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Reshaping LOGistics aiming for 3P: People, Planet, Prosperity

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1. General


SDG 3: Good Health and Well-being

SDG 4: Quality Education

SDG 6: Clean Water and Sanitation

SDG 7: Affordable and Clean Energy

SDG 8: Decent Work and Economic Growth

SDG 9: Industry, Innovation and Infrastructure

SDG 10: Reduced Inequality

SDG 11: Sustainable Cities and Communities

SDG 12: Responsible Consumption and Production

SDG 13: Climate Action

SDG 14: Life Below Water

SDG 17: Partnerships to achieve the Goal



2. Project Details

Company or Institution



Reshaping LOGistics aiming for 3P: People, Planet, Prosperity

General description of the AI solution

ReLOG3P works with the objective to support and partner with all those Stakeholders of the Logistics Ecosystem, at any level (i.e., not limited to, private or public bodies and operators, governmental institutions, research and educational entities, think-tanks or simple innovation passionates), that aim to provide their contribution in the achievement of the SDGs by means of:
1. New Technological Innovations (AI, Data Science, IoT and Edge Computing, Blockchain, Digital Twin, bio and nanotechnologies, quantum computing), interpreted and implemented according to the principles of the “Responsible Research and Innovation ”
2. Development and adoption of a full consolidation of the different logistics modes in a unique logistic network (Optimized Multimodality)
3. Deep and quick Mindset Change, more open to the growth through collaboration and knowledge sharing, shaking off the “we have always done in this way”
4. Building over the Universal Values such as integrity and respect, “Obligations VS Rights” approach





3. Aspects

Excellence and Scientific Quality: Please detail the improvements made by the nominee or the nominees’ team or yourself if your applying for the award, and why they have been a success.

– Type of AI: Any type of AI technology to be applied. In the logistic field, use of AI is still at initial stage and processes are, mostly, "silos based" / inefficient
– Quality of AI solution and algorithm: The application of AI will have to:
1) Have as final objective the increase of specific KPIs related to the SDGs in within the logistic field (mostly related to increase of safety (reduction of LTI/HPI), reduction of energy use and environmental impact, increase of diversity, reskilling/upskilling and lifelong learning, etc.)
2) Ber related to the "Global Indicator Framework for the SDGs & targets of the 2030 Agenda for Sustainable Development"
3) Be implemented according the principles of Ethic/Responsible Innovation
4) Be based on integrity and respect
– Describe status of technology: From TRL5 to TRL9

Scaling of impact to SDGs: Please detail how many citizens/communities and/or researchers/businesses this has had or can have a positive impact on, including particular groups where applicable and to what extent.

– Overall view: Logistics is considered a growing contributor to global climate change. According to the ITF freight transport accounts for about 39% of transport CO2 emissions and around 8% of CO2 emissions worldwide. It is also a major contributor to air pollution. Road constitutes 62% of emissions, while sea contributes 27%, air 6%, rail 3% and inland waterways 2%
– Measurable progress of the AI solution on specific SDGs: recurrent measuring of KPIs
– Clarity of SD components: will be on project based, with clear "Project Charter", S.M.A.R.T. KPIs based objectives
– Projection of impact and uptake of the AI solution to Sustainable Development: AI will be at the base of the achievement of the identified S.M.A.R.T. KPIs based objectives
– Global impact: project will range from local to international impact, built based on modular, scalable and repeatable frameworks
– Global added value: yes

Scaling of AI solution: Please detail what proof of concept or implementations can you show now in terms of its efficacy and how the solution can be scaled to provide a global impact ad how realistic that scaling is.

– Evidence for impact: all projects apply to the logistic chain, E2E, thus scalable @ global level
– Scalability and sustainability of AI solution: see above
– Customer and end user: solutions will impact / benefit, not limited, B2B, B2C, B2G and vice versa, businesses to education and scientific communities and SME
– Network effect: see above. Open dissemination and sharing of results will multiply network effect
– Impact: community is as big as the Global Logistic Chain. Current Company's direct network reach 1000+, of which about 5-10% maybe considered as early adopters. As indicated above, open source, GDPR, Ethic/Responsible Innovation approaches are embedded into the Company/projects DNA

Ethical aspect: Please detail the way the solution addresses any of the main ethical aspects, including trustworthiness, bias, gender issues, etc.

– Ethical considerations and implications of AI (both long and short term): deployment of AI solution in within the projects will be, as indicated above, performed in alignment (and measured against) Ethic/Responsible Innovation approaches
– Trustworthiness of AI solution: see above
– Inclusiveness of solution: see above


International Research Centre in Artificial Intelligence
under the auspices of UNESCO (IRCAI)

Jožef Stefan Institute
Jamova cesta 39
SI-1000 Ljubljana




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