Fuzzy FMEA application to improve decision-making process in an emergency department

Chanamool, Nalinee; Naenna, Thanakorn

doi:10.1016/j.asoc.2016.01.007

Cited by 175 publications

(86 citation statements)

References 29 publications

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“…To further demonstrate the advantages of the proposed FMEA method, we conduct a comparative analysis with the three related FMEA methods, ie, the traditional FMEA, 28 fuzzy FMEA, 47 and the exponential FMEA. 25 The comparison results of the 13 failure modes are given in Table 9.…”

Section: Comparison and Discussionmentioning

confidence: 99%

An interval probability‐based FMEA model for risk assessment: A real‐world case

Ouyang

Zheng

Zhu

et al. 2019

Quality & Reliability Eng

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Failure mode and effect analysis (FMEA) is an effective quality tool to eliminate the risks and enhance the stability and safety in the fields of manufacturing and service industry. Nevertheless, the conventional FMEA has been criticized for its drawbacks in the evaluation process of risk factors or the determination of risk priority number (RPN), which may lead to inaccurate evaluation results. Therefore, in this paper, we develop a novel FMEA method based on rough set and interval probability theories. The rough set theory is adopted to manipulate the subjectivity and uncertainty of experts' assessment and convert the evaluation values of risk factors into interval numbers. Meanwhile, the interval exponential RPN (ERPN) is used to replace the traditional RPN due to its superior properties, eg, solving the problems of duplicate numbers and discontinuity of RPN values. Furthermore, an interval probability comparison method is proposed to rank the risk priority of each failure mode for avoiding the information loss in the calculation process of RPN. Finally, a real case study is presented, and the comparison analysis among different FMEA methods is conducted to demonstrate the reliability and effectiveness of the proposed FMEA method.

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Section: Comparison and Discussionmentioning

confidence: 99%

An interval probability‐based FMEA model for risk assessment: A real‐world case

Ouyang

Zheng

Zhu

et al. 2019

Quality & Reliability Eng

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show abstract

“…This has been one of the main drivers for developing hybrid models. Fuzzy theory has proven to be effective and efficient in handling these types of uncertainties [14,22,[24][25][26][27].…”

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confidence: 99%

A Fuzzy-Based Holistic Approach for Supply Chain Risk Assessment and Aggregation Considering Risk Interdependencies

et al. 2019

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Supply chain risk management requires dealing with uncertainty, interrelations, and subjectivity inherent in the risk assessment process. This paper proposes a holistic approach for risk management that considers the impact on multiple performance objectives, the relation between risk agents, and the risk event interdependencies. An aggregated risk score is proposed to capture the cascading effects of common risk triggers and quantify the aggregated score by risk agent and objective. The approach also uses fuzzy logic to allow for the treatment of vague and ambiguity data as input parameters to the model from different domains and scales, according to knowledge and criteria nature. The integration of the balanced scorecard tool improves the analysis and prioritization of mitigation strategies in decision-making, both by risk agent and by strategic objective. A case study of a telecommunication company is presented to illustrate the applicability of the proposed approach.

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“…Zhang [11] presented integration 2 Journal of Quality and Reliability Engineering of both subjective weights and objective weights to avoid failure modes from being underestimated or overestimated based on fuzzy TOPSIS to get the closeness coefficient for each failure mode. Chanamool and Naenna [12] highlight the importance of fuzzy FMEA for prioritization and assessment of failures that likely occur in the working process of an emergency department of hospitals. Liu et al [13] presented a novel approach for FMEA based on combination weighting and fuzzy VIKOR method where integration of fuzzy analytic hierarchy process (AHP) and entropy method is applied for risk factor weighting in this proposed approach to deal with the uncertainty and vagueness from humans' subjective perception and experience in risk evaluation process.…”

Section: Introductionmentioning

confidence: 99%

Multicriteria FMECA Based Decision-Making for Aluminium Wire Process Rolling Mill through COPRAS-G

Pancholi

Bhatt²

2016

Journal of Quality and Reliability Engineering

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This paper presents a multifactor decision-making approach based on "grey-complex proportional assessment (COPRAS-G) method" in a view to overcome the limitations of Failure Mode Effect and Criticality Analysis (FMECA). In this model, the scores against each failure mode are expressed in grey number instead of crisp values to evaluate the criticalities of the failure modes without uncertainty. The suggested study is carried out to identify the weights of major failure causes for bearings, gears, and shafts of aluminium wire rolling mill plant. The primary findings of the paper are that sudden impact on the rolls seems to be most critical failure cause and loss of power seems to be least critical failure cause. It is suggested to modify the current control practices with proper maintenance strategy based on achieved maintainability criticality index (MCI) for different failure causes. The outcome of study will be helpful in deriving optimized maintenance plan to maximize the performance of process industry.

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Fuzzy FMEA application to improve decision-making process in an emergency department

Cited by 175 publications

References 29 publications

An interval probability‐based FMEA model for risk assessment: A real‐world case

An interval probability‐based FMEA model for risk assessment: A real‐world case

A Fuzzy-Based Holistic Approach for Supply Chain Risk Assessment and Aggregation Considering Risk Interdependencies

Multicriteria FMECA Based Decision-Making for Aluminium Wire Process Rolling Mill through COPRAS-G

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