Towards Effective Training for Process and Maritime Industries

AbstractWith the development of technological innovation, many industries such as process and maritime industries constitute more complex automated systems, dedicated operating conditions, multilevel interconnections, as well as human-machine and human-human interactions. The actions and decisions made by operators and seafarers affect the safety and productivity of these systems. It is well known that around 60-80% of the accidents are attributed to human error (directly or indirectly). Generally, each operator or seafarer is must undergo training before starting their real jobs. In recent decades, emphasis on operator training has increased – resulting in advance training simulators with several features like immersivity, stereoscopic sounds, hydraulics, and even use of different odors. However, the design of training methodology can have a significant impact on the skill acquisition of trainees. The growth of integration of technology in existing systems as well as newer systems is much higher than that of improvement in training methods. Unfortunately, there are few studies linking the training needs and the real-world demands on operators, revealing a significant research gap to fulfill.In socio-complex systems, it is necessary to consider various aspects in the training methodology, which can facilitate the operators/seafarers to handle normal and abnormal scenarios adequately. This paper provides a background of current training methods through analyzing the process- and maritime industries as illustrative examples, highlighting the limitations associated with each of different perspectives (technical, psychological and organizational) to propose an training syllabus that allows for learning by experience and interaction with scenarios of different complexity. It consists of a three-stage hierarchy with increasing demands concerning technical and relational complexity and time pressure. The training is centered on handling real-time operations with increasing complexity starting from basic components of the process, advancing to real-time operations, and reaching high technical and relational complexity that needs to be handled in situations with limited time and uncertainty in data. The challenges that arise in team-working tasks are also considered in the conceptualization of the training syllabus. The proposed training syllabus includes training content, objectives and performance evaluation criteria. The systematic methodology of performance evaluation will allow practitioners to obtain transparent, unbiased and consistent certification of trainees

Supply Chain Risk Management in the Container Liner Shipping Industry from a Strategic Point of View

One of the most significant current discussions in the container liner shipping industry (CLSI) is supply chain risk management (SCRM). In recent years, there has been an increasing interest in managing risk and reliability in the container supply chain from many viewpoints. This paper reviews the significant literature related to SCRM in the CLSI from a strategic point of view. By integrating the concept of the CLSI, the planning levels of container liner shipping and the concept of SCRM, questions have been raised about risk and uncertainty arising from the external environments (i.e. country-limited scope) and how can these factors influence the organisational reliability and capability of liner shipping operators (LSOs). Another question concerns how uncertain environments can influence the punctuality of containerships. So far, however, no research has been found that answered these questions which make further research is meaningful. For future research, this paper recommends an extensive assessment of a business environment-based risk and an evaluation of organizational reliability and capability of LSOs from the strategic point of view. Finally, it is worth mentioning that there is a research gap in both industry and academia on how to analyse and predict the punctuality of containerships (i.e. arrival and departure) under uncertain environments. Keywords: supply chain risk management, container liner shipping industry, business-environment based risk, organisational reliability and capability, punctuality

Business Environment-Based Risk Model for the Container Liner Shipping Industry

Container liner shipping is a risky industry. There are many unexpected risks and uncertainties in extended journey around the world which can be the result of external events such as political actions, economic situation and natural disasters. The impact of these external risks can be devastating. In this paper, a new business environment-based risk (BEBR) model for prioritising and assessing the external risk criteria in the CLSI, is developed. The term BEBR can be defined as a wider scope of external risks including political risks, economic risks, social risks and natural hazards that directly or indirectly influence the business performances in the CLSI. An analytic hierarchy process (AHP) approach is employed to prioritise the importance of risk criteria and is adapted into a deterministic weight factor in the context of risk impact level. So far, no study has been found that developed this model for the CLSI which highlights a significant research gap to be fulfilled. Based on the test case, the result has shown that economic risks are the most significant main criteria in the BEBR model, followed by political risks, natural hazards and social risks. For future research, this paper recommends a quantitative risk analysis for assessing the targeted or whole risk criteria in the BEBR model. Keywords: business environment-based risk (BEBR), container liner shipping industry (CLSI), analytic hierarchy process (AHP), prioritisation

An advanced risk analysis approach for container port safety evaluation

Risk analysis in seaports plays an increasingly important role in ensuring port operation reliability, maritime transportation safety and supply chain distribution resilience. However, the task is not straightforward given the challenges, including that port safety is affected by multiple factors related to design, installation, operation and maintenance and that traditional risk assessment methods such as quantitative risk analysis cannot sufficiently address uncertainty in failure data. This paper develops an advanced Failure Mode and Effects Analysis (FMEA) approach through incorporating Fuzzy Rule-Based Bayesian Networks (FRBN) to evaluate the criticality of the hazardous events (HEs) in a container terminal. The rational use of the Degrees of Belief (DoB) in a fuzzy rule base (FRB) facilitates the implementation of the new method in Container Terminal Risk Evaluation (CTRE) in practice. Compared to conventional FMEA methods, the new approach integrates FRB and BN in a complementary manner, in which the former provides a realistic and flexible way to describe input failure information while the latter allows easy updating of risk estimation results and facilitates real-time safety evaluation and dynamic risk-based decision support in container terminals. The proposed approach can also be tailored for wider application in other engineering and management systems, especially when instant risk ranking is required by the stakeholders to measure, predict and improve their system safety and reliability performance

Realising advanced risk-based port state control inspection using data-driven Bayesian networks

In the past decades, maritime transportation not only contributes to economic prosperity, but also renders many threats to the industry, causing huge casualties and losses. As a result, various maritime safety measures have been developed, including Port State Control (PSC) inspections. In this paper, we propose a data-driven Bayesian Network (BN) based approach to analyse risk factors influencing PSC inspections, and predict the probability of vessel detention. To do so, inspection data of bulk carriers in seven major European countries from 2005 to 2008 1 in Paris MoU is collected to identify the relevant risk factors. Meanwhile, the network structure is constructed via TAN learning and subsequently validated by sensitivity analysis. The results reveal two conclusions: first, the key risk factors influencing PSC inspections include number of deficiencies, type of inspection, Recognised Organisation (RO) and vessel age. Second, the model exploits a novel way to predict the detention probabilities under different situations, which effectively help port authorities to rationalise their inspection regulations as well as allocation of the resources. Further effort will be made to conduct contrastive analysis between ‘Pre-NIR’ period and ‘Post-NIR’ period to test the impact of NIR started in 2008. © 2018 Elsevier Lt

A risk assessment approach to improve the resilience of a seaport system using Bayesian networks

Over the years, many efforts have been focused on developing methods to design seaport systems, yet disruption still occur because of various human, technical and random natural events. Much of the available data to design these systems are highly uncertain and difficult to obtain due to the number of events with vague and imprecise parameters that need to be modelled. A systematic approach that handles both quantitative and qualitative data, as well as means of updating existing information when new knowledge becomes available is required. Resilience, which is the ability of complex systems to recover quickly after severe disruptions, has been recognised as an important characteristic of maritime operations. This paper presents a modelling approach that employs Bayesian belief networks to model various influencing variables in a seaport system. The use of Bayesian belief networks allows the influencing variables to be represented in a hierarchical structure for collaborative design and modelling of the system. Fuzzy Analytical Hierarchy Process (FAHP) is utilised to evaluate the relative influence of each influencing variable. It is envisaged that the proposed methodology could provide safety analysts with a flexible tool to implement strategies that would contribute to the resilience of maritime systems

How can the UK road system be adapted to the impacts posed by climate change? By creating a climate adaptation framework

This paper aims to analyse the impacts of climate change to the current and predicted future situations of road transportation in the UK and evaluate the corresponding adaptation plans to cope with them. A conceptual framework of long-term adaptation planning for climate change in road systems is proposed to ensure the resilience and sustainability of road transport systems under various climate risks such as flooding and increased temperature. To do so, an advanced Fuzzy Bayesian Reasoning (FBR) model is first employed to evaluate the climate risks in the UK road transport networks. This modelling approach can tackle the high uncertainty in risk data and thus facilitate the development of the climate adaptation framework and its application in the UK road sector. To examine the feasibility of this model, a nationwide survey is conducted among the stakeholders to analyse the climate risks, in terms of the timeframe of climate threats, the likelihood of occurrence, the severity of consequences, and infrastructure resilience. From the modelling perspective, this work brings novelty by expanding the risk attribute “the severity of consequence” into three sub-attributes including economic loss, damage to the environment, and injuries and/or loss of life. It advances the-state-of-the-art technique in the current relevant literature from a single to multiple tier climate risk modelling structure. Secondly, an Evidential Reasoning (ER) approach is used to prioritise the best adaptation measure(s) by considering both the risk analysis results from the FBR and the implementation costs simultaneously. The main new contributions of this part lie in the rich raw data collected from the real world to provide useful practical insights for achieving road resilience when facing increasing climate risk challenges. During this process, a qualitative analysis of several national reports regarding the impacts posed by climate change, risk assessment and adaptation measures in the UK road sector is conducted for the relevant decision data (i.e. risk and cost). It is also supplemented by an in-depth interview with a senior planner from Highways England. The findings provide road planners and decision makers with useful insights on identification and prioritisation of climate threats as well as selection of cost-effective climate adaptation measures to rationalise adaptation planning. © 2019 Elsevier Lt

Exploring the effects of lysozyme dietary supplementation on laying hens: performance, egg quality, and immune response

An experiment was conducted to evaluate the dietary supplementation with lysozyme's impacts on laying performance, egg quality, biochemical analysis, body immunity, and intestinal morphology. A total of 720 Jingfen No. 1 laying hens (53 weeks old) were randomly assigned into five groups, with six replicates in each group and 24 hens per replicate. The basal diet was administered to the laying hens in the control group, and it was supplemented with 100, 200, 300, or 400 mg/kg of lysozyme (purity of 10% and an enzyme activity of 3,110 U/mg) for other groups. The preliminary observation of the laying rate lasted for 4 weeks, and the experimental period lasted for 8 weeks. The findings demonstrated that lysozyme might enhance production performance by lowering the rate of sand-shelled eggs (P < 0.05), particularly 200 and 300 mg/kg compared with the control group. Lysozyme did not show any negative effect on egg quality or the health of laying hens (P > 0.05). Lysozyme administration in the diet could improve intestinal morphology, immune efficiency, and nutritional digestibility in laying hens when compared with the control group (P < 0.05). These observations showed that lysozyme is safe to use as a feed supplement for the production of laying hens. Dietary supplementation with 200 to 300 mg/kg lysozyme should be suggested to farmers as a proper level of feed additive in laying hens breeding