EU Science Hub

Customs risk management: can transport logistics data improve the analysis of shipping containers?

Aug 26 2014

The European Commission adopted a new strategy and action plan to improve customs risk management, responding to the need for better knowledge on cargo movements and supply chains. In this effort, the JRC provides scientific and technical support by evaluating the potential benefits of integrating information on shipping container movements and events in pre-arrival customs risk analysis.

As the volume of trade grows and the international supply chain becomes ever more complex and fast-moving, the new strategy identifies key priorities where action is needed to achieve more effective and efficient EU-wide customs risk management. Recognising the gap in supply chain information for containerised cargo, one of the priorities aims to ensure that customs have high-quality, timely information on goods entering and leaving the EU.

The JRC evaluates the potential contribution of information on shipping container movements and events in enhancing the existing pre-arrival risk assessment. While the entry summary declarations (ENS) currently in use by customs will indicate the port at which a container was loaded on a vessel destined for the EU, they will not necessarily show the port of origin, nor possible transhipments. Given the importance of this information for risk management purposes, the JRC analyses container status messages (CSM) that carriers and trade partners use to follow their containers around the world for commercial reasons. The strategic insights gained will help determine if and how CSM data could be incorporated in pre-arrival risk analysis.

The evaluation is based on the JRC Contraffic research prototype, an IT system which collects information on containers movements and events world-wide and targets suspicious container trips, signalling, for instance, potential cases of false declaration of origin for imported goods. Current research aims at creating novel techniques and methods for a more effective risk analysis through deep-web data mining, semantic data integration, sequence data mining, container itinerary analysis, semantic trajectory clustering and statistical analysis.