Developing a new BWM-based GMAFMA approach for evaluation of potential risks and failure modes in production processes

Kolagar, Milad; Hosseini, Seyed Mohammad Hassan; Felegari, Ramin

doi:10.1108/ijqrm-09-2018-0230

Cited by 5 publications

(1 citation statement)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the BWM process, experts were asked to select the best (most important) and worst (least important) criteria from each set of criteria: they were then asked to perform pairwise comparisons between the criteria using a number between 1 and 9 [ 25 ]. Also, this method used an equation to calculate the consistency ratio for verifying the validity of comparisons [ 26 ]. The BWM technique can be applied to efficiently and reliably solve multi-criteria problems [ 27 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement

Zhang

Yan

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

Background As the world’s largest coal producer, China was accounted for about 46% of global coal production. Among present coal mining risks, methane gas (called gas in this paper) explosion or ignition in an underground mine remains ever-present. Although many techniques have been used, gas accidents associated with the complex elements of underground gassy mines need more robust monitoring or warning systems to identify risks. This paper aimed to determine which single method between the PCA and Entropy methods better establishes a responsive weighted indexing measurement to improve coal mining safety. Methods Qualitative and quantitative mixed research methodologies were adopted for this research, including analysis of two case studies, correlation analysis, and comparative analysis. The literature reviewed the most-used multi-criteria decision making (MCDM) methods, including subjective methods and objective methods. The advantages and disadvantages of each MCDM method were briefly discussed. One more round literature review was conducted to search publications between 2017 and 2019 in CNKI. Followed two case studies, correlation analysis and comparative analysis were then conducted. Research ethics was approved by the Shanxi Coking Coal Group Research Committee. Results The literature searched a total of 25,831publications and found that the PCA method was the predominant method adopted, and the Entropy method was the second most widely adopted method. Two weighting methods were compared using two case studies. For the comparative analysis of Case Study 1, the PCA method appeared to be more responsive than the Entropy. For Case Study 2, the Entropy method is more responsive than the PCA. As a result, both methods were adopted for different cases in the case study mine and finally deployed for user acceptance testing on 5 November 2020. Conclusions The findings and suggestions were provided as further scopes for further research. This research indicated that no single method could be adopted as the better option for establishing indexing measurement in all cases. The practical implication suggests that comparative analysis should always be conducted on each case and determine the appropriate weighting method to the relevant case. This research recommended that the PCA method was a dimension reduction technique that could be handy for identifying the critical variables or factors and effectively used in hazard, risk, and emergency assessment. The PCA method might also be well-applied for developing predicting and forecasting systems as it was sensitive to outliers. The Entropy method might be suitable for all the cases requiring the MCDM. There is also a need to conduct further research to probe the causal reasons why the PCA and Entropy methods were applied to each case and not the other way round. This research found that the Entropy method provides higher accuracy than the PCA method. This research also found that the Entropy method demonstrated to assess the weights of the higher dimension dataset was higher sensitivity than the lower dimensions. Finally, the comprehensive analysis indicates a need to explore a more responsive method for establishing a weighted indexing measurement for warning applications in hazard, risk, and emergency assessments.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement

Zhang

Yan

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

show abstract

A Modified Risk Prioritization Approach Using Best–Worst Method

Gül

Yücesan

Çelik

2021

Industrial Ecology and Environmental Management

View full text Add to dashboard Cite

A holistic FMEA approach by fuzzy-based Bayesian network and best–worst method

Yücesan

Gül

Çelik

2021

Complex Intell. Syst.

View full text Add to dashboard Cite

Failure mode and effect analysis (FMEA) is a risk analysis tool widely used in the manufacturing industry. However, traditional FMEA has limitations such as the inability to deal with uncertain failure data including subjective evaluations of experts, the absence of weight values of risk parameters, and not considering the conditionality between failure events. In this paper, we propose a holistic FMEA to overcome these limitations. The proposed approach uses the fuzzy best–worst (FBWM) method in weighting three risk parameters of FMEA, which are severity (S), occurrence (O), and detection (D), and to find the preference values of the failure modes according to parameters S and D. On the other side, it uses the fuzzy Bayesian network (FBN) to determine occurrence probabilities of the failure modes. Experts use a procedure using linguistic variables whose corresponding values are expressed in trapezoidal fuzzy numbers, and determine the preference values of the failure modes according to parameter O in the constructed BN. Thus, the FBN including expert judgments and fuzzy set theory addresses uncertainty in failure data and includes a robust probabilistic risk analysis logic to capture the dependence between failure events. As a demonstration of the approach, a case study was conducted in an industrial kitchen equipment manufacturing facility. The results of the approach have also been compared with existed methods demonstrating its robustness.

show abstract

Developing a new BWM-based GMAFMA approach for evaluation of potential risks and failure modes in production processes

Cited by 5 publications

References 54 publications

A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement

A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement

A Modified Risk Prioritization Approach Using Best–Worst Method

A holistic FMEA approach by fuzzy-based Bayesian network and best–worst method

Contact Info

Product

Resources

About