Recipient organisationOldham CollegeSource-published name: The Oldham College
Funding£4.3M
PeriodMar 2024 — Mar 2026
In plain English
AI plain-English summary
Nine further education colleges across Greater Manchester are embedding innovation centres into every borough to help local businesses adopt new technologies and working methods. This matters because the region has set ambitious productivity and net-zero targets, but many small and medium-sized businesses lack the specialist knowledge or confidence to adopt innovations that could improve their performance. The programme addresses a gap in the skills system: colleges traditionally train people for existing jobs, but they rarely act as hubs where businesses can test and implement new ideas. By placing "Innovators in Residence"—specialists in advanced materials, health innovation, digital and creative industries, and clean growth—directly into colleges, the programme aims to demystify the process of adopting innovation for local employers. If successful, the programme could shift how further education colleges support regional economies. Rather than simply delivering qualifications, colleges would become permanent local entry points where businesses can access expertise, test new processes, and connect with other support services such as catapults and knowledge transfer networks. The model is designed to outlast the funding period by upskilling college staff and seconding experts from innovation partners, so the knowledge stays in the system. This is an applied programme with a clear practical goal: increasing business adoption of innovation across Greater Manchester’s ten boroughs.
View original technical description
**Greater Manchester FE Innovation Programme** Nine further education colleges have collaborated to expand innovation support and increase the diffusion and adoption of innovation and productivity across Greater Manchester in response to the demand for skills and the region's productivity ambition. The Greater Manchester Further Education Innovation model increases knowledge exchange and engagement across the city region through an innovative programme to increase capacity and capability in business and the FE system. By creating an Innovation Centre in each of the ten Boroughs in Greater Manchester, the Colleges will engage and support local employers and stakeholders in identifying starting points to build their innovation journey and community impact, prioritising businesses within the frontier sectors identified in the local industrial strategy. It directly connects adopting innovations in business with the Integrated Technical Education provision to enable it. 'Innovators in Residence' will bring specialist expertise to inform the CPD of College staff and create a college campus a place to collaborate and co-create, and new ideas can be tested and trialled. The Innovators in Residence will be specialists in diffusion and adoption in the four GM Frontier Sectors of : * Advanced Materials & Manufacturing * Health Innovation and Life Sciences * Digital and Creative * Clean Growth We will also harness the power of Apprentices in business to demystify innovation and support the mindsets and process of adoption. The programme is additive, not duplicative, and by creating a place for networking, learning and knowledge exchange, we create a local entry point from which the FE Business Innovation teams can refer and signpost to other support services such as Knowledge Transfer Networks, Catapults, Made Smarter and complementary Innovation Accelerator programmes such as the Centre for Digital Innovation. Our approach is to increase capacity and capability in the system by upskilling FE staff or seconding from Innovation Ecosystem partners wherever possible. In this way, the specialised and tacit knowledge gained from the GM FE Innovation Programme experience is retained within the network after the funding ends. It creates a FEIF legacy to build on as Greater Manchester grows and meets its net zero ambition.
Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.
Is something wrong? Let us know