A survey conducted by Harris Poll on behalf of Google Cloud has found that the pandemic is driving up adoption of artificial intelligence (AI). The global survey of 1,154 senior manufacturing executives found that 66% of manufacturers are using AI on a daily basis.
The technology is being used to assist with business continuity (38%) and to help make employees more efficient (38%). Over a third (34%) of the manufacturers surveyed regarded the daily use of artificial intelligence and machine learning (ML) as helpful for employees.
Google’s study found that quality control and supply chain optimisation were the two main applications areas for AI in manufacturing. In the quality control category, 39% of the surveyed manufacturers using AI in their day-to-day operations used it for quality inspection and 35% for product and/or production line quality checks. In the supply chain optimisation category, the study found manufacturers were using AI for supply chain management (36%), risk management (36%) and inventory management (34%).
In April, Siemens said it had begun working with Google on AI-powered applications to support manufacturing. By combining Google Cloud’s data cloud and AI/ML capabilities with Siemens’ Digital Industries Factory Automation portfolio, Siemens said it was able to offer manufacturers the ability to harmonise factory data. This enables them to run cloud-based machine learning and AI models which can then be deployed as algorithms at the network edge.
Discussing the survey results, Dominik Wee, managing director of manufacturing and industrial at Google Cloud, said: “Many customers are not just interested in buying technology, but they are interested in how to solve a business problem.”
The focus of Google’s manufacturing and industrial arm is to help manufacturers improve operations by identifying business problems. Methodologies such as lean manufacturing and Six Sigma are widely deployed across the manufacturing sector. Wee believes AI is set to become a mainstream initiative too, but there were challenges to ovecome first. “AI is at the brink of becoming mainstream,” he said. “A lot of companies have done pilots, but most are having a hard time moving from the pilot to live.”
There are a number of challenges manufacturers face when trying to evolve a pilot to a large-scale roll-out of AI-based initiatives. The first, Wee he said, is the abundance of legacy technology, particularly on the shop floor. Over time, manufacturers acquire a lot of equipment and generally take a decentralised approach to running factories around the world.
Beyond the highly heterogeneous environment in manufacturing, Wee said: “Getting data in one place is difficult. This is harder because data is stored in different systems – some may not even be connected. Even if it can be collated, manufacturers still have to make sense of their data.”
Another challenge, which is common across many sectors, is the talent gap. This is another area Google sees as an opportunity to provide its technology and services. “Many of the people who work in manufacturing are not trained in deploying AI,” Wee added. “We are making the technology easier to use and we are working to upskill people.”
