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The proposed hybridized framework provides a new performance optimization-based paradigm for analysing the failure behaviour of paneer unit (PU) in the dairy industry.

A novel fuzzy Jaya-based Lambda–Tau Optimization (JBLTO) approach-based mathematical modelling was developed for calculating various reliability indices of the considered unit. Failure mode and effect analysis (FMEA) was carried using qualitative information gathered from system's expert opinions. Fuzzy-complex proportional assessment (FCOPRAS) approach was integrated within FMEA to recognize the most critical failure causes associated with various subsystem/components.

The availability of the unit falls by 0.053% as the uncertainty level increases from ±15 to ±25% and further decreases to 0.323% as the uncertainty level increases from ±25 to ±60%. Failure causes, namely wearing in gears of gearbox (MST4), an impeller's cavitation and/or corrosion (CFP4), winding failure of electric motor (WS9), were recognized as the most critical failure causes with FCOPRAS final performance scores of 100, 100 and 100 and fuzzy combinative distance-based assessment (FCODAS) resultant assessment score of 0.5997, 1.1898 and 1.6135.

JBLTO approach-based reliability results were compared with traditional particle swarm optimization-based Lambda–Tau (PSOBLT) and traditional fuzzy Lambda–Tau (FLT) approaches for confirming the downward trend in the system's availability. The ranking results of qualitative analysis are compared with the implementation of FCODAS technique. Sensitivity analysis was executed to evaluate the robustness of the proposed hybridized framework.

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.

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.

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.

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.

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.

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.

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.

The purpose of this paper is to develop a fuzzy methodology approaches based framework for carrying the risk analysis of a real industrial system of a urea fertilizer industry located in northern part of India.

Petri Net approach was applied for representing the series-parallel arrangement of the considered system. Various failure causes related to different subsystems or equipment of the considered system were listed under FMEA approach and their Risk Priority Number was tabulated. Further, to overcome the drawbacks of traditional FMEA approach in risk ranking fuzzy FMEA and grey relation analysis (GRA) approaches were applied within traditional FMEA approach and the ranking results were compared for better and effective decision making of risky components.

The proposed framework has overcome the drawbacks of tradition FMEA approach in an effective and efficient manner. Causes AC7, CL3, ST2, DR3 and NR3 of centrifugal compressor, hot heat exchanger, ammonia convertor reactor, cold condenser and ammonia separator have been identified as the most critical failure causes of the considered system.

The proposed framework has been tested with its application on an ammonia synthesis system of the considered process industry. The risk ranking results would be highly useful in developing a planned maintenance policy for the considered system which further results in improving the system availability.

An integrated intuitionistic fuzzy (IF) modelling-based framework for examining the performance analysis of a packaging unit (PU) in three different stages has been proposed.

For the series and parallel configuration of PU, a mathematical model based on the intuitionistic fuzzy Lambda–Tau (IFLT) approach was developed in order to calculate various reliability parameters at various spreads. For determining membership and non-membership function-based reliability parameters for the top event, AND/OR gate transitions expression was employed.

For 15%–30% spread, unit’s availability for the membership function falls by 0.006442%, and it falls even more by 0.014907% with an increase in spread from 30% to 45%. In contrast, for 15%–30% spread, the availability of non-membership function-based systems reduces by 0.007491% and further diminishes. Risk analysis has presented applying an emerging approach called intuitionistic fuzzy failure mode and effect analysis (IFFMEA). For each of the stated failure causes, the output values of the intuitionistic fuzzy hybrid weighted Euclidean distance (IFHWED)-based IFFMEA have been tabulated. Failure causes like HP1, MT6, FB9, EL16, DR23, GR27, categorized under subsystems, namely hopper, motor, fluidized bed dryer, distributor, grader and bin, respectively, with corresponding IFFMEA output scores 1.0975, 1.0190, 0.8543, 1.0228, 0.9026, 1.0021, were the most critical one to contribute in the system’s failure.

The limitation of the proposed framework lies in the fact that the results obtained for both reliability and risk aspects mainly depend on the correctness of raw data provided by the experts. Also, an approximate model of PU is obtained from plant experts to carry performance analysis, and hence more attention is required in constructing the model. Under IFLT, reliability parameters of PU have been calculated at various spreads to study and analyse the failure behaviour of the unit for both membership and non-membership function in the IFS of [0.6,0.8]. For both membership- and non-membership-based results, availability of the considered system shows decreasing trend. To improve the performance of the considered system, risk assessment was carried using IFFMEA technique, ranking all the critical failure causes against IFHWED score value, on which more attention should be paid so as to avoid sudden failure of unit.

The livelihood of millions of farmers and workers depends on sugar industries. So perpetual running of these industries is very important from this viewpoint. On the basis of findings of reliability parameters, the maintenance manager could frame a correct maintenance policy for long-run availability of the sugar mills. This long-run availability will generate revenue, which, in turn, will ensure the livelihood of the farmers.

Mathematical modelling of the considered unit has been done applying basic expressions of AND/OR gate. IFTOPSIS approach has been implemented for ranking result comparison obtained under IFFMEA approach. Eventually, sensitivity analysis was also presented to demonstrate the stability of ranking of failure causes of PU.

To meet energy demand and tackle the challenges posed by global warming, Bagasse-based Cogeneration Power Generation (BCPG) plant in sugar mills have tremendous potential due to large-scale supply of renewable fuel called bagasse. To meet this goal, an integrated framework has been proposed for analyzing performance issues of BCPG.

Intuitionistic Fuzzy Lambda-Tau (IFLT) approach was implemented to compute various reliability parameters. Intuitionistic Fuzzy Failure Mode and Effect Analysis (IF-FMEA) approach has been implemented for studying risk issues results in decrease in plant's availability. Moreover, IF- Technique for Order Performance by Similarity to Ideal Solution (IF-TOPSIS) is implemented to verify accuracy of IF-FMEA approach.

For membership and non-membership functions, availability decreases to 0.0006% and 0.0020% respectively for spread ±15% to ±30%, and further decreases to 0.0127% and 0.0221% for spread ±30% to ±45%. Under risk assessment failure causes namely Storage tank (ST3), Valve (VL6), Transfer pump (TF8), Deaerator tank (DT11), High pressure heater and economiser (HP15), Boiler drum and super heater (BS22), Forced draft and Secondary air fan (FS25), Air preheater (AH29) and Furnace (FR31) with Intuitionistic Fuzzy Hybrid Weighted Euclidean Distance (IFHWED) based output scores – 0.8988, 0.9752, 0.9400, 0.8988, 0.9267, 1.1131, 1.0039, 0.8185, 1.0604 were identified as the most critical failure causes.

Reliability and risk analysis results derived from IFLT and IF-FMEA approaches respectively, to address the performance issues of BCPG is based on the quantitative and qualitative data collected from the industrial experts and maintenance log book. Moreover, to take care of hesitation in expert's knowledge, IF theory-based concept is incorporated so as to achieve more accuracy in analysis results. Reliability and risk analysis results together will be helpful in analyzing the performance characteristics and diagnosis of critical failure causes, which will minimize frequent failure in BCPG.

The framework will help plant managers to frame optimal maintenance policy in order to enhance the operational aspects of the considered unit. Moreover, the accurate and early detection of failure causes will also help managers to take prudent decision for smooth operation of plant.

The results obtained ensure continuous operation of plant by utilizing the bagasse as fuel in boiler and also mitigate the wastages of fuel. If this bagasse (green fuel) is not properly utilized, there remains a dependency on coal-based power plants to meet the power demand. The results obtained are useful for decreasing dependency on coal, and promoting bagasse as the green, and alternative fuel, the emission by burning of these fuels are not harmful for environment and thereby contribute in preventing the environment from harmful effect of GHGs gases.

IFLT approach has been implemented to develop reliability modeling equations of the BCPG unit, and furthermore to compute various reliability parameters for both membership and non-membership function. The ranking results of IF-FMEA are compared to IF-TOPSIS approach. Sensitivity analysis is done to check stability of proposed framework.

Identifying and prioritizing supply chain risk is significant from any product’s quality and reliability perspective. Under an input-process-output workflow, conventional risk prioritization uses a risk priority number (RPN) aligned to the risk analysis. Imprecise information coupled with a lack of dealing with hesitancy margins enlarges the scope, leading to improper assessment of risks. This significantly affects monitoring quality and performance. Against the backdrop, a methodology that identifies and prioritizes the operational supply chain risk factors signifies better risk assessment.

The study proposes a multi-criteria model for risk prioritization involving multiple decision-makers (DMs). The methodology offers a robust, hybrid system based on the Intuitionistic Fuzzy (IF) Set merged with the “Technique for Order Performance by Similarity to Ideal Solution.” The nature of the model is robust. The same is shown by applying fuzzy concepts under multi-criteria decision-making (MCDM) to prioritize the identified business risks for better assessment.

The proposed IF Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for risk prioritization model can improve the decisions within organizations that make up the chains, thus guaranteeing a “better quality in risk management.” Establishing an efficient representation of uncertain information related to traditional failure mode and effects analysis (FMEA) treatment involving multiple DMs means identifying potential risks in advance and providing better supply chain control.

In a company’s supply chain, blockchain allows data storage and transparent transmission of flows with traceability, privacy, security and transparency (Roy et al., 2022). They asserted that blockchain technology has great potential for traceability. Since risk assessment in supply chain operations can be treated as a traceability problem, further research is needed to use blockchain technologies. Lastly, issues like risk will be better assessed if predicted well; further research demands the suitability of applying predictive analysis on risk.

The study proposes a hybrid framework based on the generic risk assessment and MCDM methodologies under a fuzzy environment system. By this, the authors try to address the supply chain risk assessment and mitigation framework better than the conventional one. To the best of their knowledge, no study is found in existing literature attempting to explore the efficacy of the proposed hybrid approach over the traditional RPN system in prime sectors like steel (with production planning data). The validation experiment indicates the effectiveness of the results obtained from the proposed IF TOPSIS Approach to Risk Prioritization methodology is more practical and resembles the actual scenario compared to those obtained using the traditional RPN system (Kim et al., 2018; Kumar et al., 2018).

This study provides mathematical models to simulate the supply chain risk assessment, thus helping the manufacturer rank the risk level. In the end, the authors apply this model in a big-sized organization to validate its accuracy. The authors validate the proposed approach to an integrated steel plant impacting the production planning process. The model’s outcome substantially adds value to the current risk assessment and prioritization, significantly affecting better risk management quality.