risk analysis principles, 2) to define NC priorities. The FE is implemented according to the main requirements of the new ISO 9001:2015 Standard regarding risk analysis and NC assessment. The methodology was tested within an assembly line of mechanical components, where a number of NCs were detected and classified with respect to multiple features. Within this classification, risk analysis is explored through the use of failure mode effects and criticality analysis (FMECA). A risk criticality index (RCI) is defined and evaluated, which addresses NC criticality and the relative action priorities. [Received 28 January 2016; Revised 25 March 2016; Accepted 24 June 2016]
Keywords: fuzzy inference engine; quality management systems; QMS; non-conformity; risk assessment; failure mode effects and criticality analysis; FMECA; ISO 9001; fuzzy logic; quality control; risk criticality index; quality standards.
European Journal of Industrial Engineering, 2017 Vol.11 No.1, pp.78 - 100
Published online: 06 Jan 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article