A novel PFMEA-Doubly TOPSIS approach-based decision support system for risk analysis in milk process industry

PurposeThis 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/approachProcess 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.FindingsFailure 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/implicationsThe 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 implicationsThe 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 implicationsThe 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/valueThe 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.

Extending the linguistic decision suite FLINTSTONES to deal with comparative linguistic expressions with symbolic translation information

Uncertainty is so common in real decision situations that it has given rise to a new decision making approach so-called linguistic decision making, in which such uncertainty is modeled by using linguistic information. Many contributions have been proposed in order to solve LDM problems by following a Computing with Words (CW) approach to obtain linguistic outputs from linguistic premises by emulating the human beings’ reasoning process. Nowadays, there are several LDM models that, together with the complexity of LDM problems, make almost impossible to find a proper solution for these problems without a support tool. FLINTSTONES is a fuzzy decision support system that facilitates the decision process in LDM problems. This software aimed to solve LDM problems by means of the 2-tuple linguistic model, whose main advantages are high interpretability and precision of the results. However, it has other drawbacks such as the modeling of linguistic information by using solely single linguistic terms or the impossibility to model the experts’ hesitancy. Recently, the Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT) linguistic representation model was introduced in order to overcome existing drawbacks in terms of interpretability and accuracy in CW processes. This model allows to model experts’ hesitancy and, at the same time, carry out precise linguistic computations and provide interpretable results, overcoming the limitations of previous LDM models. Therefore, this contribution presents an updated version of FLINTSTONES able to manage ELICIT information in LDM problems and which integrates the ELICIT CW approach.