A new report from ABI Research forecasts widespread growth of artificial intelligence (AI) in the industrial manufacturing sector, with the total number of AI-enabled devices in the industry reaching 15.4 million by 2024 and an annual growth rate of 64.8 percent from 2019 to 2024.
“AI in industrial manufacturing is a story of edge implementation,” says 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 devices, 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 seamless as expected. Among all applications, 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 applications alone is expected to reach 9.8 million and 6.7 million, respectively, by 2024.
Many of these AI-enabled industrial devices support multiple uses on the same device due to advancements in AI chipsets. Key startups such as Uptake, SparkCognition, FogHorn and Falkonry are introducing cloud- and edge-based solutions that monitor the overall performance of industrial manufacturing assets and process flows.
Another commercial use currently gaining momentum is defect inspection. The total installed base for this application is expected to grow from 300,000 in 2019 to more than 3.7 million by 2024. This implementation is popular in electronic and semiconductor manufacturing, where major manufacturers have been partnering with AI chipset vendors and software providers to develop AI-based machine vision to perform component-level defect detection.
Conventional machine vision technology remains popular in manufacturing due to its repeatability, reliability and stability. However, the emergence of deep learning technologies opens the possibility of expanded capabilities and flexibility. These algorithms can pick up unexpected product abnormalities or defects, go beyond existing issues and uncover new insights.
Currently, manufacturers face fierce competition in building and training in-house data science teams for AI implementation. Most AI professionals prefer to work with web-scale 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,” Su notes. “The diversity in AI use cases necessitates the creation of partnerships.”
For more information, visit www.abiresearch.com.