Industrial Internet of Things (IIoT)
What is the Industrial Internet of Things (IIoT)?
The industrial internet of things, or IIoT, refers to all kinds of industrial applications of IoT devices which gather data from their environment in plants, factories, and other industrial settings, and share it with other connected devices and analytical software.
IIoT takes advantage of the low cost and wireless connectivity of IoT devices to place them in as many places as possible, including items of equipment and the most hard-to-reach locations.
IIoT devices are particularly important for driving digital transformation in process plants, because plants have so many elements and moving parts, all of which need to be mapped and monitored to deliver data about plant conditions. Many parts of the plant are too dangerous or remote for human employees to regularly inspect them manually.
IIoT data is important for many use cases, like predictive analytics, supply chain optimization, and more. It helps process manufacturers achieve operational efficiency and ensure that quality and quantity of production remains high.
Why does IIoT matter to process manufacturing plants?
Process manufacturing companies can place IIoT devices in multiple places around the plant, and apply the data to numerous use cases, including:
Use IIoT data to create a “digital twin” or model an entire plant, so that you can examine it for bottlenecks and inefficiencies without interfering with the everyday workings of the plant itself.
Monitor supply chains to trace the movement of raw materials and completed products around the globe, gathering information about delays, handovers, and obstacles throughout the chain.
Connect IIoT data with machine learning (ML) to power predictive analytics solutions that detect the earliest signs of anomalies, issuing alerts that enable process plants to repair equipment before it breaks down and prevent unexpected downtime.
Embed IIoT devices in manufacturing layers to help to automate production and maintain control over robotic manufacturing units.
Place IIoT sensors in utility pumps for waste, water, raw materials, etc. to automatically control the flow and pressure, as well as collect real-time data about systems performance.
How can process plants implement IIoT for maximum impact?
IIoT devices are crucial for providing the data which powers the most advanced, AI or ML tools in process plants. Process manufacturers need to prepare the plant before implementing IIoT devices.
Choose where to begin
Implementing IIoT in a process plant can be overwhelming, simply because there are so many possibilities. Start by identifying which areas can be most improved with real-time, reliable information, and move on from there.
Decide on your storage method
IIoT devices generate enormous amounts of data which are best stored in data lakes, or data repositories, in the cloud, but you’ll need to choose whether to connect sensors to the public, private, or hybrid cloud. Your choice will depend on the level of security and amount of storage space you need.
Acquire the resources to make the most of the data
To unlock the real value of IIoT sensors, you need to collect and analyze the data they gather, and then apply the insights to plant processes, equipment, and decision-making. Ensure that you have both the software and the human resources needed to carry this out, whether you use on-site employees or a remote analytical team.
Remove silos between departments
The data from IIoT devices is valuable for a number of projects which often span different departments. You need to prepare the ground for productive collaboration by improving communication between teams and removing barriers that prevent them from connecting.
What are the benefits of IIoT for process plants?
By attaching IIoT devices to all areas of the plant, gathering data, and applying AI and ML to the datasets, process plants can cut time to market, reduce waste in energy and raw materials, improve operational efficiency, increase resilience, and raise reliability across the organization.