Transferability Framework – Capacity Building Programme (Final)

The ICONET project’s objective was to advance the state-of-the-art in the Physical Internet (PI) deployments and solutions in T&L through extensive R&D activities into ICT (mainly cloud and IoT capabilities) required to build a new networked open architecture for interconnected logistics hubs that will pave the way for widespread industrial adoption. In essence ICONET aims to provide the live Digital Twin to the Physical Internet that represents the physical assets in the supply chain: trucks, products, rail, ships, warehouses, etc.
By informing ‘what is where?’ and ‘what needs to be where?’ it allows cargo and shipments to be routed towards their final destinations automatically and more efficiently, through collaborative decision-making. Decision making will be enabled by the sharing of information across a network, the benefits: delivery of improved efficiency, lower emissions and lower costs and higher customer service. Living Lab activities have driven the identification of the framework where data can be turned into information and through analysis the acquisition of knowledge is possible. ICONET has driven the development of novel solutions on PI where ICT-driven PI logistic configurations have been simulated, prototyped, and validated. Modelling and analysis techniques have been combined with simulation, physical and digital prototyping, based on industry-driven requirements, yielding optimal topologies and distribution policies for the PI.
Click here to read the full report

© 2018 ICONET

EU-flag-(high-resolution)

This project is funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 769119

The views expressed by the ICONET Consortium do not necessarily represent the views of the EU Commission/INEA.
The Consortium and the EU Commission/INEA are not responsible for any use that may be made of the information it contains
EU-flag-(high-resolution)

This project is funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 769119

The views expressed by the ICONET Consortium do not necessarily represent the views of the EU Commission/INEA.
The Consortium and the EU Commission/INEA are not responsible for any use that may be made of the information it contains