A novel PFMEA-Doubly TOPSIS approach-based decision support system for risk analysis in milk process industry. Issue 1 (26th February 2021)

• Record Type: Journal Article
• Authors: Das, Indraneel; Panchal, Dilbagh; Tyagi, Mohit
• Is Part Of: International journal of quality & reliability management. Volume 39:Issue 1(2022)
• Page Start: 1
• Page End: 29
• Publication Date: 2021-02-26
• Subjects: Milk process industry -- Fuzzy decision support system -- PFMEA -- Risk factor -- DTOPSIS -- VIKOR; Quality control -- Periodicals; 658.562
• DOI: 10.1108/IJQRM-10-2019-0320
• Languages: English
• ISSNs: 0265-671X

Abstract

Abstract : Purpose: This paper aims to presents a novel integrated fuzzy decision support system for analyzing the issues related to failure of a milk process plant unit. Design/methodology/approach: Process failure mode effect analysis (PFMEA) approach was implemented to list failure causes under each subsystem/component and fuzzy ratings for three risk criteria, i.e. probability of failure occurrence (O_f), severity (S) and non-detection (O_d) are collected against the listed failure causes through experts feedback. A new doubly technique for order of preference by similarity to ideal solution (DTOPSIS) approach was implemented within fuzzy PFMEA tool for ranking of listed failure causes. The proposed decision support system overcomes the restrictions of classical PFMEA and IF-THEN rule base PFMEA approaches in an effective way. Findings: Failure causes such as electrical winding failure (RM4), high pressure in plate region (C1), communication problem in supervisory control and data acquisition control (MS3), insulation problem (ST2), lever breakage (B2), gasket problem (D3), formation of holes (PHE5), cavitations (FP7), deposition of milk particle inside the pipeline because of improper cleaning (MHP2) were acknowledged as the most critical one with the application of proposed decision support system. Research limitations/implications: The analysis results are based on subjective judgments of the experts and therefore correctness of risk ranking results are totally dependent upon the quality of input data/information available from these experts. However, the analyst has taken proper care for considering the vagueness of the raw data by incorporating fuzzy set theory within the proposed decision support system. Practical implications: The proposed fuzzy decision support system has been presented with its application on milk pasteurization plant of a milk process industry. The analysis based ranking results have been supplied to maintenance manager of the plant and a consent was shown by him with these results. Once the top management of the plant took decision for the implementation of these results, the detailed robustness of the proposed decision support system could be evaluated further. Social implications: The analysis result would be highly useful for minimizing sudden breakdowns and operational cost of the plant which directly contributes to plant's profitability. With the decrease in the chances of sudden breakdowns there would be high safety for the people working on/off the plant's site. Further, with increase in availability of the considered plant the societal daily demand related to dairy products could be easily fulfilled at reasonable prices. Originality/value: The performance and proficiency of the proposed decision support system has been evaluated by comparing the ranking results with classical TOPSIS and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approaches based results.