Service · Operations intelligence
Predictive maintenance
Know which machine will fail, days before it does.
Relevant to Manufacturing
Machines fail when it hurts most: mid-shift, mid-order, with the spare part not in stock.
Your maintenance team already keeps a register of every breakdown. The pattern is in there; nobody has time to read seven years of it.
Typical for a mid-size plant: 4 to 9 percent of available hours lost to unplanned stoppages, plus emergency spares bought at premium prices.
- A few machines whose stoppage halts the line or the order.
- Someone has recorded breakdowns for at least two years, on paper or in Excel.
- The maintenance head wants this, not only the owner.
Probably not for you if
- A plant with many identical, cheap machines and spares on the shelf. Run-to-failure is the right policy there, and we will say so.
- Failure alerts days ahead for the machines that matter, ranked by likelihood and cost of stoppage.
- A maintenance calendar that moves from fixed intervals to actual condition.
- Spare-part planning based on predicted failures instead of last year's consumption.
- Models trained on your breakdown history and sensor or PLC readings, deployed on your network.
- 01
Link the registers
Breakdown register, production log and spare-part issues get one machine identifier. This takes longer than anything else, and it is where the project is won.
- 02
Reconstruct the normal days
Registers record failures, not the days a machine ran fine. We rebuild those from production records so the model learns both halves.
- 03
Start with rules, then a model
The first alerts are often rule-based, on the top three failure modes. A model replaces them when it beats them on your own history, measured.
- 04
Put alerts where the team works
A morning list on the maintenance board or on WhatsApp, with the reason for each alert in words, not a score.
- 05
Measure against the baseline
Unplanned hours per machine, before and after, over the same season. That number decides the gate at the end of the pilot.
- Maintenance and breakdown registers (paper or Excel both work)
- PLC, SCADA or sensor logs, even at coarse intervals
- Shift and production records
- Spare-part purchase and issue history
Illustrative example
Cut unplanned downtime 31% on a 12-machine line in 14 weeks
The machining line stopped without warning roughly twice a week. Each stoppage cost between two and six hours and, because the failing part was rarely in stock, sometimes a day. The plant had tried shorter preventive-maintenance intervals; that reduced failures a little and increased planned downtime a lot.
Read the case study| Duration | 2-week assessment, then a 6 to 10 week pilot on one line |
| Team | Engineering lead, data engineer, maintenance analyst |
| You receive | Running alert system, measured reduction in unplanned downtime, handover documentation |
| Price | Fixed price, quoted after the readiness assessment |
- Source code, pipelines and the model, running on your server or cloud account.
- A runbook written for a plant IT engineer with an hour a week.
- The alert thresholds and the retraining schedule, documented so your team can adjust them.
Everything runs in your environment and is handed over with documentation. Nothing stops working if we part ways.
Questions we are usually asked
Our machines are old and have no sensors. Can this still work?
Usually yes, with lower precision. Breakdown registers, operator logs and shift output alone often predict the top few failure modes. We tell you after the assessment whether it is worth adding a handful of sensors.
How much history do you need?
Two years of breakdown records is a reasonable minimum for a first model. Less than that and we start with a rule-based system and collect data properly from day one.
Does it replace our maintenance team?
No. It tells them where to look first. Our best results come from plants where the maintenance head co-owns the project.
Where does the system run?
On a server inside your plant network, or on your cloud account. Data does not leave your control.
What happens when the model is wrong?
Every alert carries its reason, so the team can dismiss it in seconds, and every dismissal is recorded. We publish the hit rate monthly and retrain when it slips. A model nobody can argue with is a model nobody trusts.
Often combined with
- Waste and qualityFind what actually causes scrap and rework, and reduce it.Operations intelligence
- ML operations and monitoringKeep the models, pipelines and assistants you already have running, honest and cheap, in your own environment.Data and platform
- Data foundationsOne clean data layer from six systems and three versions of Excel.Data and platform
Further reading
- Is your plant data ready for predictive maintenance? A 10-question checkBefore anyone sells you sensors, answer these ten questions about the data you already keep. Most plants can answer eight of them with a yes.6 min
- Running ML on-premise in an Indian factory: what actually brokePower cuts, a server under a desk, and a plant IT engineer with twelve other jobs. Notes on keeping a model alive where the cloud is not an option.7 min
- What five years of maintenance registers can tell you (and what they can't)The register is the most underused document in the plant. A guide to reading it as a dataset, and the limits of doing so.6 min
Not sure if your data is ready?
Take the free 20-minute readiness check. You get a short scorecard and a plain answer about what, if anything, is worth doing next.