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This study aims to develop an assessment methodology using a Bayesian network (BN) to predict the failure probability of oil tanker shipping firms.

This paper proposes a bankruptcy prediction model by applying the hybrid of logistic regression and Bayesian probabilistic networks.

The proposed model shows its potential of contributing to a powerful tool to predict financial bankruptcy of shipping operators, and provides important insights to the maritime community as to what performance measures should be taken to ensure the shipping companies’ financial soundness under dynamic environments.

The model and its associated variables can be expanded to include more factors for an in-depth analysis in future when the detailed information at firm level becomes available.

The results of this study can be implemented to oil tanker shipping firms as a prediction tool for bankruptcy rate.

Incorporating quantitative statistical measurement, the application of BN in financial risk management provides advantages to develop a powerful early warning system in shipping, which has unique characteristics such as capital intensive and mobile assets, possibly leading to catastrophic consequences.

Since the start of the current century, the world at large has experienced uncertainties as a result of climate change, terrorism threats and increasing economic upheaval. These uncertainties create non-classical risks for global seaborne container trade and liner shipping networks (LSNs). The purpose of this paper is to establish a novel risk-based resilience framework to measure the effectiveness of different recovery strategies for the disruptions in LSNs in a quantitative manner.

Based on a resilience loss triangle model, an indicator of resilience–cost ratio is designed to measure the performance of LSNs during recovery. Four recovery strategies are proposed to test the rationality and feasibility of the developed indicator in aiding decision-making of LSNs from a resilience perspective.

The analysis results reveal that the superiorities of different recovery strategies vary depending on both the structures of LSNs and the specific requirements during recovery. Moreover, optimizing the sequence of ports being recovered will improve the overall recovery efficiency of the investigated LSN.

As an exploratory research trying to enrich the risk-based resilience evaluation of LSNs from a complex network perspective, only two attributes (e.g. port scare and economy) are considered at the current stage when estimating the time needed to fully recover the whole LSN. In future research, more attributes from the industry may be identified and incorporated into the proposed model to further extend its ability and application scopes.

The findings will help to improve managerial understandings of recovery strategies to build more resilient LSNs. The proposed model has the capability to be tailored to tackle different types of risks in addition to the storm disaster condition.

The risk-based resilience framework and the resilience–cost ratio indicator are newly developed in this research. They can consider LSNs' structural resilience and the total costs that a recovery strategy needs to restore the whole system simultaneously.

Container shipping companies face various risks with different consequences that are required to be mitigated. Limited empirical research has been done on identifying and evaluating risk management strategies in shipping operations with different risk consequences. This paper aims to identify the appropriate risk mitigation strategies and evaluate the relative importance of these strategies.

Literature review and interviews were used to identify and validate the appropriate risk mitigation strategies in container shipping operations. A questionnaire with a Likert five-point scale was then conducted to rank the identified risk mitigation strategies in terms of their overall effectiveness. Top six important strategies were selected to evaluate their relative importance under three risk consequences (i.e. financial, reputation and safety and security incident related loss) through using another questionnaire with paired-comparison. Fuzzy analytic hierarchy process (AHP) was then conducted to analyse the paired-comparison questionnaire.

After conducting a systematic literature review and interviews, 18 mitigation strategies were identified. The results from the first questionnaire show that among the 18 strategies, the top three are “form alliances with other shipping companies”, “use more advanced infrastructures (hardware and software)” and “choose partners very carefully”. After conducting fuzzy AHP, the results show that shipping companies emphasize more on reducing the risk consequence of financial loss; and “form alliance with other shipping companies” is the most important risk mitigation strategy.

This paper evaluates the risk mitigation strategies against three risk consequences. Managers can benefit from the systematic identification of mitigation strategies, which shipping companies can consider for adoption to reduce the operational risk impact.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Effective risk management is vital for shipping container firms to combat different forms of risk. Use of a select number of relevant strategies can enable a cost-effective approach to the mitigation of risks associated with financial loss and issues relating to safety and security.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Failure mode and effect analysis (FMEA) is an analysis technique for identifying and eliminating known and/or potential failures and problems from products, processes or systems. Notwithstanding its diffusion, traditional FMEA has several limitations. Lately, scientific research has been focused on improving said limitations, yet the process is still ongoing. The purpose of this paper is to support developments in this area.

The paper improves the conventional FMEA by using the method of pairwise comparison to establish the relative importance of the input factors in risk priority number calculation, and Markov chains to calculate risk distributions in the long term.

The functioning and usefulness of the proposed methodology is demonstrated through an application to the construction industry, one of the world’s biggest industrial sectors, dogged by a high rate of work-related injuries and casualties.

Having demonstrated the applicability of the novel methodology to a real domain, the paper contributes to the process of overcoming traditional FMEA limitations.

Ports are the key elements of maritime transportation, which is crucial for world trade. Approximately 180 port facilities are located in Turkey. After 2007, 5 of the ports, which are formerly owned by Turkish Republic Railways Administration (TRRA), are privatized. The aim of the study is to evaluate the performance of these privatized ports by multi-criteria decision-making (MCDM) approach.

The application process is performed by a MCDM model. This model includes both criteria (dry bulk, liquid bulk, general cargo, container, RO-RO capacity, total port area, total berth, total berths length and depth) and alternatives (Mersin, Samsun, Bandirma, Iskenderun and Derince Ports). It determines the weights of the criteria by entropy and ranks the alternatives by ARAS and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods.

The results of entropy, ARAS and TOPSIS methods are compared. According to these results, “container” is the most important criteria while Mersin port has the best performance.

In the literature, most of the studies about this subject were analyzed by data envelopment analysis (DEA) and there are no studies had been taken into consideration ports that are owned by TRRA, in Turkey. Moreover, few of these studies used integrated MCDM models, and this is the first study that integrates entropy, ARAS and TOPSIS methods in this field.