Strategic Risk and Reliability Assessment in the Container Liner Shipping Industry Under High Uncertainties
type: CreativeWork
description: The container liner shipping industry (CLSI) can be defined as one consisting of a fleet of vessels that provides a fixed service at regular intervals between ports of call. It is noteworthy that the CLSI is remarkably acting as an artery in making contributions to the growth of the global economy. However, in an era of unprecedented global changes, the CLSI faces a variety of internal and external risks. Moreover, the reliability and capability of liner shipping operators (LSOs) vary under different environmental conditions. Consequently, it is important for LSOs to ensure that the safety and reliability of their internal operations as well as external environments through proactive assessment of their reliability and capability are intact. The literature indicates that disruptive events have been assessed and investigated by many researchers and practitioners whilst the root causes arising from external risks have not yet been fully identified. The aim of this research was to develop integrated frameworks for assessing risk and reliability in the CLSI under high uncertainties. As a result, three interlocking levels of analysis have been highlighted in this research: 1) business environment-based risk (BEBR), 2) organisational reliability and capability (ORC) of LSOs, and 3) punctuality of containerships. To achieve the aim, firstly, this research employed a combination of different decision-making methods (i.e. Analytic Hierarchy Process (AHP), Fuzzy Set Theory (FST) and Evidential Reasoning (ER)) for the assessment of the BEBR. The research outcomes are providing LSOs with a powerful decision-making tool to assess the risk value of a country prior to investment and strategic decisions. In addition, LSOs are also able to regularly assess the overall level of existing BEBR in a host country prior to development of mitigation strategies that can help to minimise financial losses. Secondly, this research employs the Fuzzy Bayesian Belief Network (FBBN) method for eva…
abstract: The container liner shipping industry (CLSI) can be defined as one consisting of a fleet of vessels that provides a fixed service at regular intervals between ports of call. It is noteworthy that the CLSI is remarkably acting as an artery in making contributions to the growth of the global economy. However, in an era of unprecedented global changes, the CLSI faces a variety of internal and external risks. Moreover, the reliability and capability of liner shipping operators (LSOs) vary under different environmental conditions. Consequently, it is important for LSOs to ensure that the safety and reliability of their internal operations as well as external environments through proactive assessment of their reliability and capability are intact. The literature indicates that disruptive events have been assessed and investigated by many researchers and practitioners whilst the root causes arising from external risks have not yet been fully identified. The aim of this research was to develop integrated frameworks for assessing risk and reliability in the CLSI under high uncertainties. As a result, three interlocking levels of analysis have been highlighted in this research: 1) business environment-based risk (BEBR), 2) organisational reliability and capability (ORC) of LSOs, and 3) punctuality of containerships. To achieve the aim, firstly, this research employed a combination of different decision-making methods (i.e. Analytic Hierarchy Process (AHP), Fuzzy Set Theory (FST) and Evidential Reasoning (ER)) for the assessment of the BEBR. The research outcomes are providing LSOs with a powerful decision-making tool to assess the risk value of a country prior to investment and strategic decisions. In addition, LSOs are also able to regularly assess the overall level of existing BEBR in a host country prior to development of mitigation strategies that can help to minimise financial losses. Secondly, this research employs the Fuzzy Bayesian Belief Network (FBBN) method for eva…
author: Salleh, NHM
keywords: HE Transportation and Communications
date published: 2015-06
date modified: 2026-07-24T06:29:57Z
provider: Liverpool Jon Moores University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Liverpool John Moores University

PropertyValue
property ID: DOI
value: 10.24377/LJMU.t.00004429