Software
Predictive Maintenance
Term from the vending glossary
Predictive maintenance: telemetry and sensor data are analyzed to forecast failures and schedule maintenance based on need — rather than at fixed intervals. In practice in 2026, this typically involves rule-based anomaly detection (vibration, temperature drift); true ML-based forecasting is only viable for large fleets (100+ units).
Predictive maintenance uses operational data to detect failures before they occur: rising brew times, more frequent reheating cycles, recurring error messages, deviations in the cooling curve. Rather than maintaining on a fixed calendar, maintenance is performed when the data indicates the need.
The benefit is greatest where downtime is costly — at high-volume locations or where service sites are far away. A prerequisite is collecting data over an extended period; two weeks of telemetry will not produce a reliable forecast. Those implementing predictive maintenance should measure first, then adjust maintenance intervals.