A novel approach to risk analysis of automooring operations on autonomous vessels

The challenge for Product Recall Insurance companies and their policyholders to manually explore their customer product’s defects from online customer reviews (OCR) delays product risk analysis and product recall recovery processes. In today's product life cycle, product recall events happen almost every day and there is no practical method to automatically transfer the massive amount of valuable online customer reviews, such as defect information, performance issue, and serviceability feedback, to the Product Recall Insurance team as well as their policyholders’ engineers to analyze the product risk and evaluate their premium. This lack of early risk analysis and defect detection mechanism often increases the risks of a product recall and cost of claims for both the insurers and policyholder, potentially causing billions of dollars in economic loss, liability resulting from the bodily injury, and loss of company credibility. This research explores two different kinds of Recurrent Neural Network (RNN) models and one Latent Dirichlet Allocation (LDA) topic model to extract product defect information from OCRs. This research also proposes a novel approach, combined with RNN and LDA models, to provide the insurers and the policyholders with an early view of product defects. The proposed approach first employs the RNN models for sentiment analysis on customer reviews to identify negative reviews and reviews that mention product defects, then applies the LDA model to retrieve a summary of key defect insight words from these reviews. Results of this research show that both the insurers and the policyholders can discover early signs of potential defects and opportunities for improvement when using this novel approach on eight of the bestselling Amazon home furnishing products. This combined approach can locate the keywords of these products’ defects and issues that customers mentioned the most in their OCRs, which allows the insurers and the policyholders to take required mitigation actions earlier, proactively stop the diffusion of the detective products, and hence lower the cost of claim and premium.

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A Novel Approach to Exploration Risk Analysis

10.2118/23163-ms ◽

1991 ◽

Author(s):

R.J.R. Cairns

Keyword(s):

Risk Analysis ◽

Novel Approach ◽

Exploration Risk

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A Novel Approach to Ship Operational Risk Analysis Based on D-S Evidence Theory

10.1007/978-981-16-5188-5_52 ◽

2021 ◽

pp. 728-741

Author(s):

Tao Liu ◽

Yuanzi Zhou ◽

Junzhong Bao ◽

Xizhao Wang ◽

Pengfei Zhang

Keyword(s):

Risk Analysis ◽

Evidence Theory ◽

Operational Risk ◽

Novel Approach

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A New Tool for Prioritising the Risk of Failure Modes for Marine Machinery Systems

Volume 4A: Structures, Safety and Reliability ◽

10.1115/omae2014-23267 ◽

2014 ◽

Cited By ~ 4

Author(s):

Ikuobase Emovon ◽

Rosemary A. Norman ◽

Alan J. Murphy

Keyword(s):

Risk Analysis ◽

Failure Modes ◽

Evidence Theory ◽

Risk Priority Number ◽

Averaging Technique ◽

Risk Criteria ◽

Novel Approach ◽

Mode Effect ◽

Simple Average

Failure Mode Effect and Analysis (FMEA) is one of the most powerful techniques for performing risk analysis for marine machinery systems, with risk being quantified through evaluating Risk Priority Numbers (RPNs) for all failure modes of the systems. The RPN is traditionally evaluated as the product of three risk criteria; occurrence (O), severity (S) and Detection (D). FMEA has several limitations such as the challenge of aggregating experts’ risk criteria rating that may be imprecise or incomplete. In this paper some of the limitations in the conventional FMEA technique are addressed using two approaches; AVeraging technique integrated with conventional Risk Priority Number (AVRPN) and AVeraging technique integrated with TOPSIS (AVTOPSIS). Both proposed techniques use a novel approach simple average in aggregating imprecise experts’ risk criteria ratings. A case study illustrates the suitability of both techniques for use in risk prioritisation jointly or independently as the results generated by both techniques are very similar. Furthermore, the AVRPN technique has been applied to an example from the literature and it has been demonstrated to be both computationally simple and capable of producing results which almost completely match those generated by modified Dempster-Shafer evidence theory techniques.

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Novel approach for manufacturing supply chain risk analysis using fuzzy supply inoperability input-output model

Manufacturing Letters ◽

10.1016/j.mfglet.2017.03.001 ◽

2017 ◽

Vol 12 ◽

pp. 1-5 ◽

Cited By ~ 3

Author(s):

Mary Eloise Brosas ◽

Michelle Abigail Kilantang ◽

Noreen Bless Li ◽

Lanndon Ocampo ◽

Michael Angelo Promentilla ◽

...

Keyword(s):

Supply Chain ◽

Risk Analysis ◽

Input Output ◽

Supply Chain Risk ◽

Novel Approach ◽

Manufacturing Supply Chain

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