Periodic Reporting for period 1 - AUTOSUP (Preparing the ground for AUTonomous Multimodal SUPply Chains)
Berichtszeitraum: 2024-06-01 bis 2025-11-30
Freight transport underpins Europe’s economy, yet logistics networks face increasing congestion, inefficiencies, rising operational costs and growing environmental pressures. Limited digitalisation, weak interoperability and fragmented collaboration across modes create delays, reduce competitiveness and hinder progress toward the European Green Deal and the Sustainable and Smart Mobility Strategy. A shift toward automated, data-driven and multimodal operations is essential.
AUTOSUP addresses these challenges by developing an integrated framework to support the adoption of automation in multimodal freight transport, aligned with the Physical Internet vision. A multidisciplinary consortium works with logistics ecosystems to identify automation requirements, analyse enabling technologies and deliver tools to assess their feasibility and impact. Two Living Hubs in Antwerp-Bruges and Trieste act as real-world testing environments across road, rail, maritime, inland waterways and airport connections.
The project provides stakeholders with an open, data-driven Decision Support System (DSS) and Digital Twin (DT) environment to simulate automation scenarios, reduce uncertainties and guide investment planning. AUTOSUP also designs governance and organisational models that promote cross-modal collaboration and lower adoption barriers. Through feasibility studies, impact assessments, stakeholder engagement and policy recommendations, AUTOSUP supports the transition toward efficient, resilient and low-emission logistics networks. Social innovation principles ensure that workforce needs, safety and human-centred factors are embedded throughout the project.
AUTOSUP addresses these challenges by developing an integrated framework to support the adoption of automation in multimodal freight transport, aligned with the Physical Internet vision. A multidisciplinary consortium works with logistics ecosystems to identify automation requirements, analyse enabling technologies and deliver tools to assess their feasibility and impact. Two Living Hubs in Antwerp-Bruges and Trieste act as real-world testing environments across road, rail, maritime, inland waterways and airport connections.
The project provides stakeholders with an open, data-driven Decision Support System (DSS) and Digital Twin (DT) environment to simulate automation scenarios, reduce uncertainties and guide investment planning. AUTOSUP also designs governance and organisational models that promote cross-modal collaboration and lower adoption barriers. Through feasibility studies, impact assessments, stakeholder engagement and policy recommendations, AUTOSUP supports the transition toward efficient, resilient and low-emission logistics networks. Social innovation principles ensure that workforce needs, safety and human-centred factors are embedded throughout the project.
During the first reporting period, AUTOSUP achieved all planned milestones and deliverables, establishing the scientific and technical foundations for its modelling and automation-assessment activities.
The project developed a comprehensive taxonomy of logistics automation technologies across all transport modes, enabling comparison of maturity levels, identification of cross-modal dependencies and informed development of operational models. AUTOSUP then produced a consolidated set of functional, technical, organisational and regulatory requirements for seamless multimodal automation.
Based on these requirements, the consortium defined Use Case scenarios and KPIs for the Antwerp-Bruges and Trieste Living Hubs. AS-IS and TO-BE models, BPMN diagrams and information flows now form the operational basis for Digital Twin and DSS development.
Major technical progress was achieved through delivery of the first DSS version, including the initial multimodal Knowledge Graph (KG) describing assets, processes, constraints and interdependencies. Information flows from WP1 were integrated, enabling machine-readable representations of L-Hub operations. In parallel, DT developments aligned with the defined scenarios, supporting early model integration and configuration of what-if analyses.
A key scientific output was the generalised Cost–Benefit Analysis (CBA) model, providing a consistent framework for evaluating automation solutions across use cases, using aligned financial assumptions, operational KPIs and environmental impact metrics.
The project developed a comprehensive taxonomy of logistics automation technologies across all transport modes, enabling comparison of maturity levels, identification of cross-modal dependencies and informed development of operational models. AUTOSUP then produced a consolidated set of functional, technical, organisational and regulatory requirements for seamless multimodal automation.
Based on these requirements, the consortium defined Use Case scenarios and KPIs for the Antwerp-Bruges and Trieste Living Hubs. AS-IS and TO-BE models, BPMN diagrams and information flows now form the operational basis for Digital Twin and DSS development.
Major technical progress was achieved through delivery of the first DSS version, including the initial multimodal Knowledge Graph (KG) describing assets, processes, constraints and interdependencies. Information flows from WP1 were integrated, enabling machine-readable representations of L-Hub operations. In parallel, DT developments aligned with the defined scenarios, supporting early model integration and configuration of what-if analyses.
A key scientific output was the generalised Cost–Benefit Analysis (CBA) model, providing a consistent framework for evaluating automation solutions across use cases, using aligned financial assumptions, operational KPIs and environmental impact metrics.
During the first reporting period, AUTOSUP made substantial progress toward its beyond-SOTA ambitions. The advances achieved to date can be mapped directly onto the following four strategic innovation areas:
(a) Open DSS and DT capability for autonomous supply chains
• Harmonised AS-IS/TO-BE models and initial DSS interface for visualising multimodal networks
• First multimodal Knowledge Graph representing processes, data exchanges and dependencies
• Generalised CBA model and aligned KPI structure enabling automated scenario assessment
• DT/KG foundations enabling future real-time event processing and predictive analytics
(b) Co-developed operational models in L-Hubs
• A replicable validation methodology combining requirements, process modelling, KPIs and simulation inputs
• Application across six diverse use cases, forming the basis for feasibility studies in RP2
(c) Social innovation for automation adoption
• Stakeholder insights on workforce, training and organisational needs collected through two workshops
• Early definition of social and behavioural indicators for inclusion in future DSS and feasibility analyses
(d) Advancing PI-like multimodal freight transport
• Mapping of automation opportunities across infrastructure layers
• Shared semantic data structure supporting interoperable exchanges
• Network-level representation enabling future routing and capacity optimisation
• Initial DSS outputs offering transparent, comparable decision-support information
(a) Open DSS and DT capability for autonomous supply chains
• Harmonised AS-IS/TO-BE models and initial DSS interface for visualising multimodal networks
• First multimodal Knowledge Graph representing processes, data exchanges and dependencies
• Generalised CBA model and aligned KPI structure enabling automated scenario assessment
• DT/KG foundations enabling future real-time event processing and predictive analytics
(b) Co-developed operational models in L-Hubs
• A replicable validation methodology combining requirements, process modelling, KPIs and simulation inputs
• Application across six diverse use cases, forming the basis for feasibility studies in RP2
(c) Social innovation for automation adoption
• Stakeholder insights on workforce, training and organisational needs collected through two workshops
• Early definition of social and behavioural indicators for inclusion in future DSS and feasibility analyses
(d) Advancing PI-like multimodal freight transport
• Mapping of automation opportunities across infrastructure layers
• Shared semantic data structure supporting interoperable exchanges
• Network-level representation enabling future routing and capacity optimisation
• Initial DSS outputs offering transparent, comparable decision-support information