製造業 IIOT 是什麼？

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.

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 systems — Cyber-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.

• 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.