Industrial IoT (IIoT) is a subset of the Internet of Things (IoT) revolution that refers to the application of IoT principles, technology, and approaches, specifically in industry, manufacturing, energy and similar sectors. For all industries, IIoT ultimately aims first at gathering and analyzing data from factory sensors and devices, and then secondly to make intelligent responses based on data-driven insights. Automated real-time responses can be implemented to significantly streamline performance.
IIoT concepts are similar to other IoT concepts, in particular the networking together of numerous small devices, sensors, instruments, and actuators, to create the “internet of things”, a convergence of networking and device technology. However, IIoT differs from common IoT examples like smart homes, in both the degree and scale of technologies that are connected. Smart home sensors can monitor temperature, and send mobile device notifications in emergencies. Comparatively, in larger industrial settings, IIoT may orchestrate the operations and interactions of tens of thousands of devices, sensors, and robots. This difference requires more complex implementation methods, including using IIoT platforms, sophisticated device management software, and custom integrated automation tools.
Industrial internet of things empower manufacturers to leverage streaming data from all their connected devices and use powerful analytics to operate smarter.
IIoT connected devices create a platform to be able to rapidly roll out new features. IIoT grants the ability to remotely monitor, manage, and analyze device data. From a product perspective, this allows designers to understand how products are being used, and engaged with, delivering incredible insight into new product features, and importantly product shortcomings. From a marketing perspective, this insight can be turned into innovative offers, bundles, and value-adding opportunities.
Real-time performance monitoring is key to optimizing manufacturing processes. Traditionally, KPIs are used to baseline performance, and analytics can determine when deviations are detected from normal operations. Newer approaches now exist, like creating a virtual model of a physical asset to simulate its performance, known as Digital Twins.
IIoT enables the integration of digital supply networks by connecting systems between suppliers, distributors, and eventually customers. Technologies like RFID, which enables item-level tracking throughout the supply chain, help extend the capabilities of IIoT.
Predictive analytics uses the help of AI to process the massive volume of IIoT data and deliver forecasts that can ensure optimal maintenance thereby extending the lifetime of devices. In business scenarios, like expedited shipping, data from delivery vehicles can be used to forecast safety maintenance and keep vehicles roadworthy.
Smart factories are a modernized concept of manufacturing where automation and network connectivity have become enabling technologies that have greatly streamlined workflows. In these dynamic environments many technologies converge, Internet of Things provides a communication backbone for sensors, actuators, and bots to both transmit data, but also receive instructions to make adjustments to the environment automatically. Behind these decisions is AI and machine learning, and potentially a whole array of other systems connected to the smart factory via the internet, such as others in the supply chain who rely on that factory’s output.
Generally, smart factories are highly digitized manufacturing operations employing automation and extended features. Smart factories are also context aware. Network communications gather data from devices and the environment as part of a more autonomous and adaptable response to the entire system as changes occur.
A slew of supporting systems help make the smart factory capable of autonomy. Two main systems bring together the physical factory world with a virtual simulated world creating an Industrial IoT.
IIoT is about connecting industrial assets and control systems with information systems, people, and business processes. Based on IIoT information, smart manufacturing seeks to dynamically respond to changes in the supply chain through fully integrated manufacturing systems and processes.
The predecessor to IIoT systems is the distributed control system (DCS), which distributes localized autonomous controls throughout a factory. The significant advantage that IIoT has over DCSs is the integration of cloud computing to further refine and optimize process controls, offering a higher degree of automation. The Industrial Internet of Things (IIoT) carries with it several advantages: