Introduction
Here’s a question worth sitting with. If your production manager called in sick tomorrow, how much of what they know about your factory’s performance is written down somewhere accessible?
For most UK SME manufacturers, the honest answer is uncomfortable. Critical operational knowledge lives in people’s heads, in ad hoc spreadsheets on individual laptops, or on paper forms filed in cabinets nobody opens. Which machine drifts on humid days. Which supplier’s material runs slightly out of spec. Which shift consistently outperforms the others and why. None of it is captured in a shared, accessible system.
This isn’t negligence. It’s the natural result of decades of organic growth and technology investments made one problem at a time. The CNC machine came with its own monitoring. The ERP was installed in 2014. The quality records are in Excel. The maintenance log is on a clipboard by the workshop door.
The numbers tell the story plainly. According to the iBASEt/MTC Digital Manufacturing Productivity Report, 35% of manufacturing processes still run on paper and 49% are tracked using manual spreadsheets. More than half, 56% of UK manufacturers, are losing sales as a direct consequence of under-investment in digital technology. And 73% cite legacy technology as a major productivity blocker, ahead of Brexit.
The data exists. It just can’t talk to itself. And when data can’t talk to itself, neither can your teams. The real value of data lies in closing the loop: gathering information, acting on it, and feeding the outcomes back into the system so the next decision is better than the last. Most SMEs never get past step one.
Current Technology Landscape
Here’s where it gets messy. The problem isn’t that technology doesn’t exist. It’s that the technology most SMEs have access to creates as many silos as it solves.
ERP systems capture transactions, not context. Your ERP knows what was ordered and invoiced. It doesn’t know why Tuesday’s first shift ran 12% slower, or why scrap rates on Product X have crept up for three months. That context lives elsewhere. Or nowhere.
Machine monitoring stays in its lane. Newer machines come with OEM monitoring, but it’s locked into that vendor’s ecosystem. Your Fanuc controller doesn’t share data with your Siemens drive, which doesn’t connect to your Mitsubishi robot. Each generates useful information in isolation. The picture never comes together.
Spreadsheets are flexible but fragile. When a factory runs on spreadsheets, it runs on the person who built them. Version control is nonexistent. The data is only as good as the last person who remembered to update it. A 2024 survey of 229 SME manufacturers found that 72% cite cost as the biggest barrier to digital adoption. But the hidden cost of not digitising is the compounding inefficiency fragmented data creates every day.
The skills gap makes everything harder. Eight out of ten manufacturers acknowledge that gaps in skills and knowledge are impacting their ability to adopt digital technologies. The Make UK/Autodesk Future Factories report reveals that only 16% of UK manufacturers actively use advanced digital tools like robotics and AI. Large companies are more than twice as likely as SMEs to adopt these technologies (71% versus 28%).
People, not technology, are the most neglected part of this equation. You can buy sensors, subscribe to dashboards, and install new software. But if the people on the shop floor don’t trust the data, can’t access it in a useful format, or had no say in setting it up, the investment underdelivers. Every time.
The Opportunity
The good news: the technology to connect fragmented data has become dramatically more accessible in the last few years. It doesn’t require ripping out what you already have.
IIoT and edge devices can now retrofit even legacy equipment with basic data capture: vibration, temperature, cycle counts, energy draw. Costs are a fraction of what they were. Open protocols mean this data can flow into a unified layer rather than creating yet another silo.
MES and MOM platforms, once the preserve of automotive and aerospace giants, are increasingly available in modular, cloud-based forms that SMEs can adopt incrementally. They connect the dots between what the ERP thinks happened and what actually happened on the shop floor.
AI and machine learning can detect patterns in combined datasets that no human analyst would spot in isolated spreadsheets: early scrap trends, subtle cycle-time drift, correlations between environmental conditions and quality outcomes. The key word is combined. AI trained on fragmented data produces fragmented insight.
Digital twins model a production line using integrated real-time data. They let manufacturers simulate changes before implementing them. Even a lightweight digital twin of a single critical process can unlock significant insight for an SME.
The iBASEt/MTC report found that 56% of manufacturers aren’t effectively harnessing Industry 4.0 data, even after investing in the technology. That’s a data integration problem, not a data collection problem. And it’s solvable.
The key shift: move from dashboards that show you what happened to systems that close the loop. The action you take and the result it produces feed back into the data, making every subsequent decision more informed. Connected data delivers more than visibility. It builds a cycle of continuous improvement that compounds over time.
Recommendation
Solving data fragmentation isn’t a technology project. It’s an organisational shift that requires attention to four interconnected pillars. The order matters.
People first. Always. This is the part most technology vendors skip, and it’s the reason most digital initiatives stall. Your operators, supervisors, and engineers know things about your processes that no system captures yet. Their buy-in isn’t optional. It’s foundational. Start by involving them in identifying what data matters, where it’s missing, and what would actually make their jobs easier. The Make UK Skills Commission reports 55,000 unfilled long-term vacancies in UK manufacturing, costing £6 billion in lost output annually. You can’t afford to alienate the people you already have by imposing systems on them. Train, involve, and listen.
Data must be connected, contextualised, and trusted. The goal isn’t to collect more data. It’s to connect what you already have. Machine performance combined with operator input, material batch information, environmental conditions, and maintenance history is transformative. Look for approaches that create a unified data layer across existing systems rather than demanding you replace them.
Technology should serve the strategy, not the other way around. Start small. Pick a single pain point where data fragmentation is visibly costing you. Instrument it. Connect it. Prove the value. Then expand. Avoid large-scale programmes that try to solve everything at once. That’s how 68% of SME manufacturers end up planning to invest but never quite getting there. Modular, open-standard systems that work with existing equipment are the right foundation.
Services bridge the capability gap. Most SMEs don’t have, and shouldn’t need, a dedicated data engineering team. External partners who understand both manufacturing operations and data integration can accelerate the journey from fragmented spreadsheets to connected insight. The right support looks like practical help on the shop floor, not a strategy deck and a software licence.
And here’s the quiet part that rarely gets said out loud: the biggest risk isn’t that you invest in the wrong technology. It’s that you wait. Every month of fragmented data is another month of invisible losses. Scrap you can’t trace. Stoppages you can’t explain. Continuous improvement programmes that can’t improve what they can’t measure.
The data is already there. It just needs to be connected. And the people who use it need to be at the centre of how that happens. When data flows in a closed loop, observed, acted upon, and the outcomes captured, every cycle makes the organisation a little smarter. That’s continuous improvement as a way of working, not digital transformation as a one-off project.
Doubly Good is a UK consultancy helping manufacturers bridge the gap between operational reality and digital opportunity, through People, Data, Technology, and Services.