Downtime you can attribute, not just count
Derive OEE directly from machine signals, detect stoppages lasting only seconds, and see which equipment and conditions are actually costing you output.
When downtime is only counted, it cannot be removed
Most downtime reporting says how much time was lost, not where it went or why. Manual logs miss short stoppages entirely, and line-level totals average away the machine that is really responsible. By the time a weekly report is assembled, the conditions that caused the losses have passed.
- Short stoppages never reach the downtime log
- Line-level reporting hides the machine responsible
- Causes reconstructed from memory rather than data
Turn lost time into a list of things you can fix
Move from counting downtime to understanding it, with losses attributed to equipment, condition and recurring pattern.
Recover hidden capacity
Detect stoppages of a few seconds that never appear in manual records but accumulate into significant lost output.
Attribute losses correctly
See which machine and which condition caused a stop, instead of averaging it across the line.
Fix causes, not symptoms
Group recurring stoppages by cause so effort goes to the patterns that cost the most time.
OEE built from what the equipment actually did
MontBlancAI reads machine states and process signals continuously, so availability, performance and quality losses are grounded in data rather than manual entry.
Signal-Based OEE
Availability, performance and quality derived from machine signals, removing the gap between what was recorded and what happened.
Micro-Stop Detection
Stoppages lasting seconds are captured and classified, making visible the losses manual logging cannot reach.
Cause Grouping and Ranking
Recurring stoppages grouped by cause and ranked by the production time they consume, so priorities are obvious.
See what signal-level measurement changes
Move from shift totals assembled by hand to continuous, attributable measurement of every stop.
Built for real production environments
See how this use case applies across different manufacturing processes and industries
Common questions about OEE and downtime
How MontBlancAI measures OEE from existing control-system data, and what it needs in order to do so.
How does MontBlancAI calculate OEE?
OEE is derived from machine signals rather than manual entry, so availability, performance and quality losses reflect what the equipment actually did.
Can MontBlancAI detect very short stoppages?
Yes. Stops lasting only a few seconds are detected at signal level. These micro-stops are usually absent from manual records but can account for a large share of lost capacity.
Do we need an MES to use this?
No. MontBlancAI works directly from control-system data. Where an MES or ERP exists, its context can be added, but it is not a prerequisite.
Find out where your output is going
