What Is IIOT In Manufacturing?

What is IIOT in Manufacturing?

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.

How is IIOT used in manufacturing?

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.

What is a smart factory?

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.

IIOT applications for smart factories

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.

  • Cyber-physical systemsCyber-physical systems are robots, process control systems, any device that integrates the physical world into the virtual. This means a convergence of physical, computational, and network processes. CSPs supply the material for creating virtual copies of physical processes for digital twins and other predictive analytics.
  • IoT systems — IoT is about connectivity, in particular connecting smart objects and devices such as sensors and automated controllers, extending their function. IoT systems interlink CPSs.

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.

Advantages of IIOT in manufacturing

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:

  • Real-Time Monitoring — IIoT systems are designed for real-time monitoring of all aspects of the system, and remotely.
  • Predictive Analytics — IIoT systems rely on innovative analytical systems to make adjustments to operations based on real-time demand, make optimizations, and predict when failures will occur so they can be addressed preventatively.
  • Asset/Resource Optimization — Data analytics on asset and resource usage helps to inform the accuracy of lifecycle management. Because lifecycles can be better understood and predicted, more control is gained over production uptime.
  • Remote Diagnosis — Remote access and control are key administrative features of IIoT manufacturing.
  • Unified Controls and Decentralized Autonomy — IIoT systems provide decentralized device autonomy to improve localized operations, but also a unified control dashboard so that full control is always retained.
IIOT in manufacturing use cases
  • Asset Tracking — Modern businesses rely on asset tracking technology to ensure optimal supply chains. IIoT devices and sensors coupled with RFID technology provides the ideal scenario for increasing machine to machine communications and reducing human interaction with assets, equipment, and inventory. Because IIoT can interact with item-level RFID tracking, production flows can be easily mapped, and accounted for, and laying the grounds for optimization.
  • Fleet Management — Transportation and logistics companies can benefit from IIoT enabled vehicle fleets. Fleet management software can minimize risks, improve productivity, and increase vehicle ROI while reducing costs. Real-time vehicle tracking, combined algorithmically with traffic data, can help teams manage their time with better efficiency. And data from vehicle sensors can be extrapolated into predictive preventative maintenance.
  • Smart Factories — A smart factory integrates all aspects of a factory into a unified operational ecosystem. IIoT is used to enable the factory, fleets, and equipment for real-time tracking. A manufacturing execution system (MES) can work in conjunction with IIoT, adding another layer of decision making. The MES combines data from IIoT with its own contextual data, like customer details, orders, products, recipes, billings, etc., to present a complete picture of operations. With this insight, employees can then make final informed decisions about operations.
Types of distributed file systems

Three distributed file system architectures are in use today. Client/server file systems are most common, and readily available. Decentralized file systems are often found in peer-to-peer community-based networks. And cluster-based file systems are useful in large datacenters.

  • Client/Server Architecture — The most common DFS today is the client/server, where files are managed by a file server, and files are stored on one of several servers within the distributed system. This model relies on remote access, where a client sends requests to work on a file that is physically located on a server, in contrast to downloading a file, and uploading an updated version if there are any edits.
  • Decentralized Architecture — In a decentralized architecture, files are dispersed throughout the participating network, and transferred peer-to-peer (P2P). This style is popular with torrent networks.
  • Cluster-based Architecture — Typical solutions for client/server architectures become burdensome when scaling to very large applications. Cluster-based architecture is deployed in large data centers, and uses more enhanced methods of data management, like file-striping, chunking, and parallelism, to make data access, transfer, and processing more efficient. The leading solutions in this space are Google and Hadoop