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Industry 4.0

IoT

The future is already our reality!

Industry 4.0 is revolutionizing the way companies manufacture, improve and distribute their products. Manufacturers are integrating enabling technologies, including the Internet of Things (IoT), cloud computing and analytics, as well as AI and machine learning, into their production facilities and across all of their operations. These smart factories are equipped with advanced sensors, embedded software and robotics that collect and analyze data and enable better decision making.

IOT

What technologies are driving Industry 4.0?

We are now in the fourth industrial revolution, also referred to as Industry 4.0, which is characterized by increasing automation and the employment of smart, data-informed factories to produce goods more efficiently and productively. Flexibility is improved so that manufacturers can better meet customer demands using mass customization, ultimately looking to achieve efficiency with, in many cases, a single small batch. By collecting more data from the shop floor and combining it with other company operational data, an intelligent plant enables better decisions to be made.

Internet of Things

The Internet of Things (IoT) is a fundamental component of smart factories. Machines on the shop floor are equipped with sensors that include an IP address that allows the machines to connect with other web-enabled devices.

Cloud Computing

Cloud computing is the foundation of any Industry 4.0 strategy. The complete implementation of smart manufacturing requires connectivity and integration of engineering, supply chain, production, sales and distribution, and service. Cloud helps make this possible.

IA and Machine Learning

AI and machine learning enable manufacturing companies to fully leverage the volume of information generated not just on the shop floor, but across their business units and even from partners and third-party sources.

Edge Computing

The demand for real-time production operations means that some data analysis must be done at the "edge", ie where the data is created. This minimizes latency time from when data is produced to when a response is needed. For example, detecting a safety or quality issue may require near real-time action with the equipment.

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