This use case will take a deeper look at how Upstream AutoHealth’s predictive maintenance and data analytic capabilities can be used by service and utility fleet stakeholders as methods ...
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Automotive Data-Driven Predictive Maintenance
Upstream AutoHealth’s software-based and cloud-based technology utilizes telematics data already being collected from the connected fleet vehicles to help fleet stakeholders make highly informed real-time decisions for proactive quality and safety improvement and allows for increased vehicle uptime through prediction-based maintenance.
Your Fleet Maintained. Your Money Saved.
Unplanned downtime is of primary concern to commercial fleets, where vehicle reliability and availability are crucial to the business. Upstream AutoHealth’s agentless and cost-effective predictive maintenance technology can be deployed immediately, covering vehicles already on the road, to reduce fleet maintenance costs and maximize vehicle efficiency, ensuring that costly vehicle downtime is limited and customer retention and satisfaction grows.
Reduces unplanned vehicle downtime and preserves fleet finances and brand image.
Protects against spontaneous component failures which are dangerous to fleet drivers and road users.
Early detection of potential component failure enables cost-effective treatment of maintenance issues before they occur.
Not A “One-Size-Fits-All” Approach
Predictive maintenance has been on the minds of fleet operators and OEMs for many years – unfortunately, that dream has rarely been fulfilled. Classic predictive maintenance offerings approach the automotive industry with the same methodology and technology used for supply chains and other industry manufacturing processes. This one-size-fits-all approach to data and machine learning is both impractical and ineffective.
Upstream’s AutoHealth leverages our experience working with automotive data from millions of vehicles and telematics servers worldwide, ongoing automotive research, and deep automotive expertise. Our predictive maintenance technology utilizes telematics or OEM data already being collected from the connected fleet vehicles, which is then analyzed with machine learning algorithms, leading to root cause analysis, early detection of issues, and effective automotive-specific predictive maintenance recommendations.
With Upstream AutoHealth, fleet stakeholders are empowered to make highly informed real-time decisions for proactive quality and safety improvement and increase their vehicle uptime through automotive-only focused predictive maintenance recommendations.
Software-based Solution Enables Immediate Deployment
Upstream AutoHealth’s 100% agentless and cloud-based solution requires no in-vehicle components, allowing for immediate deployment, covering vehicles already on the road. The platform is built on a distributed microservice-based architecture that enables parallel automotive protocol analysis, correlating and analyzing multiple sources of data in real-time. The platform architecture provides high-availability cloud service with performance and scalability that support modern large-scale connected vehicles. Additionally, the service can be deployed on the user’s cloud.
Seamless Real-Time Data Integration
Upstream AutoHealth is a real-time, data-driven platform that aggregates and analyzes vast amounts of existing data already being transmitted between connected fleet vehicles or aftermarket telematics devices and telematics servers; no hardware installation in the vehicle is needed, as Upstream AutoHealth seamlessly integrates with existing TSPs.
Additionally, for data privacy and security regulation compliance, Upstream AutoHealth users have multiple configuration options for data anonymization, ranging from PII stripping to obfuscation and encryption on various elements of the data. Data anonymization can be set by the local administrator and is never accessible by Upstream.
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This use case will take a deeper look at how Upstream AutoHealth’s predictive maintenance and data analytic capabilities can be used by fleet stakeholders as methods for smarter fleet ...