What is it about?

To be competitive, transportation systems must be able to analyze and to evaluate, in real-time, critical differences between the short-term planned actions and the actual performed actions generating states of undesirable or unacceptable risk. We propose a method for monitoring the dynamic evolution of risk in the operational flow of a transportation system. It consists in an approach assessing risk associated to delays affecting the transportation operations. We use the FMEA (Failure Modes and Effects Analysis) around failure scenarios rather than failure modes, and we evaluate risk using probability and cost. A scenario probability is estimated dynamically in discrete points based on events occurrences during the process execution. The proposed approach useful to transportation managers is based on consistent and meaningful risk evaluation criteria to facilitate cost-based decisions during execution. The implementation of this method is performed by monitoring a container delivery process facing delays risks.

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Why is it important?

The proposed approach is an important contribution in terms of introducing a new concept of dynamic risk assessment strategies. The results clearly provided an insight into realizing the importance of adopting such an approach as implementation of dynamic risk assessment strategies without performing a rigorous analysis would lead to inefficient outcomes. The process can also be used to dynamically assess various types of risk other than that of delay such as, for instance, a production machine breaking down, some changes in demand and supply errors. The presented technique will help researchers and practitioners in developing and using efficient models of dynamic assessing transportation system risks respectively.

Perspectives

In case of a huge delivery network with several possible paths to travel by the vehicles, it might be tedious to estimate all time intervals between each considered tracking point and the destination. However, it still be possible to estimate these time intervals between tracking points and the most frequent destinations such as districts or avenues presenting a high percentage of deliveries.

Dr Amine BOUFAIED
Higher Institute of Computer Science and Information Techniques

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This page is a summary of: Dynamic delay risk assessing in supply chains, IET Intelligent Transport Systems, December 2016, the Institution of Engineering and Technology (the IET),
DOI: 10.1049/iet-its.2015.0235.
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