Remote Site Device Health Monitoring for Proactive Healing
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Solution Overview
Problem
Hardware/system issues at remote sites, such as retail stores, result in system downtime and lost productivity, necessitating a reduction in help desk tickets.
Innovation Solution
A system that monitors health metrics of remote sites using machine learning and predictive models to identify potential failures, automatically generates service tickets, and dispatches technicians to address issues proactively, thereby reducing downtime and manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring and help desk tickets are used for remote site issues, then system issues can be detected and addressed, but system downtime and lost productivity increase
Solution Approach 1:
The system performs preliminary actions by continuously monitoring health metrics and using machine learning models to predict potential failures before they occur. This allows proactive maintenance scheduling and prevents system downtime by addressing issues before they cause outages.
Solution Approach 2:
The system enables self-service through automated health monitoring, predictive analytics, and self-healing capabilities. The system automatically detects issues, generates service tickets, and attempts self-repair without requiring manual help desk intervention, reducing both downtime and operational costs.
2Loss of information
If continuous monitoring of all remote sites is implemented, then system health visibility is enhanced, but data processing complexity and resource requirements increase
Solution Approach 1:
The system extracts only the most relevant health metrics and data points from remote sites for monitoring and analysis. By focusing on critical parameters rather than processing all possible data, the system maintains comprehensive health visibility while reducing processing complexity and resource requirements.
Solution Approach 2:
The system transforms raw health metric data into meaningful insights by changing parameters through normalization, aggregation, and predictive scoring. This allows the system to handle large volumes of monitoring data efficiently while maintaining high visibility into system health status across all remote sites.
Data Source
AI summary
A system, method, and apparatus are provided that include: receiving data corresponding to an operability status for each remote site of a plurality of remote sites, converting the data received into a structured data array for each remote site, storing the structured data array for each remote site into a respective group storage location, determining a state of health for each remote site based on the structured data array, generating a first dashboard user interface comprising site identifiers representing each remote site and the state of health for each remote site, rendering the first dashboard user interface via a display device, receiving a user interface selection of a select identifier of the site identifiers corresponding to a select remote site of the plurality of remote sites, and rendering a second dashboard user interface for the select remote site via the display device.