About the Manufacturer
A UK-based automotive remanufacturer, specialising in fuel injection systems for passenger and commercial vehicles, wanted to increase throughput across its test benches, CNC machines and assembly areas.
With around 30 critical machines and rising demand from both OEM and aftermarket customers, the business needed to understand how much untapped capacity existed in its current operation before committing to additional shifts, headcount or capital investment.
By introducing FourJaw’s production monitoring system in a phased rollout, the team gained a clearer view of utilisation, downtime and lost production value, helping them prioritise improvement activity with confidence.
Results
Operational Challenges
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Spreadsheet-heavy performance tracking
The operations team relied on manual spreadsheets and periodic exports from their ERP system to understand how cells were performing. By the time data was consolidated, it was often out of date and difficult to act on.
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Large variation in utilisation across product families
Initial reviews suggested injector lines were running at 70–90% while pump lines were closer to 40–60%, but there was no robust machine‑level data to confirm this or explain why.
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Limited visibility of real downtime drivers
Teams suspected setup and changeover were a major source of lost time, yet there was no consistent way to measure or cost these losses across the 30+ machines.
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Pressure to prove ROI before expanding
Leadership wanted evidence that existing assets were being fully utilised before committing to extra shifts, more headcount or additional machines.
The Solution
The manufacturer introduced FourJaw in phases, beginning with 11 priority machines across key test rigs and CNC equipment.
Operators were trained to label downtime using clear categories and thresholds, helping ensure the data was credible, consistent and easy to act on. As the rollout progressed, downtime rules and measurement settings were refined collaboratively to focus on the losses that mattered most, including changeovers, setups and extended stoppages.
A key part of the implementation was attaching cost rates to downtime categories. This allowed the operations team to move beyond hours lost and start discussing downtime in financial terms, making it easier to prioritise improvement projects and communicate the opportunity to senior stakeholders.
The data was also used alongside the company’s ERP system to support capacity planning, board reporting and wider operational conversations with international partners.
The Solution
The manufacturer introduced FourJaw in phases, beginning with 11 priority machines across key test rigs and CNC equipment.
Operators were trained to label downtime using clear categories and thresholds, helping ensure the data was credible, consistent and easy to act on. As the rollout progressed, downtime rules and measurement settings were refined collaboratively to focus on the losses that mattered most, including changeovers, setups and extended stoppages.
A key part of the implementation was attaching cost rates to downtime categories. This allowed the operations team to move beyond hours lost and start discussing downtime in financial terms, making it easier to prioritise improvement projects and communicate the opportunity to senior stakeholders.
The data was also used alongside the company’s ERP system to support capacity planning, board reporting and wider operational conversations with international partners.
The Results
The engagement gave the manufacturer a much clearer picture of how its test and machining assets were really performing and where the biggest opportunities for improvement lay.
Clear machine utilisation baseline established
The first full dataset showed average utilisation across connected machines in the high 30% range, with some cells performing significantly better than others. This confirmed that there was meaningful headroom in the existing operation before major capital investment or additional shifts were required.
Setup and changeover quantified as a major loss
One early analysis identified more than 600 hours of setup-related downtime in a representative period. This gave the team a clear, data-backed target for SMED activity, standard work and process improvement.
Downtime data quality dramatically improved
On the best-performing machines, labelled downtime increased from roughly half of downtime events to more than 99%. This transformed the reliability of the insights and gave teams much greater confidence in using the data to make decisions.
Lost time translated into financial impact
By costing downtime on a weekly basis, the team could identify thousands of pounds in lost output in a single week and use that information to support sharper conversations around overtime, shift patterns and targeted investment.
Stronger shop-floor engagement
With operators consistently engaging with the system, daily performance conversations shifted from anecdotal feedback to evidence-based discussion. Instead of relying on assumptions about where time was being lost, teams could point to specific machines, categories and costed losses.
Summary
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Established a clear utilisation baseline across around 30 key machines, revealing significant untapped capacity.
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Identified setup and changeover as a quantified, high-impact loss area, with hundreds of hours of improvement potential.
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Turned raw downtime into weekly costed losses, enabling sharper decisions on shifts, overtime and targeted investment.
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Built operator engagement and a stronger culture of data-driven performance conversations on the shop floor.
