A model based on FMEA and Fuzzy TOPSIS for risk prioritization in industrial processes

Magalhães, Wauires Ribeiro de; Lima, Francisco Rodrigues

doi:10.1590/1806-9649-2020v28e5535

Cited by 6 publications

(5 citation statements)

References 23 publications

(52 reference statements)

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“…The TOPSIS method was used to decide the favorable and unfavorable ideal options from the decision matrix. Hence, this is one of the apt methods for application in risk assessment problems, the subject of this research (Magalhães and Lima Junior, 2021).…”

Section: Topsis Methodsmentioning

confidence: 99%

“…Kushwaha et al (2020) presented an application of IF-TOPSIS over IF-FMEA to evaluate the risks of cutting systems in the sugar mill industry. Magalhães and Lima Junior (2021) developed a model based on FMEA and fuzzy TOPSIS to prioritize risks in an industrial setup.…”

Section: Literature Reviewmentioning

confidence: 99%

“…We now describe the sequential steps used for the TOPSIS method (Magalhães and Lima Junior, 2021; Jozi and Majd, 2014).…”

Section: Proposed Systemmentioning

confidence: 99%

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Supply chain risk prioritization: a multi-criteria based Intuitionistic Fuzzy TOPSIS approach

Mukherjee,

De,

Roy

2024

IJQRM

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Purpose 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.Design/methodology/approach 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.Findings 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.Research limitations/implications 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.Practical implications 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).Originality/value 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.

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Section: Topsis Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Supply chain risk prioritization: a multi-criteria based Intuitionistic Fuzzy TOPSIS approach

Mukherjee,

De,

Roy

2024

IJQRM

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“…Despite the widespread use of FMEA for more than 50 years, this method still has certain limitations, which contributes to the development of new versions by combining it with other techniques (Magalhães and Lima Junior, 2021). One of these limitations lies in the use of deterministic numerical values that do not allow the quantification of uncertain or imprecise measurements inherent in the risk assessment process.…”

Section: Failure Mode and Effects Analysis -Fmeamentioning

confidence: 99%

“…The time-consuming and financial cost of the analysis in the case of systems that are composed of many components and contain many functions, or if the analysis is used in the organization for the first time in a complex way of the system. Authors Magalhães and Lima Junior (2021) in their publication provide a proposal for the application of FMEA according to the three steps listed in Tab. 2.…”

Section: Failure Mode and Effects Analysis -Fmeamentioning

confidence: 99%

Risk Assessment Using the PFDA-FMEA Integrated Method

Cicka

Turisová

Čičková³

2022

QIP Journal

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Purpose: The paper aims to introduce risk assessment in new product development as an important activity for a successful new product launch. A practical example is presented to demonstrate the integration of tools Failure Mode and Effect Analysis (FMEA) and Pythagorean Fuzzy Dimensional Analysis (PFDA) at new product development process, which is a machined component. Methodology/Approach: Individual steps for creating a case study were carried out: create a Subject Matter Expert (SME) team, identify product failure modes, use linguistic values to assess the FMEA, compute and obtain the PFDA-FMEA and determine the product failure modes ranking. Findings: Minimized uncertainty in the final evaluation of the FMEA and improvement in the decision-making process based on the risks already identified in the new product development process. Research Limitation/Implication: The PFDA-FMEA method was based on the risk assessment of a machined part development process. Nevertheless, this method can be used for application in many other areas of industry that require high precision in risk analysis. Originality/Value of paper: The aim of this paper is to reveal a new integrated method in which FMEA, Pythagorean Fuzzy Sets (PFS) and Dimensional Analysis (DA) are coherent in one model.

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Design for manufacturing(DFM): a sustainable approach to drive the design process from suitability to low cost

Moeeni

Javadi

Raissi

2022

Int J Interact Des Manuf

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A model based on FMEA and Fuzzy TOPSIS for risk prioritization in industrial processes

Cited by 6 publications

References 23 publications

Supply chain risk prioritization: a multi-criteria based Intuitionistic Fuzzy TOPSIS approach

Supply chain risk prioritization: a multi-criteria based Intuitionistic Fuzzy TOPSIS approach

Risk Assessment Using the PFDA-FMEA Integrated Method

Design for manufacturing(DFM): a sustainable approach to drive the design process from suitability to low cost

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