Lone Star’s real-time predictive model for compressed air services improved uptime and reduced costs by enhancing maintenance insights and scheduling predictions, enabling new service offerings and better service level agreements with no prior training data.
The Issue
A prospective company that generates compressed air as a service requires better uptime performance.
Needed to reduce PP-CBM maintenance costs.
Problems were hampering business development and delivery.
The Solution
Predictive Model Developed: Lone Star built a real-time predictive model to address these needs.
Data Collected: Compact platform to read equipment sensor and set point parameter data.
Data Analytics Delivered: Combined data with client-provided business rules to deliver analytics.
The Results
Actionable Insights Delivered: Solution provided maintenance insights more rapidly, reducing maintainer site visits
Improved SLA’s: Diagnostics were performed by the solution, improved service level agreements
Real-time Predictive Insights Provided Significant Improvements: Generated vastly better parts and labor demand and scheduling predictions
Super Charged Sales: Solution enabled a new business offering previously thought impossible.
