Use Case · Manufacturing
Maintenance
& Asset Care
From reactive firefighting to planned maintenance. Work orders created automatically, spare parts ordered in advance, downtime predicted before it happens.
Why Maintenance Is a Lever
Unplanned downtime costs £1,000-5,000 per hour
In a typical manufacturing operation with 20-50 machines, 200-500 unplanned stoppages occur per year.
Each stoppage lasts on average 2-6 hours, not because the repair takes that long, but because the spare part is not in stock, the right technician is not available, or nobody immediately knows what is faulty.
Maintenance planning often runs on spreadsheets, Outlook calendars or paper sheets on a whiteboard. Intervals are planned by calendar, not by condition. The result: some machines are maintained too often (cost), others too rarely (breakdowns). Spare parts are ordered when they run out, not before they are needed.
And the documentation: handwritten maintenance logs that nobody digitises. Machine history spread across 3 systems. At audit time: panic. The real problem is not the machines’ technology, it is the organisation of the maintenance.
How the Process Changes
Before / After
unplanned machine stoppages per month
The Solution in Detail
How We Automate Maintenance
01
Condition Monitoring & Anomaly Detection
Sensor data from machines is continuously analysed: vibration, temperature, current draw, pressure, oil quality. The AI agent learns each machine’s normal behaviour and detects deviations days before a failure occurs.
02
Automatic Work Order Creation
On detecting an anomaly or a due maintenance interval, the AI agent automatically creates a work order with machine data, fault description, required spare parts and maintenance instructions. No manual entry in the CMMS.
Integration with SAP PM, Dynamics 365 Field Service or CMMS. Automatic assignment to the right technician based on skills and availability. Priority by criticality and production schedule.
03
Spare Parts Management & Pre-Ordering
For every work order, the system automatically checks whether the required spare part is in stock. If not, an automatic order is raised with delivery date matched against the maintenance window. No more emergency orders, because the part is already there when the technician arrives.
Linking of machine components to spare part master data. Minimum stock levels automatically calculated. Order triggered on breach. History-based consumption forecasting.
04
Maintenance Dashboard & Machine History
Central dashboard: machine status, scheduled maintenance, open work orders, spare parts availability, technician utilisation. Complete machine history at the touch of a button, for internal reviews and audits.
Power BI dashboard. KPIs: OEE, MTBF (Mean Time Between Failures), MTTR (Mean Time To Repair), unplanned vs. planned downtime. Drill-down per machine. Trend analysis for investment decisions.
Results
What Maintenance Automation
Typically Delivers
−40%
downtime
99%
availability
−25%
costs
+8%
OEE
The greatest lever is predictability. A planned stoppage of 2 hours at the weekend costs almost nothing. An unplanned stoppage of 2 hours on Monday at 9am costs £2,000-10,000.
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Ready?
How many unplanned stoppages does your production have per month?
Let us look in 30 minutes at which machines cause the most breakdowns, and how condition-based maintenance and automatic work order management can increase your availability.
No sales pitch. Just an honest assessment.
120+ Clients. 100% Satisfaction. 7 months to Profitability.
WHAT YOU GET IN THE DISCOVERY CALL
Identify your best use cases
Based on your industry and process landscape
Calculate concrete ROI
In Euros, FTE equivalents and time savings
Show examples from your industry
Real results of comparable companies
Define timeline and next steps
Concrete roadmap, no vague promises