Bsquare’s IoT Maturity Index lays out five stages that are commonly associated with IIoT technology adoption. Each phase builds on the earlier stage enabling businesses to drive maximum value. These stages are device connectivity, data monitoring, data analytics, automation, and edge computing. Most manufacturing organizations haven’t progressed beyond the stage of data analytics. These companies have to gauge the difficulties that they encounter in automating the processes. They can increase their ROI by automating the most repeatable tasks in the manufacturing process.
Since most manufacturers have moved to device connectivity and data monitoring stages, these businesses have become proficient at obtaining data and transmitting it to cloud databases for analysis. Many of these companies now use dashboard and visualization tools to raise awareness of the equipment status and organize simple alerts.
Businesses generate significant returns in the data analytics stage. It lays the groundwork for automation. At this stage, companies leverage dynamic rule-based logic to orchestrate complex actions across an organization, such as ticketing and inventory adjustment requests.
As the system detects abnormal conditions on a production line, it increases data collection and transmission. The system then executes a series of automated steps to correct the error, and automatically adjust operating parameters to minimize damage. It also sends a notification to the technician regarding the issue and repair urgency. To improve the potential for automation, manufacturers should take a pragmatic approach to help minimize organizational and individual resistance, by building the foundation for advanced capabilities. A successful IIoT initiative is a cross-organizational effort built upon business goals shared among many stakeholders. Creating a culture from the start will help keep parties engaged and focused on the ultimate reward. The company has to set a clear definition of what the IIoT project is intended to solve.
A company that has a data science team at their disposal can significantly increase its deployment quality. These teams often have experience with implementations helping business steer clear of common hurdles.
In the last stage, manufacturers can deploy edge computing that can bring the distribution of analytics and orchestration to the device level for higher computing power and quicker response to real-time conditions.