AI for operations: OEE, quality and maintenance See losses where they occur
Many plants know their OEE as a monthly figure, but not per station and shift. We make availability, performance and quality measurable at that level. We track measures through to their effect and automate clear-cut inspections and reports.
Production & operations
DataClue applies AI and analytics in production and operations: for OEE analysis per station, the release of clear-cut inspection lots and the classification of fault reports. We build on existing MES and shop floor data and anchor the KPIs in your shop floor management. We track measures until their effect shows in the numbers.
Where production is under pressure today
- OEE losses cannot be measured per station, so people debate instead of deciding.
- Improvements start without a baseline. Later nobody can prove whether they worked.
- Specialists release inspection lots and sort fault reports instead of eliminating root causes.
Typical starting points in production and operations
Downtime, scrap and inspection effort cost output that shows up in no monthly figure. Your production management sets which action on the line comes first.
Losses and improvement
Downtime and scrap are recorded in notes and scattered spreadsheets, measures start without a baseline.
- Runs automatically
- Daily data flow from the MES, loss drivers per station across availability, performance and quality.
- Your team decides
- Which measures start and when one counts as effective.
Metric, e.g. technical availability
Quality and inspection
Every inspection lot is released by hand, even when everything is within tolerance.
- Runs automatically
- Release of lots that are clearly within tolerance.
- Your team decides
- Special release, block, rework.
Metric, e.g. inspection effort per shift
Maintenance
Many fault reports, the maintenance history sits in another system.
- Runs automatically
- Classifying clear reports, with the history of the asset.
- Your team decides
- Intervene or keep running.
Metric, e.g. response time per fault
Measure losses per station, track measures through to their effect
- Data-driven decisionsReporting from MES data with a fixed weekly review of measures.
- AI-supported workflowsClear-cut inspection lots and fault reports run under approved rules. Borderline cases go to quality and maintenance with all the data.
- Taskforce & expert deploymentWhen output, yield or quality do not rise as planned in a launch: experts on site who work toward agreed targets.
What this looked like in practice
- +18percentage points more technical availability, the biggest OEE leverData-driven continuous improvement: technical availability raised from 70% to 88%
- Above 80%good-part rate, from about 30% when the taskforce startedRamp-up taskforce: weekly output in a new-product launch increased more than tenfold
All client examples are anonymized.
Other functions
Production & operations
Frequently asked questions
What is OEE?
OEE (overall equipment effectiveness) measures how well an asset uses its planned production time. It is the product of availability, performance and quality. Knowing the three factors per station shows whether downtime, speed losses or scrap are costing you output.
Do we need a new MES for this?
No. We build on existing MES and shop floor data and embed the reporting in your daily shop floor routine. The initial assessment shows which data is missing.
What happens if a rule decides wrongly?
Every automatic decision is logged and measured against the agreed target. If a rule is too loose or too strict, it is adjusted with a documented reason. If the error rate reaches the agreed stop criterion, the workflow falls back to manual work.
What does the works council say?
Systems that log decisions can be subject to co-determination by the works council. We involve the works council early and disclose what is logged and why.
Contact
Where is your production losing output today?
Tell us about a line, an inspection or a fault that keeps costing time. We tell you openly whether data or AI makes a difference there.
