Window and door manufacturers operate in a difficult production environment.
Demand can fluctuate with the construction and home-improvement markets, while customers (both trade and public) still expect competitive prices, consistent quality and reliable delivery dates. At the same time, manufacturers must manage complex product ranges, skilled-labour constraints, rising operating costs and growing pressure to reduce waste.
The Construction Products Association’s Q4 2025 survey found that demand remained subdued across the construction products sector. Although sales volumes among lightside manufacturers continued to rise, the balance was the weakest recorded in 18 months.
Manufacturers therefore need production operations that can respond efficiently when demand changes. The answer is not always another machine, more overtime or an additional shift. In many factories, the first opportunity is to make better use of the capacity already available.
In this article, we share experiences from two UK window and door manufacturers to demonstrate how production data can help operations teams identify that capacity and make changes with greater confidence.
We appreciate that every factory is different, but from our experience, we see several challenges that repeatedly affect window, door and fenestration production.
A finished window or door may pass through cutting, machining, welding, fabrication, assembly, glazing and inspection. Different product ranges can follow different routes, while individual operations may depend on materials or components arriving at precisely the right time.
When output falls behind, the most visible problem is not necessarily the underlying constraint.
A machine may appear to be the bottleneck because work is accumulating in front of it. However, its lost production time could be caused by:
Material shortages
Long changeovers
Operators working across several processes
Quality checks
Tooling problems
Poor production sequencing
Upstream or downstream disruption
Without reliable production data, it is difficult to distinguish between a lack of capacity and poor use of existing capacity.
It would be fair to say that most manufacturers will already be collecting production information through job sheets, spreadsheets, ERP systems or even manual operator reporting, but the problem is that these systems often record what should have happened, or what was completed by the end of the shift, rather than what happened throughout production.
Measures of operator efficiency can be particularly misleading if they are used as a substitute for machine or line utilisation.
One of our window and door manufacturer customers found that its reported efficiency regularly exceeded 100%, despite output not reflecting the same level of performance. The figures were measuring operator activity against expected times, rather than showing how much productive time was being achieved by the equipment constraining output.
This created an apparent contradiction: reports suggested strong performance, while the factory still experienced bottlenecks.
The lesson is not that operator efficiency is irrelevant, it's that different measures answer different questions:
Operator efficiency helps assess labour performance against expected standards.
Machine utilisation shows how much available production time equipment spends adding value.
Throughput shows how much acceptable product the process produces.
Downtime analysis explains where and why productive time is being lost.
Senior operations teams need to understand the relationship between all four.
When demand increases, adding overtime or another shift can feel like the quickest route to more capacity. But extending production hours does not automatically improve output.
If the underlying losses remain unchanged, the factory can simply reproduce the same downtime, waiting and scrap across a more expensive operating window.
This risk is particularly relevant while labour costs are under pressure. Make UK’s Executive Survey 2026 found that nearly nine in ten manufacturers expected employment costs to rise, while energy costs remained a major concern.
Before increasing operating hours, managers should establish:
What output each shift is actually producing.
How much of the available time is productive.
Whether the same downtime patterns occur across shifts.
Whether materials, maintenance and supervision are equally available.
Whether production could be consolidated into fewer, better-performing hours.
For one window and door manufacturer, this analysis led to the removal of its night shift while maintaining output.
Window and door factories rarely produce one standard item continuously. Differences in frame material, dimensions, colour, hardware, glazing, machining requirements and batch size all affect production.
A daily utilisation figure may show that performance has changed, but it cannot always explain why.
Operations teams need enough context to compare:
Product families
Work orders
Batch sizes
Shifts
Machines
Changeovers
Good and scrap quantities
This makes it possible to determine whether poor performance is caused by the equipment, the production plan, the product mix or the way work is being organised.
Production, quality, maintenance and energy data frequently sit in different systems, or are owned by different teams., but in practice, these losses are connected.
A poorly maintained machine may create short stops and quality defects. An extended setup can increase energy consumed per completed product. A compressor leak can add cost without being visible in conventional production reports.
The Building Our Skills initiative also highlights the skills gap facing the fenestration, glass and glazing industry. When experienced people are difficult to recruit, reducing avoidable interruptions becomes even more important. Skilled operators should spend as much time as possible on the work that requires their expertise.
In this section, we're going to share the experiences of two FourJaw customers who demonstrate why operations teams should investigate existing performance before committing to more capacity. Unfortunately , we're unable to name one of them, but the other manufacturer is Lister Windows. Here's what they each discovered when using FourJaw's production monitoring system on their factory floors.
Lister Windows produces more than 3,000 windows each week. As demand increased, the company wanted to improve productivity without adding equipment or increasing its workforce.
Listers introduced FourJaw production monitoring across six production lines, including saws and welders. Machine utilisation, downtime and energy data were then incorporated into the company’s continuous-improvement activities.
This helped the team identify and target specific production losses. The resulting improvements included:
A 10% productivity increase across production lines.
A 12% increase in machine utilisation.
More than 250 additional windows produced each week using existing resources.
A 10% reduction in wasted energy.
Identification of avoidable energy losses, including an inefficient air compressor.
Operators also gained access to live information that helped them identify problems and respond more quickly, rather than waiting for retrospective reports.
Read the Lister Windows case study.
A UK manufacturer of aluminium windows and doors, composite doors, uPVC panels and plastic extrusions believed its CNC department was operating close to full capacity.
Every door passed through four CNC processes across seven machines, making the area central to factory throughput. As demand increased, the business expected that more machinery might be required.
However, its existing reporting relied heavily on manual, retrospective information. Managers had a good understanding of where problems might exist, but lacked the evidence needed to make high-risk decisions about equipment, shifts and capacity.
The manufacturer introduced real-time production monitoring across its CNC operations and began categorising the reasons machines were stopped. This gave production and maintenance teams a shared view of machine performance and allowed them to compare shifts and test changes.
The data showed that the opportunity was not simply to add more hours. By rebalancing production and improving the use of existing equipment, the company:
Increased uptime from approximately 30% towards 50% on key machines.
Removed the night shift while maintaining output.
Reduced premium labour and energy costs.
Brought scrap below its previous baseline.
Improved confidence in capacity and production-planning decisions.
The important point is not just that the manufacturer collected more data. Itsmanagers used that evidence to challenge an assumption about capacity, run controlled trials and validate a new operating model.
Manufacturers do not need to measure everything at once. We always recommend a focused approach to establish where the biggest opportunity exists before the project is expanded, for example:
|
Stage |
What to establish |
Practical action |
|
Choose the constraint |
Which process currently limits finished output? |
Select one important line or machine group rather than monitoring the easiest assets first. |
|
Establish a baseline |
How much available time is genuinely productive? |
Measure utilisation, throughput, downtime and scrap over a representative production period. |
|
Add context |
Why does performance change? |
Compare machines, shifts, work orders, products and batch sizes. |
|
Prioritise losses |
Which recurring issues remove the most capacity? |
Rank downtime by total lost time, not simply by how frequently it occurs. |
|
Test improvements |
Which intervention changes the result? |
Trial one change at a time and compare performance with the baseline. |
|
Standardise success |
Can the improvement be repeated? |
Update production routines, shift plans or maintenance processes. |
|
Review investment |
Is more equipment still required? |
Build the capital case using demonstrated demand, utilisation and constraint data. |
A common mistake is creating a long list of downtime reasons that operators struggle to use consistently., we recommend starting with a small number of categories linked to actions, such as:
Waiting for material
Changeover or setup
Quality or inspection
Tooling
Machine fault
Operator unavailable
Planned maintenance
No work scheduled
More detail can be added where it will help someone make a decision. If two downtime categories would lead to the same response, they may not need to be separate.
Factory-wide averages can disguise the process limiting finished output.
A high-performing machine does not compensate for persistent losses at a process through which every product must pass. Improvements should therefore be prioritised according to their effect on total production flow.
Ask: If this machine gained another hour of productive time, would the factory ship more products?
If the answer is no, it may not be the first improvement priority.
Shift comparisons should account for the products made, planned breaks, changeovers, material availability and support coverage.
The aim is not to create a league table of operators (unless you've got a team of operators that enjoy some friendly competition). It is to understand the operating conditions that produce the best result and reproduce them consistently.
Data creates value when it changes a decision or behaviour, for example a practical management rhythm could include:
A short daily review of the previous shift’s output and largest losses.
A weekly review of recurring downtime and improvement actions.
A monthly capacity review covering demand, throughput and constraints.
A defined owner and completion date for each agreed action.
This is also why operators need to be involved. They often know why production stopped; technology should make that knowledge easier to capture and act on, rather than being used simply to scrutinise individual performance. Check out our blog here on why engagement matters.
Production-monitoring technology such as FourJaw can automatically capture when saws, welders, CNCs (actually any machine that draw power!) and other production assets are running or stopped. Operators can add downtime reasons, while production teams compare performance across machines, shifts and work orders.
This is particularly useful in window and door factories with mixed-age machinery, where replacing equipment or integrating every machine into an MES may be impractical.
The purpose is not to produce more reports. It is to give operations teams reliable answers to practical questions:
Where is productive time being lost?
Which process is limiting factory output?
Why do shifts perform differently?
Is additional machinery genuinely required?
Can production be achieved in fewer operating hours?
Which improvement has delivered a measurable result?
Where is energy being consumed without creating output?
Research and case studies from the government-backed Made Smarter programme similarly show how real-time shopfloor data can replace delayed manual reporting and help manufacturers investigate downtime, energy consumption and differences between machines.
Window and door manufacturers do not need more data for its own sake. They need a sufficiently accurate view of production to make important decisions with less risk.
For some factories, that decision will concern overtime or shift structure. For others, it may involve a bottleneck, a recurring quality loss, energy waste or a proposed machinery investment. The two manufacturers featured here achieved different outcomes, but followed the same underlying process:
They measured actual production, identified the constraint, changed the way the factory operated and checked whether the change delivered the expected result.
That is the real opportunity presented by production-monitoring technology: not simply seeing the factory more clearly, but using that visibility to release capacity, control costs and improve delivery performance.
We hope you found this article useful, and remember, if production capcity is affecting your OTIF, you know where we are!