Operational Intelligence
Dashboards, analytics and triggers that turn machine data into a decision.
Detail sheet
How we build it
Every deployment is documented to the last terminal, so your team can own it the day we hand it over.
Collecting data is the easy half. We build the layer above it: real-time dashboards for the shift, performance analytics for the week, condition triggers that raise maintenance before a failure, and reporting that survives contact with a management meeting. Each view is designed around a decision somebody actually makes, because a dashboard nobody opens is an expensive screensaver.
- Views
- Shift, line, plant and multi-site
- Metrics
- OEE, downtime attribution, energy per unit
- Triggers
- Threshold and trend-based alerting
In detail
What you should know
How this building block behaves in a working plant, and where it fits.
One decision per view
Dashboards fail by trying to show everything. A shift supervisor needs to know what is stopped and why; a plant manager needs to know which line lost the most hours this week; a maintenance planner needs to know what is drifting. Those are three screens, not one.
We design each around its decision and leave the rest in the detail view, which is what keeps a dashboard in daily use after the novelty passes.
Downtime attribution before prediction
Most plants can name their worst machine but not their worst hour. Attributing stops to causes — with the reason captured at the line rather than reconstructed later — usually recovers more output than any model does in its first year.
It is also the dataset that makes prediction possible later. Skipping it is why predictive projects stall.
Triggers that people trust
An alert that fires wrongly three times is an alert everyone learns to ignore. Thresholds are set from observed behaviour rather than from a specification sheet, and each one is reviewed after a few weeks of real data.
Where a genuine model is justified we build it, and we are honest that most equipment fails too rarely to train one quickly.
Applied
Where it earns its place
- Real-time line status and andon for the shop floor
- OEE and downtime reporting with causes attached
- Condition triggers on vibration, temperature and current draw
- Energy per unit tracked against production output
- Multi-site comparison from a single view
What ships with it
- Dashboards scoped to named roles and decisions
- Metric definitions agreed and documented
- Alerting rules with thresholds and escalation paths
- Scheduled reports in the format your meetings use
- Handover so your team can add views without us
Common questions
- Do we need IIoT in place first?
- You need the signals. If your machines already expose them over a network, we can build on that; if they do not, the gateway layer comes first and we scope it honestly.
- Can it read our ERP as well as the machines?
- Yes, and it usually should. Output without orders and downtime without schedule context tells only half the story.
- How long before it is useful?
- Visibility dashboards change decisions within weeks because the data is already being produced. Anything predictive takes considerably longer and depends on how often the equipment actually fails.
Elsewhere in the stack
Other solutions
The layers above and below this one — most projects use three or four together.