Call for Implementation Cases for ALICE Innovation Award for Data Sharing in Supply Chain

Wednesday, August 3rd, 2022

Data sharing has contributed to reduced administrative costs in transport and logistics, improved overall efficiency of the logistics chain,  more efficient enforcement of freight transport rules in the Union, as well as the European Maritime Single Window Environment and the EU Single Window Environment for Customs.

We open the call to look for concrete examples in which R&I projects’ results have been further developed and have been deployed to enable and facilitate data sharing in supply chain. We will form a selection committee to look into all collected cases and present the 4th ALICE Logistics Innovation Award to recognise the achievement in a dedicated event at the end of 2022.

Submission Open: 03 August 2022

Submission Close: 15 September 2022

To: info@etp-logistics.eu (ref: Implementation Cases for Data Sharing)

Implementation Cases

Implementation Cases are concrete examples in which causal links between public R&I funding and technology, organizational or process innovation in a specific logistics area can be established. Implement Cases are that research results have been further developed and have been deployed as commercial solutions, have generated a new market or have contributed to new policies and will stablish causal links between research funding and impact.

Data Sharing in Supply Chain

Data sharing in supply chain is referred to as the extent to which crucial and/or proprietary information is available to other actors of the supply chain for the completion of their daily operations. Data sharing includes business-to-business and/or business-to-authorities.

You may download the Brochure Call for Implementation Case on Data Sharing in Supply Chain for further details.

More information:

 

Activities performed in the frame of BOOSTLOG Project, “Boosting impact generation from research and innovation on integrated freight transport and Logistics system”, that has received funding from the European Union´s Horizon 2020 research and innovation Programme under grant No 101006902

 


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