Imagine an 'Airbnb' for factory machines where companies can rent out their spare production capacity to others. The project creates a smart digital map that matches a buyer's needs with a provider's available tools automatically. It also includes a set of rules to ensure data stays private and the AI decisions are easy to understand.
Manufacturers often have idle machinery while other companies struggle to find production capacity. Current systems lack the trust, data security, and real-time coordination needed to share these resources safely.
["Governance services and APIs for data and AI model management.", "Optimization and analytics models with integration guidelines for real-time service composition.", "Digital Twin models for both individual factories and wider value networks."]
If you are a component supplier dealing with sudden spikes in demand that exceed your machine capacity — this project developed a network of digital twins that allows you to find and rent trusted production capacity from other factories. This ensures you meet deadlines without investing in new hardware.
If you are a machining shop dealing with expensive equipment that sits idle for 30% of the time — this project developed a service broker platform that lets you sell your idle capacity as a service. This turns wasted machine time into a new revenue stream.
If you are a hardware startup dealing with high costs of setting up a full production line — this project developed a Manufacturing-as-a-Service system that lets you outsource production to a distributed network. You can scale production up or down based on real-time market demand.
Based on available project data, specific pricing is not mentioned, but the project focuses on developing innovative business models to ensure trust and resource sharing.
The project is designed for distributed value networks and will be validated in 3 real-world networks to prove its ability to handle varying supply and demand.
Based on available project data, the project includes a Data Governance view specifically addressing data authorization and ownership, though specific licensing terms are not listed.
The system uses APIs and configuration guidelines to integrate digital twin models and governance services at both the factory and value network levels.
The project period runs from 2024-01-01 to 2026-12-31, indicating the solution will be developed and validated by the end of 2026.
The consortium is heavily weighted toward industrial application, with 8 industry partners (62% of the 13 total members) and 2 SMEs. This strong industrial presence, spanning 6 countries (DE, EL, ES, IT, NO, TR), suggests the resulting tools are being built for actual shop-floor utility rather than purely academic interest.
Contact MAGGIOLI SPA in Italy for coordination details.
Contact us to connect with the Tec4MaaSEs consortium for early adoption of MaaS governance tools.
