Predictive Maintenance & Asset Management

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Predictive Maintenance & Asset Management    

Predictive maintenance uses sensor data (vibration, temperature, current draw, acoustic signatures) and machine learning to estimate when a piece of equipment is likely to fail, so maintenance can be scheduled before an unplanned breakdown rather than on a fixed calendar or after something already broke. The category has two entry points that are worth telling apart when budgeting: dedicated sensor-and-analytics platforms (Augury, SparkCognition) that specialize in reading raw signals from rotating equipment and are usually layered on top of whatever maintenance system already exists; and CMMS-native AI (MaintainX, Limble, Fiix) that starts from work-order and asset-management software and adds AI features like anomaly flags, auto-generated SOPs, and image-based asset logging. The single biggest factor in whether predictive maintenance pays off is data quality — a model is only as good as the sensor data feeding it, and many first attempts fail not because the AI is weak but because the underlying data capture on the shop floor is inconsistent. Documented industry results across vendors report 30-50% reductions in downtime and 18-25% maintenance cost savings, but these numbers assume a reasonably mature sensor deployment; teams starting from paper-based maintenance logs should expect a data-capture project before an AI-prediction project. Pricing ranges from per-technician CMMS seats in the $8-$70/user/month range up to enterprise industrial-AI platforms priced by asset count and sold through a sales process.