Risk Management Analysis Using FMECA and ANP Methods in the Supply Chain of Wooden Toy Industry

Tanjung, W. N.; Atikah, S. A.; Hidayat, Syarif; Ripmiatin, Endang; Asti, S. S.; Khodijah, R. S.

doi:10.1088/1757-899x/528/1/012007

Cited by 6 publications

(4 citation statements)

References 1 publication

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“…The Risk of Supply Chain Disruption represents the failure to meet demand for orders. Non-compliance may be related to a lack of suppliers, inputs, raw materials, and others [11,16,20,25,26,43,63,71,72,82,[84][85][86]112,116,122].…”

Section: Organizational Risksmentioning

confidence: 99%

Development of a Theoretical Model for Digital Risks Arising from the Implementation of Industry 4.0 (TMR-I4.0)

dos Santos Filho,

de Resende,

Pontes

2024

Future Internet

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This study aims to develop a theoretical model for digital risks arising from implementing Industry 4.0 (represented by the acronym TMR-I4.0). A systematic literature review was initially conducted using the Methodi Ordinatio methodology to map the principal dimensions and digital risks associated with Industry 4.0 in order to achieve this objective. After completing the nine steps of Methodi, a bibliographic portfolio with 118 articles was obtained. These articles were then subjected to content analysis using QSR Nvivo® version 10 software to categorize digital risks. The analysis resulted in the identification of 9 dimensions and 43 digital risks. The categorization of these risks allowed the construction of maps showing the digital risks and their impacts resulting from the implementation of Industry 4.0. This study advances the literature by proposing a comprehensive categorization of digital risks associated with Industry 4.0, which resulted from an exhaustive literature review. At the conclusion of the study, based on the proposed Theoretical Risk Model for Digital Risks arising from the implementation of Industry 4.0, a research agenda for future studies will be proposed, enabling other researchers to further explore the landscape of digital risks in Industry 4.0.

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Section: Organizational Risksmentioning

confidence: 99%

Development of a Theoretical Model for Digital Risks Arising from the Implementation of Industry 4.0 (TMR-I4.0)

dos Santos Filho,

de Resende,

Pontes

2024

Future Internet

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“…After knowing the failure mode, the cause of failure, and the effect of failure then the process continues by finding the RPN value of each component. After obtaining the next RPN value, ranking and labeling the risks of each component using the risk level and critically score (Tanjung et al, 2019). All components that have a risk level of "Unaccepted" and critically level "Very critical" will be included in the list of critical components.…”

Section: Condenser Pumpmentioning

confidence: 99%

Risk and Reliability Improvement Analysis of Boiler System Using the Failure Mode Effect Analysis & Critical Analysis (FMECA) Method

2023

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Electrical energy is a vital source of energy for human life. Currently, various power plants can provide electrical energy, one of which is the Steam Power Plant (PLTU). A PLTU must carry out operational tasks repeatedly and continuously to meet electricity needs. Based on historical data for PT. PJB Service Kendari for the last three years, there has been an increasing trend of downtime and derating at PLTU Nii Tanasa Kendari in the 2019–2021 timeframe. The boiler system is one of the systems that often experiences disturbances. FMECA is required to carry out a risk analysis to find out in detail the causes, effects, impacts, and ways of mitigating risks, as well as sorting and labeling risks for critical components based on the RPN value. The boiler itself is the main component and is supported by other components in the steam-water process. The reliability value is determined after the critical component with a high level of risk is marked “Not Accepted”. The findings of the risk and reliability analysis will be used to mitigate risks and increase the value of the reliability of critical components. The results of reliability calculations based on MTTF found that 9 out of 13 critical components had a reliability value below 50%; increasing reliability values could be achieved using preventive maintenance (Rm(t)), and maintaining reliability values above 60% could be achieved using periodic component replacement (R(t-nT)) based on the MTTR data of each critical component.

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“…On the other hand, researchers have used ranking methods to prioritize failure modes for decision-making. One approach is to improve the RPN, such as with weighted RPN (Tanjung et al, 2019) or fuzzy measure and integral (Liu, Deng, & Jiang, 2017). Other studies have introduced new indices like the Maintainability Criticality Index (MCI) with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Preference Section Index (PSI) to optimize maintenance planning (Pancholi & Bhatt,1 4th Asia Pacific Conference of the Prognostics andHealth Management, Tokyo, Japan, September 11 -14, 2023 R02-14 2017).…”

Section: Introductionmentioning

confidence: 99%

“…append triplet [i (priority rank), p (failure mode), q (criterion index)] to priority ranking list L. 24: end while 25: return the priority ranking list L. Singh et al, 2019;Khorshidi et al, 2016;Catelani et al, 2021;Zhai et al, 2021;Catelani et al, 2011;Yssaad et al, 2012;Khalil et al, 2014;Saraswati et al, 2014;El-Dogdog et al, 2016;Silva et al, 2020;Bezerra et al, 2020;Scriboni, 2020;Royer et al, 2020;Pancholi & Bhatt, 2017;Dumnić et al, 2020;Nursanti et al, 2018;Ciani et al, 2019;Tanjung et al, 2019;Mohanty et al, 2021). Each instance is identified by a specific failure mode.…”

mentioning

confidence: 99%

Comparative Study of K-Means Clustering and a Novel Ranking Algorithm for Risk Priority Number Analysis in FMECA

Cheng,

Cho,

Tan

et al. 2023

PHMAP_CONF

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Failure mode, effects, and criticality analysis (FMECA) has become a fundamental tool for identifying critical failure modes and prioritizing maintenance activities. As part of the analysis, the risk priority number (RPN), a numeric assessment of the risk, has received much attention as it is computed using severity (S), occurrence (O), and detectability (D), which serve as the main criteria for criticality analysis in many practical FMECA cases. In this paper, we assemble and present a data set containing RPN evaluations from 20 real-world cases. We then apply K-Means clustering to identify the most critical failure modes and propose a novel ranking algorithm that prioritizes mitigation actions based on specific criteria for each failure mode. Our experimental results suggest that both clustering and ranking methods can provide effective prioritization for critical failure modes under given assumptions, while our novel ranking algorithm can adapt to general scenarios and provide more accurate prioritization that can help develop effective maintenance strategies to minimize equipment failure risk and optimize maintenance costs.

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Risk Management Analysis Using FMECA and ANP Methods in the Supply Chain of Wooden Toy Industry

Cited by 6 publications

References 1 publication

Development of a Theoretical Model for Digital Risks Arising from the Implementation of Industry 4.0 (TMR-I4.0)

Development of a Theoretical Model for Digital Risks Arising from the Implementation of Industry 4.0 (TMR-I4.0)

Risk and Reliability Improvement Analysis of Boiler System Using the Failure Mode Effect Analysis & Critical Analysis (FMECA) Method

Comparative Study of K-Means Clustering and a Novel Ranking Algorithm for Risk Priority Number Analysis in FMECA

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