Introduction
Here is an uncomfortable truth about UK manufacturing. The sector’s biggest vulnerability is people, or rather, the growing absence of them.
The numbers are blunt. 92% of manufacturing SMEs report skills shortages heading into 2025, according to the Skills Horizon Barometer. 75% of manufacturers cite skills shortages as their single biggest barrier to growth, ahead of recruitment, retention, and supply chain disruption. 61,000 manufacturing job vacancies remained unfilled in late 2024, still well above pre-pandemic levels.
This goes beyond numbers. The workforce is ageing. An estimated 20% of the engineering workforce will retire by 2026. More than half of manufacturers expect 6 to 20% to retire in the next decade. The pipeline of replacements is patchy. Engineering apprenticeship starts declined to 27,900 in 2024/25, still below pre-pandemic levels. Level 2 apprenticeships have fallen 8.7% year-on-year.
Here is where it gets messy. The shortage concentrates in exactly the roles SMEs need most. Fitters, maintenance engineers, process engineers, and multi-skilled operators keep production running and equipment healthy. When they leave, they take decades of institutional knowledge with them. The sound a bearing makes before it fails. The machine that needs ten minutes to warm up on cold mornings. The workaround for a PLC quirk that was never documented. No handover document captures this. No sensor replaces it.
This is a broken loop. Experienced people learn from years of observation and action, but that learning never gets captured in a system where others can benefit. The knowledge cycle ends when the person leaves. Closing that loop, turning individual expertise into organisational memory, is the real challenge.
Women remain strikingly underrepresented, making up just 26% of the manufacturing workforce despite being half the working population. Only 20% of engineering and technology apprenticeship starts are female. When cost pressures hit, training and diversity recruitment are often the first budgets cut. That is precisely when they matter most.
Where Current Technology Falls Short
The technology to transform maintenance, production monitoring, and operational efficiency already exists. Affordable IIoT sensors, AI-driven analytics, digital twins, MES/MOM systems: the tools are there. Most SMEs still lack the ability to use them.
Eight out of ten manufacturers recognise that skills and knowledge gaps block their ability to adopt digital technologies, according to Made Smarter research. This creates a painful cycle. Businesses need digital tools to improve productivity. They lack the people to select, deploy, and maintain those tools. So the tools either sit unused or never get purchased in the first place.
The consequences are measurable. More than half (56%) of UK manufacturers have lost sales due to under-investment in digital, according to the iBASEt/MTC Digital Manufacturing Productivity Report. The ONS has shown that smaller manufacturers consistently have less structured management practices, which correlates directly with lower productivity. Digital tools could help close that gap, if anyone could operate them.
Enterprise manufacturers can hire data scientists and digital transformation teams, competing against finance and tech companies who offer significantly higher salaries. An SME with fifty staff simply cannot compete on those terms. It cannot justify a full-time role for someone whose primary job is interpreting equipment data.
Technology gets bought on promise and stalls on reality. Dashboards go unwatched. Alerts get ignored. 43% of organisations leave the Apprenticeship Levy unspent. Money for workforce development sits idle because there is no capacity to manage the programmes.
The Opportunity: Where People Meet Technology
Here is the quiet part that rarely gets said out loud. The opportunity lives in designing technology around the people who actually have to use it.
AI, IIoT, digital twins, and MES/MOM systems grow increasingly accessible to SMEs. But only when they are implemented in a way that respects the reality of a small team, mixed-age equipment, and limited in-house digital expertise. The most promising approaches share common traits. They learn from the operational data a business already generates. They surface insight in plain language rather than requiring specialist interpretation. They augment existing expertise rather than replacing it.
Consider predictive maintenance. The value lies in connecting sensor data to the practical knowledge your most experienced engineers already carry. A system that captures maintenance history, operating context, and human observation alongside machine data creates something more powerful than either source alone. It turns tacit knowledge into organisational memory that survives retirement. And when the outcome of each intervention feeds back into the system, what worked, what failed, what the engineer noticed, the loop closes. The next person to face that problem starts with the accumulated wisdom of everyone who came before.
Digital twin adoption among UK manufacturers has jumped from 21% to 37% in just twelve months. The UK reports the highest planned adoption rate in Europe. 38% of UK manufacturers now plan to upskill existing talent to work with AI and smart technologies, up from 30% the previous year. The direction of travel is clear. The question is whether SMEs can get on board before the gap widens further.
Recommendation
No single manufacturer can solve the skills shortage alone. But concrete steps separate the SME that stalls from the one that builds resilience. Those steps map directly to four interdependent pillars.
People come first. This is the neglected component. Audit your workforce age profile and identify retirement risk. Build mentoring programmes that pair experienced engineers with newer staff, covering operational knowledge as well as technical skills. Use the Apprenticeship Levy if you have it. Actively recruit from underrepresented groups. Women make up only 26% of manufacturing workers and represent an enormous untapped talent pool. Invest in digital literacy as an ongoing expectation, not a one-off training day.
Data starts with capturing what you already know. The institutional knowledge in your team’s heads is your most perishable asset. Build systems and habits that record maintenance decisions, equipment quirks, and process adjustments alongside automated sensor data. Connected data, machine performance linked to maintenance records, production schedules, and environmental context, turns isolated readings into actionable insight. Without it, even the best technology is guessing.
Technology must meet your team where they are. Prioritise solutions designed for non-specialists: systems that surface plain-language recommendations rather than raw data visualisations, that work with mixed-age equipment, and that can be adopted incrementally. Open standards and modular architectures matter. They protect you from vendor lock-in. They let you build capability over time rather than all at once.
Services fill the gaps you cannot fill internally. External expertise in digital deployment, system integration, and ongoing support lets SMEs access specialist capability without hiring specialists they cannot find or afford. The right partner understands manufacturing operations, not just software. That partner helps you build internal competence rather than creating permanent dependency.
The skills shortage is real. It will not resolve itself. But the manufacturers who treat it as a people-first problem, who invest in knowledge capture, workforce development, and technology that amplifies human capability, will be the ones still thriving in ten years.
The machines will keep running. The question is whether anyone will know how to fix them. The answer lies in closing the loop: capturing what your best people know, embedding it in systems that learn, and building an organisation where every intervention makes the next one better. That is how knowledge stops walking out the door.
Doubly Good is a UK consultancy helping manufacturers bridge the gap between operational reality and digital opportunity, through People, Data, Technology, and Services.