Solution 03

Predictive operations

Forecasts and anomaly alerts that arrive early enough to act on, in the channel the responsible team already watches.

Outcome

Earlier failure warnings

Outcome

Demand and capacity forecasts

Outcome

Alerting into existing channels

Inside the solution

Demand and capacity forecasts

Horizon and granularity chosen to match the decision being made, not the data that happened to be available.

Alerts explain which signals moved and against what baseline, so the first response is action rather than investigation.

Early-warning models on sensor and maintenance history, tuned so the false-alarm rate stays credible.

Email, WhatsApp, Teams, Slack or your existing SCADA and CMMS — no new dashboard to remember.

Every model is replayed over your history so you see how it would have performed before you trust it.

Responders mark alerts useful or not, which drives threshold tuning instead of alert fatigue.

Scope of delivery

Indicative timeline

6-10 weeks from data access to alerts in production, assuming 12 months of usable history.

Questions we are asked

How much history do we need?: Twelve months covering at least one full seasonal cycle is a comfortable starting point. With less, we still build, but we set expectations against it and re-baseline as data accumulates.
Our sensor data is messy. Is that a blocker?: It is normal. Gap handling, unit normalisation and outlier treatment are part of the pipeline, and the readiness assessment tells you up front what will limit accuracy.
How do you avoid alert fatigue?: Thresholds are set from the cost of a miss versus a false alarm, responders rate every alert, and we review the ratio monthly rather than leaving defaults in place.
Do we need new sensors?: Often not. We start with what you already collect and recommend additional instrumentation only where the model shows a clear blind spot.