Smart manufacturing originates from research on artificial intelligence. It is a human-machine integrated intelligent system jointly composed of intelligent machines and human experts, capable of carrying out intelligent activities throughout manufacturing processes such as analysis, reasoning, judgment, conception and decision-making. Industrial big data, by contrast, serves as the linchpin for the transition to smart manufacturing.

Smart manufacturing centered on Industry 4.0 has become the shared development direction for manufacturers worldwide. Different from the demands of general consumer markets, the industrial production sector features industrial artificial intelligence tailored for industrial scenarios, alongside the corresponding industrial big data for data collection. As the foundation of industrial artificial intelligence, access to accurate industrial big data directly determines the success or failure of manufacturing transformation and upgrading.

Unlike generic big data defined by the four Vs—Volume, Velocity, Variety and Veracity—industrial big data additionally places special emphasis on Visibility and Value. To distinguish regular big data from its industrial counterpart: metrics of data volume, collection speed/frequency, diversity and authenticity emerge naturally after manufacturers implement digitalization and automation. However, for Industry 4.0 and smart manufacturing, which drive the shift from equipment manufacturing to end-user services, Visibility and Value embody the core goals and significance pursued through industrial big data.

Amid the prevailing trend of digital transformation and industrial upgrading, most manufacturers advance digitalization, automation and smart manufacturing transition simultaneously. Digital and automated equipment generates massive volumes of data at high speed. Without comprehensive planning and phased construction, enterprises are highly likely to take missteps in the initial stage.

Industry insiders point out that commercial big data can be processed and analyzed on a fixed or periodic basis after large datasets are accumulated. In contrast, to deliver tangible value in smart manufacturing, industrial big data must be analyzed and processed in real time close to production equipment, with corresponding control feedback executed immediately. Real-time analytical results also need to be visualized for intuitive monitoring.

A critical distinction between industrial big data and conventional commercial big data lies in accuracy requirements. For commercial big data and AI applications, an accuracy rate of roughly 90% is already considered outstanding. Minor inaccuracies such as misjudging consumer age or pushing irrelevant ads rarely trigger severe consequences. In industrial production, however, industrial big data paired with industrial AI demands an accuracy rate of 99.9% or even higher. Any data deviation in manufacturing may lead to incalculable losses across subsequent production links.

The demand for high-speed, precise on-site analysis and processing of industrial big data has fueled widespread adoption of edge computing architectures alongside the rise of smart manufacturing. Industry practitioners explain that industrial big data collected locally on equipment can be prioritized for real-time analysis and feedback. This enables ultra-fast response while effectively reducing overall data throughput, lowering the burden of subsequent data transmission and storage.

Industry stakeholders note that without robust industrial big data support, outcomes will differ drastically across the full smart manufacturing journey, spanning on-site data collection planning, edge computing infrastructure deployment and the delivery of end-to-end integrated solutions. Meanwhile, industry players acknowledge that as smart manufacturing scales up, the scope of industrial big data will keep expanding while data sources become increasingly granular. Even so, industrial big data will only grow more pivotal to manufacturing’s smart transformation.

Source: Informatization and Software Service Network