Manufacturing
From reactive maintenance to predictive operations: reducing critical equipment downtime.
Two constrained assembly lines set the pace for the entire plant network. Maintenance ran on fixed calendars, so healthy machines were serviced early while degrading machines failed mid-shift.
- Client
- Global discrete manufacturer, automotive and industrial components
- Region
- North America, Europe, Asia
- Scale
- 14 plants, 1,900 monitored assets
The challenge
What was actually going wrong.
- Unplanned stoppages on constrained lines absorbed overtime and lost saleable output every week.
- Condition data existed in a historian but was never joined to work-order history, so failure patterns were invisible.
- Maintenance planners distrusted earlier analytics attempts that produced alerts without lead time or context.
The AI solution
How it was engineered.
Asset-class failure models with edge inference and planner-approved work orders.
Signal foundation
Instrumentation audit first: we corrected sampling rates, tag naming and dead sensors before modelling anything, then built an asset-class feature store from historian and CMMS history.
Asset-class failure models
One model family per asset class rather than per machine, so a new machine inherits a working model on day one and only needs calibration.
Edge inference
Inference runs on plant edge nodes with store-and-forward, so predictions survive network loss and never depend on cloud round trips.
Planner-approved work orders
Every prediction becomes a draft work order with evidence, recommended window and confidence. A planner approves, edits or rejects — and that decision trains the next model version.
Delivery
The path to production.
10-week pilot on two lines, then multi-site rollout.
Weeks 1–2
Discover
Asset criticality ranking, signal quality audit, baseline downtime agreed with operations.
Weeks 3–10
Prove
Pilot on two constrained lines, shadow mode for four weeks, then planner-approved actions.
Months 4–9
Scale
Rollout by asset class across sites, edge deployment standardised, MLOps handover.
Ongoing
Operate
Drift monitoring, quarterly retraining, model ownership inside the reliability team.
The outcome
What changed, and how we know.
Reduced
Downtime on covered assets
Client-reported, figure withheld pending approval
4 wks
Shadow mode before authority
Trust built before automation
1,900
Assets under model coverage
After multi-site rollout
- Maintenance shifted from calendar-driven to condition-driven on all covered asset classes.
- Planners received warnings with enough lead time to schedule inside planned windows instead of reacting mid-shift.
- The reliability team now owns the models, with AsterMind AI in a support role.
“The models earned trust because maintenance teams saw the lead time they needed to act.”
What is next Extend coverage to utilities and tooling, and connect predictions to spare-parts planning.
More case studies
Other programmes in production.
Banking & Financial Services
Detecting financial crime in real time without drowning analysts in false positives.
Read case studyHealthcare & Life Sciences
Compressing regulated document drafting while keeping clinicians in control.
Read case studyGovernment & Public Sector
A sovereign caseworker copilot that shortens handling time and logs every step.
Read case studyStart here
What should your enterprise be able to do next?
Tell us the outcome you want to achieve. We will help you identify, engineer and scale the right AI solution.