Summary
The past years have shown the vulnerability of rigid international supply chains. The ability to adapt to changes and create resilient supply chains will be a key competitive advantage for manufacturers in the future. RAASCEMAN tackles three different possibilities to react to unforeseen events:
adapting the production plan based on supply chain data
adapting the supply chain by switching the supplier using a MaaS network
integrating remanufacturing as procurement alternative leveraging circularity.
The project aims to enable companies to mitigate short- and medium-term unforeseen events and to enable companies to participate in a dynamic MaaS-network lowering the market barriers for companies specialized in remanufacturing or alternative technologies such as 3D printing.
RAASCEMAN designs and demonstrates a series of software tools digitizing supply chains by using digital twins and an infrastructure for data-exchange based on European values. RAASCEMAN will develop its overall ambition by the means of five scientific objectives namely:
Actionable propositions for adapting supply chains or internal production and logistics based on reliable quantification and impact prediction of unforeseen events
Dynamic supply chain generation enabling resilience and self-adaptation of MaaS networks,
Building Trust in MaaS networks auditing suppliers’ reliability and testing plausibility of offers
Dynamic planning and scheduling of production processes enabling companies to swiftly adapt logistics and production to varying external conditions and
Dynamic assembly and disassembly to enable machines in the field level
We demonstrate our solutions in two industrial use-cases of the automotive and bike industry and create a MaaS network connecting five pilot lines distributed over Europe. The majority of project results will be made available under appropriate open-source licensing schemes to allow further maturation in integration after the RAASCEMAN project concludes.
adapting the production plan based on supply chain data
adapting the supply chain by switching the supplier using a MaaS network
integrating remanufacturing as procurement alternative leveraging circularity.
The project aims to enable companies to mitigate short- and medium-term unforeseen events and to enable companies to participate in a dynamic MaaS-network lowering the market barriers for companies specialized in remanufacturing or alternative technologies such as 3D printing.
RAASCEMAN designs and demonstrates a series of software tools digitizing supply chains by using digital twins and an infrastructure for data-exchange based on European values. RAASCEMAN will develop its overall ambition by the means of five scientific objectives namely:
Actionable propositions for adapting supply chains or internal production and logistics based on reliable quantification and impact prediction of unforeseen events
Dynamic supply chain generation enabling resilience and self-adaptation of MaaS networks,
Building Trust in MaaS networks auditing suppliers’ reliability and testing plausibility of offers
Dynamic planning and scheduling of production processes enabling companies to swiftly adapt logistics and production to varying external conditions and
Dynamic assembly and disassembly to enable machines in the field level
We demonstrate our solutions in two industrial use-cases of the automotive and bike industry and create a MaaS network connecting five pilot lines distributed over Europe. The majority of project results will be made available under appropriate open-source licensing schemes to allow further maturation in integration after the RAASCEMAN project concludes.
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More information & hyperlinks
Web resources: | https://cordis.europa.eu/project/id/101138782 |
Start date: | 01-09-2024 |
End date: | 31-08-2027 |
Total budget - Public funding: | 4 637 300,00 Euro - 4 637 300,00 Euro |
Cordis data
Original description
The past years have shown the vulnerability of rigid international supply chains. The ability to adapt to changes and create resilient supply chains will be a key competitive advantage for manufacturers in the future. RAASCEMAN tackles three different possibilities to react to unforeseen events:adapting the production plan based on supply chain data
adapting the supply chain by switching the supplier using a MaaS network
integrating remanufacturing as procurement alternative leveraging circularity.
The project aims to enable companies to mitigate short- and medium-term unforeseen events and to enable companies to participate in a dynamic MaaS-network lowering the market barriers for companies specialized in remanufacturing or alternative technologies such as 3D printing.
RAASCEMAN designs and demonstrates a series of software tools digitizing supply chains by using digital twins and an infrastructure for data-exchange based on European values. RAASCEMAN will develop its overall ambition by the means of five scientific objectives namely:
Actionable propositions for adapting supply chains or internal production and logistics based on reliable quantification and impact prediction of unforeseen events
Dynamic supply chain generation enabling resilience and self-adaptation of MaaS networks,
Building Trust in MaaS networks auditing suppliers’ reliability and testing plausibility of offers
Dynamic planning and scheduling of production processes enabling companies to swiftly adapt logistics and production to varying external conditions and
Dynamic assembly and disassembly to enable machines in the field level
We demonstrate our solutions in two industrial use-cases of the automotive and bike industry and create a MaaS network connecting five pilot lines distributed over Europe. The majority of project results will be made available under appropriate open-source licensing schemes to allow further maturation in integration after the RAASCEMAN project concludes.
Status
SIGNEDCall topic
HORIZON-CL4-2023-TWIN-TRANSITION-01-07Update Date
13-11-2024
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MiE_25-27_RP01: Sustainable value network resilience and competitiveness through robust and flexible production technologies
R&I Objective 1.1: Data ‘highways’ and data spaces in support of smart factories in dynamic value networks
R&I Objective 1.4: Artificial intelligence for productive, excellent, robust and agile manufacturing chains - Predictive manufacturing capabilities & logistics of the future