Analysis of Determining Critical Components Using Fmeca Method in Seawater Pump of Diesel Generator Caterpillar 3412

Hasan, Saiful; Sahiri, Erpan; Suprayitno, Suprayitno; Hartanto, Endin Tri

doi:10.37875/asro.v10i3.170

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…The criticality matrix referred to in this study is a failure mode categorized as High in the rating of risk table based on the results of risk matrix analysis in the FMEA method [14]. The high category in the Risk Matrix has an average level of frequency of occurrence and a higher level of effects caused by impacts compared to others [36]. The following is the result of the matrix risk analysis on each failure mode presented in the table below.…”

Section: Criticality Matrixmentioning

confidence: 99%

Pay Later Risk Management: A Review of FMECA and Potential Customer Prediction Frameworks Through the Application of Machine Learning

Nugraha Adz Zikri,

Suwarningsih

2023

International Journal of Advances in Data and Information Syste

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The development of technology continues to develop and gradually change the way people buy such as on online shopping sites. The increase in internet use, especially in the use of E Commerce, has given birth to great potential in the market, especially in Indonesia. These changes prompted the birth of various payment methods. One of them is Pay Later. 27% of the 3560 samples decided to use Pay Later with all the conveniences offered. However, the development of Pay Later is not synchronized with good risk management. The use of Pay Later, which is not targeted at the right consumers, causes PT. XYZ suffered losses due to 22.37% of users defaulting on Pay Later installments. The purpose of this study is to reduce Pay Later default users by answering what factors cause consumers to default. To support this study, the authors used FMECA, Cause Effect Diagrams and conducted tests using Machine Learning to improve company efficiency. Through critical matrix analysis, the author gets 3 priority failure modes, Users default, users disappear, and users experience payment delays. In solving the problems in this study, the authors provide recommendations in the form of a new framework in the form of analyzing the best Pay Later offers by analyzing consumer behavior patterns in an E Commerce by utilizing Machine Learning. However, future research will need to be conducted correlation analysis and static testing in testing attribute correlation before testing algorithms when building machine learning models. The authors also suggested comparing using other methods to improve risk management in this study.

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Section: Criticality Matrixmentioning

confidence: 99%