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Field services involve tasks that are performed at the customer's location, such as installations, repairs, and maintenance. The industry has been rapidly evolving to increase efficiency and customer satisfactions by task automation. Now field service providers can bring productivity and customer experience to a new level by providing more advanced services with digitalization and cloud services such as Azure IoT Hub, Azure Data and AI services, and Azure Digital Twins.
There are published open source projects that provide ready to deploy solutions. For example, the Dynamics 365 Connected Field Service - Azure IoT Deployment Template is a solution that uses IoT sensors and data analysis to monitor and service customers' equipment, allowing companies to detect issues remotely and dispatch technicians efficiently. It integrates with Dynamics 365 to provide a comprehensive view of customer data, service history, and technician availability. For more details, please refer to the CFS for IoT Hub architecture and workflow description.
This GitHub repository contains multiple solution accelerators. Each solution accelerator focuses on one area, such anomaly detection, predictive maintenance, or intelligence derived from IoT alert.
The Anomaly Detection solution detects unusual patterns or anomalies in IoT telemetry data in real time. We provide multivariate anomaly detection solution that leverages Azure Cognitive Services. It collects IoT telemetry data and invokes API Services provided by the Anomaly Detector. The Anomaly Detector uses machine learning algorithms to analyze time-series data, such as IoT telemetry, logs, and business metrics, and identify anomalies based on historical patterns and trends. Anomaly Detector also provides a confidence score to help assess the severity of the anomaly, enabling field service providers to take appropriate actions.
For more detailed description of the solution and architecture, please refer to anomaly detection architecture and description.
To set up the solution accelerator after deployment, please follow the instructions provided by the anomaly detection deployment Guide
Predictive Maintenance solution uses real-time asset data (collected through sensors), historical errors and failure data, maintenance data, and advanced analytics to predict when an asset failure might occur. This allows maintenance professionals to act before equipment fails, reducing downtime and maintenance costs.
For more detailed description of the solution and architecture, please refer to predictive maintenance architecture and description.
To set up the solution accelerator after deployment, please follow the instructions provided by the predictive maintenance deployment guide.
Additional solution accelerators are under construction and planned to be published in the coming months:
- Insights and Analytics
- Digital Twins Enablement
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