Who this is for
- Plants and facilities that installed sensors two years ago and still make decisions from walk-rounds
- Operations teams who find out about equipment failure from the production line stopping
- Managers responsible for sites they cannot see, who want the state of the frontline on a phone
- Anyone with a maintenance schedule based on the calendar rather than on the condition of the asset
The problem, stated plainly
IoT projects fail at the last step. Sensors get installed, data flows to a cloud account, a dashboard gets built, and then nobody looks at it, because a dashboard asks a busy person to notice something. The information exists and the decision still does not happen.
An aircraft cockpit solved this with the annunciator panel: nothing lights up until something needs a decision, and when it lights up it tells the crew what to do. The same design applies to a factory floor or a fleet of pumps. Alert on the exception, attach the action, stay quiet otherwise.
What we build
Sensor-to-cloud integration
Temperature, humidity, vibration, current, pressure and flow from new or existing sensors, streamed to the cloud, stored and made queryable. Retrofitting legacy equipment with clamp-on or wireless sensors is routine; we design the data model so the readings still mean something in five years.
Real-time dashboards built for the person on shift
Grafana, Power BI or a custom web view, with the layout designed around the decision the viewer has to make and readable on a phone in a plant. We spend more time on what to leave off a dashboard than on what to put on it.
Predictive maintenance models
Models that read the early signature of failure in vibration, temperature or current trends and estimate the remaining useful life, so maintenance is scheduled by condition rather than by calendar. The target is zero unplanned stoppage; the honest early result is usually a large drop in surprise failures and a clearer view of which assets deserve the attention.
Alerts with a recommended next action
When a threshold or model flags an anomaly, the alert goes by Slack, Teams, email or SMS to the person who can act, with the recommended response drawn from your maintenance history attached. Alert fatigue is a design failure, so thresholds, escalation and quiet hours are tuned with the people who receive them.
Where the aviation model shows
Aircraft have monitored their own health for decades: engine parameters trended after every flight, alerts prioritized by what the crew must do now versus at the next stop. Condition-based maintenance is the airline norm, not a novelty. We bring that discipline, and its caution about false alarms, to equipment that has never had it.
The general principle, that "be careful" is not a corrective action and the mechanism has to do the noticing, is in what aviation safety practice teaches other operations.
How an engagement runs
| Stage | What happens | What you get |
|---|---|---|
| Free consultation (30 min) | Inventory of sensors, equipment and the data you already collect | A proposal for what to do with the data you have |
| Workflow improvement, implemented (from ¥50,000, about 2 weeks) | Existing data analyzed; a first dashboard and alert rules | The first alerts running, and the gain measured |
| Whole-operation architecture, implemented (from ¥300,000) | Sensor integration, predictive models, alert routing | A monitoring system your team owns |
| Operate and improve (from ¥20,000 / month) | Threshold tuning, model retraining, new assets onboarded | Alerts people still trust a year later |
Figures are indicative. Where you have no sensors yet, we start with a walk-through of the equipment to decide which measurements would actually change a decision.
What this will not do
Predictive maintenance needs failure history to predict failure. If an asset has never failed in the data, the model can flag abnormality but not name the fault. We are also careful about the difference between a correlation in sensor data and a cause on the machine; the model proposes, the maintenance engineer confirms.