An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks
- Submitting institution
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Liverpool John Moores University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 1291
- Type
- D - Journal article
- DOI
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10.1016/j.tre.2019.03.011
- Title of journal
- Transportation Research Part E: Logistics and Transportation Review
- Article number
- -
- First page
- 222
- Volume
- 125
- Issue
- -
- ISSN
- 1366-5545
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2019
- URL
-
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
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B - LOOM
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Supported by China National Key Technologies Research & Development Program (2017YFC0804900, 2017YFC0804904) and EU H2020 project (RESET- 730888), this work pioneers the solution to tackling different types of risks in the same framework. It led to an ERC grant (TRUST-CoG-864724, €1.99m to LJMU, 2020-2025). Its methodological part led to the receipt of the QR2MSE Best Paper Award 2016 (over 300 papers) (Chairman, hzhuang@uestc.edu.cn). It forms a part of a keynote opening address in ICSMC2018 (Chairman, wangyuhong@nbu.edu.cn). It has helped COSCO to reduce its incident-related logistics costs by an estimated 10% (Tinggang Tu, Logistics Division Manager, dutg@cosco-logistics.com.cn).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -