Industrial IoT (IIoT) applications bring about many opportunities to increase operational efficiency by presenting personnel with timely insights into their operations. Visualizing IIoT data using visual analytics is a proven way to facilitate insight-driven decisions. So at the very least your IIoT initiative will start off by integrating IIoT connectivity, visual analytics and other system components. To best ensure early and ongoing success it is recommended that you follow the best practice of starting small, attaining quick wins and then increasing scope and/or scale.
The first step is to connect devices and systems and use visual analytics to create a simple visualization of your IIoT data. If the IIoT devices are mobile or geographically separated, then an appropriate visualization would be to display the location of the devices on a map such as shown above. This is an effective way to verify connections and validate successful integration.
The second step is to collect and intuitively visualize your IIoT data. At this point you can identify issues to make operational efficiency improvements. As an example, a freight trucking business can see a map with the locations and times of where their trucks are moving at slower than expected speeds. This information is used to change the routes on the fly to maximize on-time deliveries. As this example highlights, connecting to IIoT data streams and visualizing the data facilitates operational efficiency improvements.
The third step is to correlate data from different systems and data sources, including time series data from devices at different locations. Visualizing data correlated by time and location makes it possible to create comprehensive big picture views that reveal details about what happened and is happening, where, when, why and how. Using the trucking example, areas where driving speeds are consistently slower than expected are highlight by the red lines on the map above. This information is used to refine future routes, schedules and delivery commitments.
The fourth step is to apply advanced analytics to the IIoT data to generate insights for inclusion into the visualizations. Returning to the trucking example, advanced analytics will recommend the optimal average truck speed to minimize fuel costs based on the weight of the load they are carrying. Visualizing each truck using color coding to highlight the biggest offenders makes the analytics results actionable at-a-glance so that operations managers and drivers can improve driving efficiency. In the image above it is easy to see the truck icons colored yellow and red that represent the trucks that are traveling outside of the optimal speed range.
Having completed these steps you are positioned to leverage your IIoT infrastructure and expand on your competency by combining visual analytics, data correlation and advanced analytics in innovative ways to address business problems and facilitate operational efficiencies that would not otherwise be possible. Future blog posts will cover such combinations and the corresponding operational efficiencies.