Unplanned downtime with no early signal
Symptom
Maintenance runs on a calendar, yet the same three machines cause most of the unplanned stops.
Manufacturing Intelligence Lab
Where AI pays off on a manufacturing floor
Your MES, historian, ERP and quality records may already contain enough signal for a useful first test. We help you find it, measure the result and build the one use case worth scaling.
4 minutes · results on screen · no email needed
The challenges we see
Four patterns come up in almost every one of the plants we work with. If one sounds familiar, that is usually where to start.
Defects caught after value is added
Symptom
Defects are caught at final inspection, after material and machine hours are already spent.
Schedules rebuilt whenever reality changes
Symptom
The production schedule lives in a spreadsheet and is redone every time an order changes or a supplier slips.
Critical know-how is hard to find
Symptom
Setup tricks and fault fixes sit with two senior operators. When they're on leave, changeovers take twice as long.
Practical AI uses
Four uses we would consider first. Each one runs on data most teams already have.
Use case 01
In-line visual inspection
- What it does
- Scores images at one station for a defined defect class and flags suspect parts for operator review before the next process step.
- Data it needs
- Camera images from one station, defect labels or inspection decisions, and part or lot context where available.
- Time to first value
- 8–12 weeks
Use case 02
Operator and maintenance assistant
- What it does
- Answers questions from approved manuals, SOPs and fault history, with the source passage shown so a supervisor can verify it.
- Data it needs
- Machine manuals, SOPs, lockout and changeover instructions, and maintenance notes in PDF, Word or text.
- Time to first value
- 4–6 weeks
Use case 03
Failure-risk alerts for one asset group
- What it does
- Ranks assets by near-term failure risk from sensor trends and work-order history, so maintenance can inspect or schedule the next stop.
- Data it needs
- Historian or PLC/SCADA trends, alarms, runtime and maintenance work orders for the selected assets.
- Time to first value
- 10–14 weeks
Use case 04
Schedule suggestions for planners
- What it does
- Proposes a daily sequence using due dates, routings, changeover rules, materials and available capacity; the planner reviews and approves each release.
- Data it needs
- Open orders, routings, calendars, material status, changeover times and machine constraints from the ERP, MES or planning files.
- Time to first value
- 12–16 weeks
How we implement
Four steps, with a decision point after each. You can stop after any of them and keep what was built.
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Diagnose
Map one loss, its process owner and the data needed to measure it. Review the process with operations, maintenance, quality and IT.
You get: a ranked list of use cases, baseline measures, data gaps and a simple business case
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Pilot
Run one use case on one line with a baseline agreed before build. Keep a human in the decision loop and review results weekly.
You get: a working pilot, measurement report and go / no-go recommendation
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Integrate
Connect the approved workflow to the MES, ERP, CMMS or operator interface. Add access control, monitoring, fallback steps and training.
You get: a production system with an owner, runbook and monitoring
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Scale
Extend the proven workflow to similar lines or plants only where the data and process match. Your team owns the rollout; we support the next releases.
You get: a repeatable rollout playbook, adoption checks and handover
Expected outcomes
These are estimates, not client results. We confirm or revise each one against your own baseline during the diagnosis.
- Back-test of failure prediction on 12 months of your work orders. Assumes most stops come from assets with sensor data.
- Assumes the target defect is visible in the image and current inspection decisions are consistent.
- Based on the hours planners log rebuilding schedules today, measured during the diagnosis.
- Pilot and integration cost divided by the estimated monthly saving from the first three figures. Excludes sensor or camera hardware.
Questions plant managers ask
Something else on your mind? Ask us directly.
Do we need a data platform before we start?
Usually not. We can start with a controlled export or read-only connection from the systems you already use. The diagnosis checks whether the fields are complete, timestamped and joined well enough for the selected use case.
Can AI work with older machines?
Often, yes. Start with assets that expose PLC or SCADA tags or have useful maintenance history. If the selected asset lacks a usable signal, we compare a small retrofit sensor or manual capture during diagnosis before recommending new hardware.
Who owns the models and the data?
You do. Models, code and data stay in your environment or your cloud account. We don't reuse your data to train anything for other clients.
How much time does a pilot typically take?
Plan for about four hours a week from a process owner, a short IT setup for access, and brief reviews with the operators, maintenance staff or planners who use the result.
What if the pilot misses its target?
We agree the target and baseline before we start. If the pilot falls short, you get a written explanation of why, and you are not committed to the next phase.
Reviewed by Faruk Karagöl, CTO ·
Find out which use case fits your plant
18 questions, about 4 minutes. You see your score and top three gaps before we ask for anything.
