Summary
In this module, you learned about Azure Digital Twins through the lens of an IoT worker at a manufacturing company, tasked with designing a digital solution that factory operators can use to monitor the efficiency of robotic arms in your distribution centers.
You saw how you can enable this scenario using Azure Digital Twins, by creating models that define a robotic arm and a distribution center; creating digital twins to represent specific instances of these concepts; and connecting the twins using relationships to form a full graph reflecting your environment. You visualized the digital environment, and read about how to bring the graph to life with IoT data and business information that convey pickup success and overall efficiency information across the robotic arms in the factory. You ran several queries to identify arms that have experienced problems, and read about how data can be sent to Azure Data Explorer to enable historical queries over time.
Using Azure Digital Twins capabilities for modeling, querying, and visualization allowed you to design a custom solution that represented the data you're interested in, without having to build these features from scratch. Individual, aggregate, and historical data is organized and readily available for you to build into a dashboard for factory operators, which they can use to quickly identify when an arm misses a box pickup. They can use this information to recover missed packages with minimal downtime and investigate the historical efficiency of that arm. This will allow them to determine which arm machines may need maintenance to increase future productivity, which they can measure using the overall efficiency property computed by your Azure Digital Twins graph.
If this example scenario reflected aspects of your environment and business situation, Azure Digital Twins may be a good choice to help you digitally represent and gain insights from your IoT environment.