Why better use of operational data could unlock the next stage of UK manufacturing productivity

UK manufacturing is frequently described as an industry under pressure. Rising employment costs, energy prices, skills shortages, supply-chain disruption and international competition are all placing greater demands on manufacturers. Yet the sector remains one of the most strategically important parts of the British economy. Nick Hallam from ELF Productivity looks at how manufacturers can unlock the next stage of their productivity improvement journey using the data they already have.

Manufacturing accounted for 8.5% of total UK economic output in the first quarter of 2026, while UK manufacturers recorded product sales of £452.2 billion during 2024. The sector supports approximately 2.6 million jobs and is responsible for a disproportionately large share of business research and development.

The UK is now the world’s eleventh-largest manufacturing economy, with annual output valued at approximately $279 billion. Manufacturing also accounts for an estimated 48% of UK business research and development and 17% of total business investment.

The importance of manufacturing is therefore not adequately represented by its percentage of GDP alone. It anchors supply chains, supports skilled employment, drives exports and creates innovation that spreads into the wider economy.

However, the next major improvement in UK manufacturing competitiveness may not come solely from buying faster machinery, increasing headcount or installing more automation.

It may come from understanding more clearly what is already happening inside the factory.

UK Manufacturing Is Data-Rich but Often Insight-Poor

Every production environment generates an enormous amount of information.

ERP and MRP systems contain orders, routings, standards, material requirements and planned production quantities. Time and attendance systems hold working-time information. Machines and PLCs produce performance signals. Quality systems record defects and rework. Finance departments hold labour rates, overheads and cost information.

Employees and supervisors possess another critical layer of knowledge: why a job stopped, why a changeover took longer, why material was unavailable or why output fell below expectation.

The problem is not necessarily that this information does not exist.

The problem is that it frequently exists in different systems, at different levels of detail and at different points in time.

Across the wider UK economy, approximately 83% of businesses handle some form of digital data and 72% of those businesses analyse it. Yet only 4% report analysing high-volume or high-variety “big data”. Just 13% work with sensor data, such as information generated by machinery, equipment or CCTV.

This distinction matters.

Possessing data is not the same as being able to use it operationally.

A manufacturer may know the total labour cost for the month, the number of employees who attended work and the total quantity produced. That does not necessarily reveal:

• Which jobs absorbed the labour
• Which operations exceeded their standard times
• Where downtime occurred
• Whether delays were caused by labour, machinery, material or process issues
• Which products generated the greatest level of rework
• How Work in Progress changed during the day
• Which customers, jobs or contracts delivered the expected margin

The business may have all the individual pieces of information while still lacking a complete operational picture.

Better Decisions Require Better Context

Data becomes valuable when it explains the relationship between an event and its commercial consequence.

Knowing that a machine stopped for 40 minutes is useful.

Knowing that the stoppage delayed three jobs, created two hours of additional labour, affected a customer delivery and reduced the expected margin is considerably more valuable.

The same principle applies to workforce information.

Knowing that 80 employees worked an eight-hour shift confirms attendance. It does not explain how their 640 paid hours were distributed across productive work, changeovers, maintenance, waiting time, rework, training and other non-productive activities.

Manufacturers therefore need to connect four essential dimensions:

What was planned
The order, routing, standard time, expected quantity, labour requirement and target cost.

What actually happened
The employees involved, activities completed, hours consumed, quantities produced, machines used and interruptions encountered.

Why performance varied
Downtime, material shortages, quality failures, changeovers, resource constraints, skill issues or process inefficiencies.

What the variation meant financially
The effect on labour cost, Work in Progress, production cost, recovery, margin and customer delivery.

Until these dimensions are connected, management teams can spend considerable time debating whose figures are correct instead of addressing what the figures are saying.

The Management Gap Is Also a Data Gap

The quality of operational information is closely connected to the quality of management practice.

The Office for National Statistics measures structured management practice across four areas: Continuous Improvement, the use of key performance indicators, target-setting and employment practices.

Its latest survey found that production businesses had an average management-practice score of 0.52, compared with 0.56 across service-sector firms. Of the four measured management disciplines, the use of KPIs recorded the lowest average score, at 0.42. Firms with below-median management scores were four times more likely to use little or no analysis when making important business decisions.

This reveals an important point.

Continuous Improvement is not simply a manufacturing methodology. It is a management discipline built on measurement, analysis and response.

A business cannot consistently improve a process it cannot measure accurately.

Nor can it sustain an improvement if the data required to monitor that process arrives several weeks after the activity occurred.

When reports are retrospective, improvement becomes reactive. Managers investigate after cost has already been incurred, production has already been delayed and margin has already been lost.

Operational data must therefore move at the speed of the operation.

From Lagging Indicators to Operational Intelligence

Traditional management reporting is dominated by lagging indicators.

These show the final result:

• Monthly labour cost
• Total production output
• Scrap value
• Overtime expenditure
• Gross margin
• Delivery performance

These measures remain important, but they often reveal the outcome after the opportunity to intervene has passed.

Operational intelligence provides the underlying explanation.

It examines the activities that created the result:

• Labour consumed by job and operation
• Actual time compared with standard
• Machine utilisation and interruption
• Productive and non-productive activity
• Changeover performance
• Scrap and rework by product or process
• Work in Progress
• Output by employee, team, line or shift
• The reasons behind operational variance

This allows management teams to move from asking: Why was last month’s margin lower than expected?
to asking: Which jobs are moving outside their expected labour and production cost today?

That is a fundamentally different management capability.

Five Principles for Better Manufacturing Data

1. Begin with the decision, not the dashboard

Manufacturers should not begin a data project by asking what information can be collected.

They should begin by identifying the decisions they need to improve.

For example:

• Should another shift be introduced?
• Is a particular product commercially viable?
• Which machine represents the greatest constraint?
• Why does one production line consistently outperform another?
• Is overtime increasing output or compensating for avoidable inefficiency?
• Which contracts are failing to recover their true labour cost?

Once the decision is clear, the organisation can determine which information is required to support it.

A dashboard containing dozens of measures may look impressive while still failing to answer the questions that matter.

2. Connect planned performance with operational reality

Most manufacturers already have standards, estimates and production plans.

The difficulty lies in comparing them consistently with what happened.

A production order should not remain an isolated record inside an ERP system. It should become a live operational entity against which labour, activity, output, downtime, waste and machine use can be attributed.

Only then can the business compare:

• Estimated cost with actual cost
• Standard hours with actual hours
• Planned output with completed output
• Expected margin with developing margin
• Scheduled completion with likely completion

This connection turns planning information into a mechanism for operational control.

3. Capture information where the activity takes place

Accuracy declines when operational information depends on memory or retrospective entry.

Employees may be asked at the end of a shift to remember when a job started, why it stopped or how long they spent on an unplanned activity. Supervisors may reconstruct events from paper records, spreadsheets and conversations.

Information should instead be captured as close as possible to the event.

That might involve:

• Shop-floor touchscreens
• Barcode scanners
• Mobile devices
• Machine or PLC signals
• Automated data feeds
• Simple employee declarations
• Supervisor validation

The objective should not be to burden employees with administration. It should be to make accurate recording a natural part of completing the work.

4. Translate operational variance into financial value

Operational measures become more influential when they are expressed commercially.

A 6% reduction in downtime is positive.

A £140,000 increase in recoverable capacity is a business case.

Reducing a changeover by 12 minutes is useful.

Showing that the reduction creates an additional production run each week gives the improvement strategic relevance.

Manufacturers should therefore connect productivity, downtime, waste and efficiency measures to:

• Labour cost
• Production cost
• Capacity
• Work in Progress
• Customer service
• Cash flow
• Margin

This allows finance, operations and senior management to work from the same evidence.

5. Close the improvement loop

Continuous Improvement should operate as a closed cycle:

• Define the problem.
• Measure current performance.
• Identify the causes.
• Implement the change.
• Measure the result.
• Control the improved process.

Too many improvement initiatives reach the implementation stage without establishing how the result will be measured and sustained.

Reliable operational information allows the organisation to distinguish between a temporary improvement and a permanent change in performance.

It can show what changed, why it changed, whether the change has been maintained and what commercial value has been created.

Technology Alone Does Not Create a Data-Driven Manufacturer

The evidence suggests a relationship between advanced technology adoption and business performance, although association should not automatically be interpreted as causation.

ONS analysis found that UK businesses adopting advanced technologies were associated with 19% higher turnover per worker after controlling for management-practice scores and other business characteristics. In the same study, 69% of firms had adopted cloud-based systems, 61% specialised software and 36% specialised equipment. Adoption was considerably lower for AI and robotics, at 9% and 4% respectively.

The lesson is not that every manufacturer needs an immediate AI or robotics programme.

It is that technology generates the greatest value when it is supported by structured management, reliable data and clearly defined operational objectives.

This is particularly relevant to the current enthusiasm surrounding artificial intelligence.

Make UK’s 2026 research found that only 2% of manufacturers had AI widely embedded across their operations. Operational adoption remained limited: 11% were using it in production, 7% in supply-chain activity and 6% in quality control. More than half identified skills shortages as the principal barrier to adoption.

AI cannot compensate for inconsistent job codes, unreliable standards, disconnected systems or missing operational context.

Before manufacturers ask what AI could predict, they must ensure the organisation can accurately explain what has already happened.

The foundation of industrial AI is not the algorithm.

It is trusted operational data.

Moving from Data Collection to Operational Intelligence

For many manufacturers, the next step is not to replace every existing system.

It is to create a connected operational layer between those systems, the workforce and the production environment.

This layer should be capable of:

• Receiving orders, routings, standards and cost information
• Presenting relevant instructions to employees
• Capturing activity as work takes place
• Combining employee, job, process and machine data
• Applying operational and financial rules
• Calculating performance down to the required level of detail
• Returning meaningful information to managers and authorised systems

Ceequel® Operational Intelligence has been designed around this principle.

It connects operational activity with data from existing business systems, machinery and the workforce. Information can be captured at job, employee, activity, process and machine level before being converted into measures such as labour productivity, job cost, Work in Progress, actual-versus-standard performance, downtime, OEE, cost variance and margin.

The purpose is not simply to produce more reports.

It is to create a consistent operational record that helps manufacturers understand what is happening, why it is happening and what it means commercially.

The Real Opportunity

UK manufacturing does not lack ingenuity, engineering capability or ambition.

Nor does it lack data.

The opportunity lies in turning the information already being generated into a more accurate understanding of operational performance.

Manufacturers that can connect planned activity with real-world execution will be better positioned to:

• Identify inefficiency earlier
• Use scarce skills more effectively
• Protect job and contract margins
• Improve production planning
• Reduce hidden labour cost
• Increase recoverable capacity
• Sustain Continuous Improvement
• Make investment decisions with greater confidence

The next productivity breakthrough may not arrive as a dramatic new machine or a revolutionary algorithm.

It may begin with something more fundamental:

A reliable understanding of who did what, when they did it, what was produced, what interrupted the process and what the activity cost.

Because better performance does not begin with more data.

It begins with making better use of the data manufacturers already have.