Predictive maintenance and ops dashboards for a UK industrial manufacturer

An unplanned-downtime bill of roughly £4m a year rarely comes down to a missing sensor. This UK heavy industrial manufacturer, running four production sites, already had sensor packages fitted to its critical machinery from an earlier digitalisation programme. The telemetry existed. It simply sat in a data lake, unused, generating no decisions and no change in maintenance behaviour. That gap, between data collected and data acted on, is where the engagement began.

Rather than start by building models across the whole estate, the first two weeks were spent on diagnosis: which machines were actually driving the downtime, and whether the existing sensor streams contained enough signal to predict failure before it happened. The answer narrowed the problem considerably. Around 70% of the manufacturer's unplanned downtime was concentrated in just 12 pieces of equipment, and failure patterns on those machines were detectable up to 14 days in advance from data the business was already collecting. That single finding reframed the project: rather than instrumenting the whole estate, the task became predicting failure on the twelve machines that actually mattered.

Failure-prediction models were built for those 12 assets, using the existing sensor streams rather than commissioning new hardware. The harder design problem was not the modelling but the delivery: a prediction is worthless if it lands somewhere nobody looks. So the predictions were fed into the manufacturer's existing CMMS, generating maintenance tickets inside the tool engineers already used, rather than a new standalone system competing for attention. Alongside this, an operations dashboard was built for plant managers, bringing equipment health, throughput and predicted issues into a single view rather than the scattered reports that typically accompany sensor deployments of this kind.

That workflow choice, prediction into an existing tool rather than a new one, appears to be the reason the deployment stuck. Plant managers report using the dashboard daily, which is an unusual outcome for an industrial analytics tool; most such dashboards are checked occasionally, if at all, once the novelty wears off.

The results bear out the approach. Over the 12 months following deployment, unplanned downtime on the twelve covered machines fell by approximately 65%. The original business case had projected £2.5m in annual savings; the actual measured saving came in higher, at £2.8m. That gap between projection and delivery is worth noting in its own right: it suggests the diagnostic-led targeting of the highest-impact machines, rather than a blanket rollout, captured more value than the initial model assumed.

The wider lesson for manufacturers sitting on unused sensor data is that the constraint is rarely the data itself. It is usually the translation layer: turning telemetry into a specific, targeted prediction, and routing that prediction into a workflow people already use, rather than adding another tool to check. A two-week diagnostic that narrows a plant-wide problem down to a dozen machines is cheap relative to guessing at scale, and it is also what let a targeted, well-integrated deployment outperform its own business case.

For manufacturers considering a similar programme, the sequence here is repeatable: audit what sensor data already exists before commissioning anything new; diagnose where downtime actually concentrates rather than assuming it is spread evenly; build prediction models only for the highest-impact assets; and integrate outputs into the maintenance and management tools staff already use, so adoption does not depend on habit change. The measurable outcome, a 65% cut in downtime and savings ahead of the original business case, followed from getting that sequence right rather than from any single piece of technology.