Industry 4.0 Technologies & Terms: IIoT, Industrial Big Data, AI, Machine Learning, & Digital Thread

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The Technologies, Processes, and Terms of Industry 4.0

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IIoT
The new digital era is driven by a web of connected devices or the “internet of things” (IoT). A Smart fridge, The Apple Watch, Nest’s home thermostat, and Amazon Alexa are all examples of independently connected devices that communicate data to other devices and people over the internet. The culmination of all “smart devices” makes up the Internet of Things.

Likewise, the Industrial Internet of Things or IIoT consists of millions of connected devices for manufacturing, packaging, warehousing, and shipping facilities. IIoT brings manufacturing machines to life by adding sensors to critical components that share data. This data feeds into business systems and is analyzed to determine production time, potential delays, and maintenance schedules.

Industrial Big Data
All the connected IIoT devices and other industrial data feed unfathomable amounts of data to various business systems. The aggregate of all of this collected industrial information is known as Industrial Big Data. Storage and networks are currently being deployed by manufacturing companies worldwide to handle the increase in data bandwidth.

Larger data storage capacity and networks with higher bandwidth capabilities are only one of many challenges Industrial Big Data has introduced. Another major challenge is the ability to analyze all of the newly created information and translate the information to the right machines and people at the right time.

Data Intelligence
Industrial Big Data requires companies to analyze data and turn it into usable insights to drive business decisions; this is data intelligence. The significant change Industry 4.0 brings to data intelligence is the sheer volume of data that sensors collect across various connected systems. The information is being communicated at a rate too fast for people to analyze it on their own.

Industry 4.0 AI
Artificial Intelligence (AI) & Machine Learning
The future of Industry 4.0 requires automated analysis of data and communication using artificial intelligence and machine learning. Artificial intelligence, or AI, simulates human thinking and decision-making using advanced algorithms. Machine learning uses past data to give more efficient and accurate outputs or complete specific tasks based on past data.

Machine to Machine Communication (M2M)
Machine-to-machine communication (M2M) is paramount for Industry 4.0. Machine-to-machine communication enables manufacturing machines to send data to other devices and software applications that use machine learning and AI to complete tasks.

For example, a machine on the manufacturing floor with a built-in thermometer can signal when the machine’s risk of overheating is high. The alert can trigger an algorithm that slows down all the proceeding machines in the production line, allowing the machine to cool down.

Simultaneously, the algorithm can activate a cooling system with the exact temperature to restore optimal production. The system can also send a push notification to the facility manager’s phone, showing her how the production slowdown will affect product output.

Finally, the system sends updated production data to other business systems updating supply chain, sales, and the executive team about delays and exact dollar losses from the slow down.

When machine-to-machine communication is operational, it enables a digital thread of communication to pass to the right people and devices at the right time.

Industry 4.0 and Digital Thread
Digital Thread
The digital thread describes a digital line of communication that breaks the barriers of traditionally siloed departments. Digital thread initiatives aim to push decision-making power to the lowest level possible by giving relevant information to the right person, in the right department, at the right time.

Digital thread pricing example
The digital thread looks, in practice, like someone in procurement adding pricing information to a purchased part in their ERP system. The system automatically updates the company’s PLM system. Now engineers can see pricing when searching for a component within their connected CAD program, enabling better decision-making.

Machine learning and artificial intelligence can analyze data from many departments and automatically push the data throughout the organization in a format that can be read by the appropriate machine or person receiving it. Moreover, this whole process happens in real-time.

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