Artificial Intelligence (AI) has been portrayed as a technology that can revolutionize the industrial manufacturing sector. The sentiment is somewhat valid, but the scenario is complex. AI in industrial manufacturing is a collection of use cases at different phases of the manufacturing process.
According to the latest worldwide market study by ABI Research, the total installed base of AI-enabled devices in industrial manufacturing will reach 15.4 million in 2024 -- with a CAGR of 64.8 percent from 2019 to 2024.
Artifical Intelligence Market Development
"AI in industrial manufacturing is a story of edge implementation," said Lian Jye Su, principal analyst at ABI Research. "Since manufacturers are not comfortable having their data transferred to a public cloud, nearly all industrial AI training and inference workloads happen at the edge, namely on device, gateways and on-premise servers."
To facilitate this, AI chipset manufacturers and server vendors have designed AI-enabled servers specifically for industrial manufacturing. More and more industrial infrastructure is equipped with AI software or dedicated AI chipsets to perform AI inference.
Despite these solutions and the wealth of data in the manufacturing environment, the implementation of AI in industrial manufacturing has not been as easy as expected. Among all the industrial use cases, predictive maintenance and equipment monitoring are the most commercially implemented so far, due to the maturity of associated AI models.
The total installed base for these two use cases alone is expected to reach 9.8 million and 6.7 million, respectively, by 2024. It is important to note that many of these AI-enabled industrial devices support multiple use cases on the same device due to advancements in AI chipsets.
Another commercial use case currently gaining momentum is defect inspection. The total installed base for this use case is expected to grow from 300,000 in 2019 to over 3.7 million by 2024.
This is a use case that is popular in electronic and semiconductor production, where major manufacturers have been partnering with AI chipset and software vendors to develop AI-based machine vision to perform surface, leak and component-level defect detection, microparticle detection, geometric measurement, and classification.
Conventional machine vision technology remains popular in the manufacturing factory, due to its proven repeatability, reliability, and stability. However, the emergence of AI deep learning technologies opens the possibility of expanded capabilities and flexibility.
These AI algorithms can pick up unexpected product abnormalities or defects, go beyond existing issues and uncover valuable new insights for manufacturers.
Outlook for Industrial Manufacturing AI Applications
At the moment, manufacturers are facing significant competition in building and training in-house data science teams for AI implementation. Most AI experts prefer to work with webscale giants or AI startups, making talent acquisition a challenging task for industrial manufacturers.
"As such, they are left with one viable option, which consists of partnering with other players in the AI ecosystem, including cloud service providers, pure-play AI startups, system integrators, chipset and industrial server manufacturers, and connectivity service providers. The diversity in AI use cases necessitates the creation of partnerships," Su concludes.
According to the latest worldwide market study by ABI Research, the total installed base of AI-enabled devices in industrial manufacturing will reach 15.4 million in 2024 -- with a CAGR of 64.8 percent from 2019 to 2024.
Artifical Intelligence Market Development
"AI in industrial manufacturing is a story of edge implementation," said Lian Jye Su, principal analyst at ABI Research. "Since manufacturers are not comfortable having their data transferred to a public cloud, nearly all industrial AI training and inference workloads happen at the edge, namely on device, gateways and on-premise servers."
To facilitate this, AI chipset manufacturers and server vendors have designed AI-enabled servers specifically for industrial manufacturing. More and more industrial infrastructure is equipped with AI software or dedicated AI chipsets to perform AI inference.
Despite these solutions and the wealth of data in the manufacturing environment, the implementation of AI in industrial manufacturing has not been as easy as expected. Among all the industrial use cases, predictive maintenance and equipment monitoring are the most commercially implemented so far, due to the maturity of associated AI models.
The total installed base for these two use cases alone is expected to reach 9.8 million and 6.7 million, respectively, by 2024. It is important to note that many of these AI-enabled industrial devices support multiple use cases on the same device due to advancements in AI chipsets.
Another commercial use case currently gaining momentum is defect inspection. The total installed base for this use case is expected to grow from 300,000 in 2019 to over 3.7 million by 2024.
This is a use case that is popular in electronic and semiconductor production, where major manufacturers have been partnering with AI chipset and software vendors to develop AI-based machine vision to perform surface, leak and component-level defect detection, microparticle detection, geometric measurement, and classification.
Conventional machine vision technology remains popular in the manufacturing factory, due to its proven repeatability, reliability, and stability. However, the emergence of AI deep learning technologies opens the possibility of expanded capabilities and flexibility.
These AI algorithms can pick up unexpected product abnormalities or defects, go beyond existing issues and uncover valuable new insights for manufacturers.
Outlook for Industrial Manufacturing AI Applications
At the moment, manufacturers are facing significant competition in building and training in-house data science teams for AI implementation. Most AI experts prefer to work with webscale giants or AI startups, making talent acquisition a challenging task for industrial manufacturers.
"As such, they are left with one viable option, which consists of partnering with other players in the AI ecosystem, including cloud service providers, pure-play AI startups, system integrators, chipset and industrial server manufacturers, and connectivity service providers. The diversity in AI use cases necessitates the creation of partnerships," Su concludes.