In my experience, the core premise behind Industry 4.0 lies in the increasing ease of generating, classifying, storing, accessing and analyzing data. Data, today being generated in very high volumes, forms the core building block of Industry 4.0. When systems collect data, information is used by the machines to interpret it for improved performance.
Big data refers to the extraordinary volume of data that is constantly generated and made available for analytics. The sources and forms of data may vary but smart machines are designed to receive and interpret big data sets cohesively and produce a large number of insights.
The process of data collection and management is driven by the Internet of Things (IoT) – a network of devices built around electronics, sensors and actuators, connected to interact and exchange data. Industrial IoT (or IIoT) describes a set of machines and systems that are connected by the internet, in the context of running plant operations.
With sufficient instances of data collection and analysis, assets are being made smart enough to respond to data triggers by themselves – creating “self-reporting” and “self-performing” assets. Through this process of machine learning, the industrial world is being driven by smart machines that recognize patterns and flag problems; with quick solutions for them. On the assembly line, data drives the information pathway to address lags and delays more effectively than ever before.
Machine learning constitutes a major component of IIoT. With higher analyzed data fed into smart machines, they are able to function and respond better. This self-learning over time is the artificial intelligence that is built into the machine.