Case study · Edge AI · Pharmaceutical and cold-storage warehouse operator, Singapore

Cold-chain monitoring for pharmaceutical warehouses

Temperature forecasts 15, 30 and 60 minutes ahead, with a trust score for every sensor.

60minutes ahead: the longest temperature forecast horizon
The problem

What the client needed

Temperature, humidity and pressure readings came from sensors and SCADA, excursions were noticed after the fact, and nobody could say when a rack would leave its band.

The result

What runs now

The roadmap adds predictive maintenance, root-cause analysis and remaining useful life estimates for the cooling equipment.

Facility AItime-series storeforecasting and anomaly modelsmobile app
What we built

The system, step by step

1

Temperature forecasting on 15, 30 and 60-minute horizons, with an explanation of why the forecast moved

2

Anomaly detection with a ranked queue and a sensor-trust score, so a failing sensor is not mistaken for a failing room

3

A data preparation pipeline for missing values, duplicates and outliers, with the quality score shown to the operator

4

A recommendation engine, alarm prioritisation and a mobile app for live monitoring, alerts and acknowledgements

Regulations and standards
GDP cold-chain guidancePDPA (Singapore)

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