The (α, β)-Precise Estimates of MTBF and MTTR: Definition, Calculation, and Observation Time

Alavian, Pooya; Eun, Yongsoon; Liu, Kang; Meerkov, Semyon M.; Zhang, Liang

doi:10.1109/tase.2020.3017134

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…However, a simple mean often may not be a good approximation for heavy-tailed shapes that the TTR and TTA processes can follow. In this light, they can be posed as a distribution function, not a scalar, following, typically, a log-normal or exponential distribution [43]. The implication of Eq.…”

Section: Limitationsmentioning

confidence: 99%

“…In other words, the best, worst and regular analysts/working days are offsetting the estimation. Actually, the authors in [43] suggest measuring in the range of hundreds of samples to ensure the significance of MTTR estimate but, at the same time, they state that in practical cases this number may be in dozens rather than hundreds. In the case of our work, we have used a number of samples an order of magnitude above hundreds on our hands-on working experience with operations centers, this provides us with a significant estimate with narrow confidence intervals.…”

Section: Limitationsmentioning

confidence: 99%

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Workforce capacity planning for proactive troubleshooting in the Network Operations Center

2023

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Section: Limitationsmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Workforce capacity planning for proactive troubleshooting in the Network Operations Center

2023

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“…An additional filter to our search was the date of publication: for the most appropriate findings, only recent (since 2017) articles were processed. As a first step of the research, requirements and indicators were collected and evaluated from the literature [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43]. This input supported the authors to make AHP analyses with the use of which the importance of these components were calculated and consistency calculations carried out.…”

Section: Meta-analysismentioning

confidence: 99%

Selection of Production Reliability Indicators for Project Simulation Model

2022

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Due to technological enhancements, traditional, qualitative decision-making methods are usually replaced by data-driven decision-making even in smaller companies. Process simulation is one of these solutions, which can help companies avoid costly failures as well as evaluate positive or negative effects. The reason for this paper is twofold: first, authors conducted a Quality Function Deployment analysis to find the most vital reliability indicators in the field of production scheduling. The importance was acquired from the meta-analysis of papers published in major journals. The authors found 3 indicators to be the most important: mean time between failure (MTBF), mean repair time and mean downtime. The second part of the research is for the implementation of these indicators to the stochastic environment: possible means of application are proposed, confirming the finding with a case study in which 100 products must be produced. The database created from the simulation is analyzed in terms of major production KPIs, such as production quantity, total process time and efficiency of the production. The results of the study show that calculating with reliability issues in production during the negotiation of a production deadline supports business excellence.

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“…Untuk mengetahui MTBF dan MTTR, waktu kerja mesin perbaikan yang nyatanya tidak beraturan harus dihitung dan setelah itu dirata-rartakan untuk mengetahui nilai MTBF dan MTTR. [4] Terdapat tiga jenis distribusi yang dapat dipakai untuk menghitung nilai MTBF dan MTTR yaitu distribusi Normal, Eksponensial, dan Weibull. Distribusi Eksponensial digunakan apabila laju kerusakan komponen tidak terkait dengan umur pakai dan faktor lain dalam historis pemakaiannya, sedangkan jika laju kerusakannya tergantung pada umur pakainya maka distribusi yang digunakan adalah distribusi Normal dan Weibull.…”

Section: B Mtbf Dan Mttrunclassified

Analisis Penentuan Kebutuhan Suku Cadang Refueller di DPPU XYZ

Adelia¹,

Sutanto²

2022

sntem

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DPPU XYZ menggunakan refueller untuk menyalurkan Avtur ke konsumen. Terdapat salah satu refueller yang berhenti beroperasi selama beberapa bulan dikarenakan suku cadang refueller yang rusak tidak tersedia di gudang dan perbaikan harus menunggu sampai suku cadang tersedia. Ketidaktersediaan suku cadang akan berdampak pada pemanfaatan kinerja yang kurang maksimal dan dapat menghambat penyaluran Avtur. Reliability Centered Spares digunakan untuk mengetahui jumlah suku cadang kritis yang dibutuhkan dalam jangka waktu tertentu. Metode ini dihitung berdasarkan data kerusakan suatu komponen yang kemudian dijumlahkan menjadi Mean Time To Repair dan Mean Time Between Failure komponen. Metode poisson process digunakan untuk mengetahui hasil akhir dari persediaan suku cadang. Dengan menggunakan metode Reliability Centered Spares, dapat diketahui bahwa untuk memenuhi kebutuhan suku cadang kritis dari 8 refueller yaitu filter solar atas sebanyak 46 komponen, filter solar bawah sebanyak 56 komponen, filter oli sebanyak 30 komponen, dan filter udara sebanyak 30 komponen.

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The (α, β)-Precise Estimates of MTBF and MTTR: Definition, Calculation, and Observation Time

Cited by 9 publications

References 9 publications

Workforce capacity planning for proactive troubleshooting in the Network Operations Center

Workforce capacity planning for proactive troubleshooting in the Network Operations Center

Selection of Production Reliability Indicators for Project Simulation Model

Analisis Penentuan Kebutuhan Suku Cadang Refueller di DPPU XYZ

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