EFFRA recommendations on Factories 4.0 and Beyond (Sept 2016) clearly stated the need for development of large scale experimentation and demonstration of data-driven “connected smart” Factories 4.0, to retain European manufacturing competitiveness. BOOST 4.0 will address this need, by demonstrating in a measurable and replicable way, an open standardised and transformative shared data-driven Factory 4.0 model through 10 lighthouse factories. BOOST 4.0 will also demonstrate how European industry can build unique strategies and competitive advantages through big data across all phases of product and process lifecycle (engineering, planning, operation, production and after-market services) building upon the connected smart Factory 4.0 model to meet the Industry 4.0 challenges (lot size one distributed manufacturing, operation of zero defect processes & products, zero break down sustainable operations, agile customer-driven manufacturing value network management and human centred manufacturing). Our chief objectives include:
- Establish 10 big data lighthouse smart connected factories (VW, FILL, AutoEuropa, +GF+, FIAT, Phillips, Volvo, GESTAMP, Benteler, Whirlpool).
- Provide the RAMI 4.0 and IDS based BOOST 4.0 open EU framework and governance model, for both services and data assets.
- Put together methodologies, assets, models and communities in order to maximise visibility, mobilization, replication potential, and impact (business, financial, standardization) of BOOST 4.0
The investment leveraging factor of BOOST 4.0 will be well above the 4:1 ratio, up to 10:1. In terms of exploitation, in 5-years horizon after the project end, just only the participating lighthouse factories will make a direct follow-on investment above 33Meuro (ROI 10,61), while the commercialisation of the BOOST 4.0 products in the market is expected to generate some 96Meuro cumulative profits (ROI 4,73) for the rest of the partners.
More information under: https://boost40.eu/
Boost 4.0 has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 780732