CAMS self-learning algorithms represent a fundamental shift in how asset management decisions are made. By continuously ingesting new data, our system builds comprehensive models that perpetually evolve.
Unlike traditional static models, CAMS uses machine learning to refine its understanding of how different asset types deteriorate under various conditions. This creates a virtuous cycle where each inspection improves the accuracy of future predictions.
The result is a system that grows more intelligent and valuable over time, providing increasingly precise lifecycle forecasts that help the optimisation of maintenance schedules and capital expenditure.
Asset inspection data is captured through our mobile app, and integrated systems. Each data point can be tagged with metadata including environmental conditions, usage patterns, and maintenance history.
Raw data flows through our validation mechanisms where it's cleaned and normalised. Advanced feature extraction identifies patterns and correlations that traditional methods might miss.
CAMS algorithms continuously train on the expanding dataset, refining their understanding of deterioration patterns. The system automatically identifies which variables most significantly impact asset lifespan.
As predictions are validated against real-world outcomes, the system accounts for any discrepancies. This feedback loop continuously improves accuracy, making each prediction more reliable than the last.
After an initial calibration period, CAMS has been shown to achieve up to 95% accuracy in predicting asset condition 3-5 years into the future. This level of precision enables confident long-term capital planning and maintenance scheduling.
Clients have reported an average 23% reduction in emergency repairs and unplanned maintenance costs. By accurately predicting when components will reach critical condition, maintenance can be scheduled during planned downtime.
With each inspection cycle, prediction accuracy can improve by an average of 2-5%. This compounding effect means your asset management system becomes more valuable over time, unlike traditional systems that degrade.
"Inspection time reduced by a third. Component count increased three-fold. The product and team that supports CAMS as a system are best in class and we have multiple certifications and industry awards that underpin or otherwise directly attributed to this work."
Brad Costello
Director, Facilities & Asset Management | RMIT University
RMIT University deployed CAMS across their facilities, which contain specialised buildings and equipment with complex maintenance requirements and high replacement costs.
Initially, the system was trained on a variety of asset data. Within 6 months, the algorithms had identified previously unknown correlations between usage patterns, environmental conditions, and equipment degradation.
After 18 months, the system was accurately predicting equipment failures up to 4 weeks in advance, allowing for scheduled maintenance during semester breaks and avoiding class disruptions.
See how CAMS can transform your asset management with intelligent predictions that improve over time.
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