Five challenges every UK manufacturer is talking about right now

Spend enough time speaking with manufacturers and you start to notice something interesting.

The factories may be different. The products coming off the line may be different. The equipment, processes, teams and operational pressures certainly are.

But the conversations have a habit of arriving at remarkably similar places.

Over the past couple of years, I’ve spent a lot of time speaking with manufacturing teams across the UK and Europe. Some businesses are well into their digital transformation journey. Others are still working with a mixture of established systems, spreadsheets, manual processes and knowledge that sits largely in the heads of experienced people.

Yet regardless of where they sit on that spectrum, many of the operational challenges being discussed are becoming increasingly familiar.

• How do we get a clearer picture of what is actually happening on the factory floor?
• Why does it still take so long to understand what caused a performance issue?
• How do we react faster when production doesn’t go to plan?

And with more manufacturing technology available than ever, how do we make things simpler rather than simply adding another system?

Those conversations are also reflected in some of the latest research into manufacturing operations technology.

The Manufacturing Operations Management Institute’s Manufacturing Use Case Navigator: Flagship Report 2026 looks at 21 manufacturing use cases across five operational domains. One of its central conclusions is particularly interesting: at a feature level, many manufacturing systems increasingly appear similar. The meaningful differences become much clearer when actual production deviates from plan.

And that matters because, as everyone working in manufacturing knows, production rarely follows the plan perfectly.

Here are five challenges I think will continue to dominate conversations with manufacturers throughout 2026.

We have more data than ever. But can we actually see what is happening?

Manufacturing certainly doesn’t have a shortage of data.

Machines generate it. Production systems capture it. ERP platforms hold it. Operators record it. Quality systems, maintenance platforms and countless spreadsheets contain even more of it.

The problem is that having data and having operational visibility are two very different things.

A production manager shouldn’t need to spend half the morning piecing together yesterday’s performance before they can start improving today’s.

If a line stops, slows down or begins producing below expectation, the most useful question isn’t whether that event was eventually recorded. It’s whether the right people can see what is happening quickly enough to do something about it.

That distinction is becoming increasingly important.

The MOMi report itself deliberately moves beyond asking what a manufacturing system technically “supports” and instead examines how systems behave in execution. It argues that feature lists alone can obscure meaningful differences between platforms and operational approaches.

For manufacturers, I think there’s a useful lesson in that.

The objective shouldn’t be to collect everything simply because we can.

It should be to make the information that matters visible, understandable and useful to the people who can act on it.

Production doesn’t go to plan, and that’s where the real test begins

Every production plan looks good before the shift starts.

Then reality arrives.

A machine stops unexpectedly. A material is unavailable. A quality check fails. A changeover takes longer than expected. 

This is where manufacturing operations become far more interesting than the plan itself.

One of the strongest observations in the MOMi research is that differences between manufacturing systems become most apparent when execution deviates from plan. Some systems observe what happened. Some record it. Others can enforce rules or actively govern what should happen next.

I think manufacturers should be asking themselves a similar question about their wider operating model.

• What happens in our factory when reality deviates from the plan?
• How quickly is the problem visible?
• Does everyone understand what has happened?
• Who needs to make the decision?
• Do they have the information required to make it?
• And once the issue is resolved, do we capture enough information to stop it happening again?

That last question is particularly important.

Because the goal isn’t simply to become better at reacting to problems. It’s to build an operation that continuously learns from them.

Our systems are connected. Our information often isn’t.

Most established manufacturing businesses already have a substantial technology environment.

ERP. MES. SCADA. Quality systems. Maintenance platforms. Warehouse systems. Scheduling tools. Automation. Machine-level systems. 

And, inevitably, a healthy collection of spreadsheets filling the gaps between them.

The MOMi research highlights this complexity directly. Manufacturing Operations Management increasingly sits amongst a much broader IT and OT landscape, and functional boundaries between technologies are becoming less clear.

That creates an important distinction between having multiple systems and having a connected operation.

Manufacturers don’t necessarily need another platform every time they discover a new problem.

Often the bigger opportunity is getting more value from what already exists.

• Can production information move between systems?
• Can data from the factory floor be connected to its operational context?
• Can the people making decisions get the information they need without opening four systems and manually reconciling the answer?
• Can technology reduce administrative work for frontline teams rather than create more of it?

Integration is often discussed as a technical challenge.

Increasingly, I think it should be viewed as an operational one.

Because every disconnected system potentially creates another gap between something happening and someone understanding what it means.

Insight is only valuable if it leads to action

Dashboards have become ubiquitous in manufacturing.

That’s generally a positive development. Better visibility is an important foundation for better performance.

But visibility alone doesn’t improve a factory.

People do.

• The more useful question is therefore becoming: what happens after we see the problem?
• If performance drops, can the team understand why?
• If downtime increases, can they identify the recurring causes?
• If one shift consistently performs differently from another, can managers investigate what is driving the variation?

And importantly, can the people closest to production contribute their knowledge to that process?

This is where I think the next phase of manufacturing intelligence becomes particularly interesting.

Artificial intelligence will inevitably play a larger role in helping teams interrogate operational information, identify patterns and accelerate analysis. But the opportunity isn’t AI for AI’s sake.

It is shortening the distance between something happening, understanding why it happened and deciding what to do next.

For manufacturers evaluating new technology, that should remain the benchmark.

• Does this help our people make a better decision?
• Does it help them make it faster?
• And does it ultimately improve the operation?

If the answer isn’t clear, another dashboard probably isn’t the solution.

How do we improve without adding more complexity?

This may be the biggest challenge of all.

Manufacturers are under constant pressure to improve productivity, reduce downtime, manage costs, maintain quality and make better use of their existing assets.

At the same time, the technology landscape available to solve those problems is expanding rapidly.

That creates a very real risk: trying to simplify manufacturing operations by introducing more operational complexity.

The MOMi research makes an important point here too. Manufacturing capabilities don’t exist independently. Performance in one use case can depend heavily on capabilities elsewhere, which is why the report recommends manufacturers begin with their own critical operational use cases and constraints rather than starting with vendor names.

That is an approach I strongly agree with.

Start with the operational problem.

• Where are we losing productive time?
• What information are we currently missing?
• Which decisions are taking too long?
• Where are operators being asked to complete unnecessary manual processes?
• What recurring problems do we already know exist but struggle to quantify?

Only then should technology enter the conversation.

The objective isn’t to digitise everything.

It’s to identify where greater visibility, better information or a more effective workflow can create a measurable improvement.

Prove it, learn from it and build from there.

The common thread

When I look across these five challenges, there is a common theme.

Manufacturers are not simply trying to generate more information.

They are trying to close the gap between what happens on the factory floor and what happens next.

See the issue sooner.

Understand it faster.

Give the right people the right information.

Respond more effectively.

Learn from what happened.

Then use that knowledge to improve the next shift, the next run and the next production decision.

That is ultimately where I believe manufacturing technology delivers its greatest value.

Not when it adds another layer of complexity to the operation, but when it removes it.

And not when it replaces the experience of manufacturing teams, but when it gives those teams better information with which to apply that experience.

These are exactly the kinds of conversations I’m looking forward to having at PPMA Show UK 2026 at the NEC Birmingham from 22–24 September, where the OFS team will be exhibiting at Stand P62.

If any of these five challenges sound familiar, come and speak with us.

Sometimes the most useful place to start isn’t with a product demonstration.

It’s with the problem you’re trying to solve.

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