Salleh, NHM (2015) Strategic Risk and Reliability Assessment in the Container Liner Shipping Industry Under High Uncertainties. Doctoral thesis, Liverpool John Moores University.

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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 evaluating the ORC of LSOs. By exploiting the proposed FBBN model, LSOs are able to conduct a self-evaluation of their ORC prior to the selection of a strategy for enhancing their competitive advantages in the CLSI. A significant concern in container liner shipping operations is the punctuality of containerships. Therefore, thirdly, this research concentrated on analysing and predicting the arrival punctuality of a liner vessel under dynamic environments by employing a combination of Fuzzy Rule-Base (FRB) and FBBN methods. Finally, a probabilistic model for analysing and predicting the departure punctuality of a liner vessel was generated. Accordingly, from the outcomes of this research LSOs are able to forecast their vessels’ arrival and departure punctuality and, further, tactical strategies can be implemented if a vessel is expected to be delayed. In addition, both arrival and departure punctuality models are capable of helping academic researchers and industrial practitioners to comprehend the influence of uncertain environments on the service punctuality. In order to demonstrate the practicability of the proposed methodologies and models, several real test cases were conducted by choosing the Malaysian maritime industry as a focus of study. The results obtained from these test cases have provided useful information for recommending preventive measures, improvement strategies and tactical solutions. The frameworks and models that have been proposed in this research for assessing risk and reliability of the CLSI will provide managerial insights for modelling and assessing complex systems dealing with both quantitative and qualitative criteria in a rational, reliable and transparent manner. In addition, these models have been developed in a generic sense so that they can be tailored for application in other industrial sectors.

Item Type:	Thesis (Doctoral)
Subjects:	H Social Sciences > HE Transportation and Communications
Divisions:	Maritime & Mechanical Engineering (merged with Engineering 10 Aug 20)
Date Deposited:	28 Oct 2016 13:34
Last Modified:	03 Sep 2021 23:26
DOI or ID number:	10.24377/LJMU.t.00004429
Supervisors:	Riahi, R
URI:	https://researchonline.ljmu.ac.uk/id/eprint/4429

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