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Predicting Maintenance Means Planning Maintenance
More than 30% of life-cycle costs for rail operators are related to maintenance. Scheduled maintenance provides core backstop but does not always prevent unplanned events. An unscheduled issue can have a large impact on the overall network and customer satisfaction. Predicting these anomalies can increase reliability and at the same time reduce cost.
Freya’s data analytics team uses engineering principals to develop, predictive models using our customers maintenance, usage and sensor data. We have developed highly accurate predictive models that provide maintenance with the knowledge to greatly improve asset availability.
One customer reported that Freya’s predictive analysis was within 3-4% accurate over actual performance and provided more than $500,000 in savings for three consecutive years.
Developed a analytical model to identify key driver maintenance and prioritize work resulting in an 8% increase in equipment availability.
Over 10 years of support programs to maintenance.
Predicting the future maintenance needs has resulted in increased reliability and reduced cost for our customers and why Freya Systems we rapidly become their trusted advisor.
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