Research Reveals What’s Needed for AI Acceleration in Manufacturing
While the promise of artificial intelligence transforming the manufacturing industry is not new, long-ongoing experimentation hasn’t yet led to widespread business benefits. Manufacturers remain in “pilot purgatory,” as Gartner reports that only 21% of companies in the industry have active AI initiatives in production.
However, new research from Google Cloud reveals that the COVID-19 pandemic may have spurred a significant increase in the use of AI and other digital enablers among manufacturers. According to data from more than 1,000 polled senior manufacturing executives across seven countries 76% have turned to digital enablers and disruptive technologies due to the pandemic such as data and analytics, cloud, and artificial intelligence (AI). And 66% of manufacturers who use AI in their day-to-day operations report that their reliance on AI is increasing.
The top three sub-sectors deploying AI to assist in day-to-day operations are automotive/OEMs (76%), automotive suppliers (68%), and heavy machinery (67%).
Bryan Goodman, Director of Artificial Intelligence and Cloud, Ford Global Data & Insight and Analytics shares, “Our new relationship with Google will supercharge our efforts to democratize AI across our business, from the plant floor to vehicles to dealerships. We used to count the number of AI and machine learning projects at Ford. Now it’s so commonplace that it’s like asking how many people are using math. This includes an AI ecosystem that is fueled by data, and that powers a ‘digital network flywheel.’”
Moving from Edge Cases to Mainstream Business Needs
Why are manufacturers now turning to AI in increasing numbers? Google Cloud research shows that companies who currently use AI in day-to-day operations are looking for assistance with business continuity (38%), helping make employees more efficient (38%), and to be helpful for employees overall (34%). It’s clear that AI and machine learning (ML) technology can augment manufacturing employees’ efforts, whether by providing prescriptive analytics like real-time guidance and training, flagging safety hazards, or detecting potential defects on the assembly line.
In terms of specific AI use cases called out by the research, two main areas emerged: quality control and supply chain optimization. In the quality control category, 39% of surveyed manufacturers who use AI in their day-to-day operations use it for quality inspection and 35% for product and/or production line quality checks. Google Cloud often speak with manufacturers about AI for visual inspection of finished products. Using AI vision, production line workers can spend less time on repetitive product inspections and can instead focus on more complex tasks, such as root cause analysis.
The Golden Age of AI for manufacturing
“The key to widespread adoption of AI lies in its ease of deployment and use. As AI becomes more pervasive in solving real-world problems for manufacturers, we see the industry moving away from “pilot purgatory” to the “golden age of AI.” The manufacturing industry is no stranger to innovation, from the days of mass production, to lean manufacturing, six sigma and, more recently, enterprise resource planning. AI promises to bring even more innovation to the forefront” state Google.
To learn more access the full report here.
