2021-09-29

Building AI into the factory of the future

by Vawn Himmelsbach, a technology and business writer
Montreal, Quebec - September 28, 2021
Manufacturing

Any IT director working in the manufacturing sector knows that uptime and quality control are top priorities. Disruptions and downtime cost money, and a stoppage could add up to millions of dollars in high-speed, high-frequency production lines.

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But with the convergence of technologies — including the Internet of Things, data analytics, automation, machine learning and artificial intelligence — ushering in the era of Industry 4.0, it’s possible to optimize production lines while mitigating risk.

“Not long ago, optimizing cost and time was the overarching objective in the design of global manufacturing footprints, supply chains, and logistical support,” according to a recent report from the Boston Consulting Group (BCG). Prior to the pandemic, inventory and excess capacity were equated with waste, but in this new environment, redundancy and resiliency are critical components of risk management.

AI can help manufacturers optimize their costs through predictive maintenance and better planning, according to the report. But, “companies must apply AI at scale to unlock the value of data, and they must operate in an agile manner at scale to enable nimble, data-driven teams.”

This means AI projects are more likely to be successful if they incorporate change management. Joint research by BCG and MIT recommends dedicating 10 per cent of AI investment to algorithms, 20 per cent to technology and 70 per cent to business process transformation.

To get that support, however, IT directors will need to prove to the C-suite how AI can solve specific business problems.

“A lot of people are trying to do lots of different AI initiatives, but the most successful ones are those focused on solving a core business problem,”

Clement Bourgogne, VP Strategic Programs at Scale AI, Canada’s artificial intelligence supercluster.

He recommends that IT directors look for ‘unicorns’ to build a foundation for AI — rather than rolling out several small initiatives that aren’t highly impactful for the organization. For example, Scale AI is working with Bombardier on one of these ‘unicorns’ — to develop AI models that will better predict demand for spare parts for aircraft maintenance. This will reduce the risk of supply shocks‚ ultimately benefitting all stakeholders in the supply chain.

Two key areas where IT directors can earn wins are uptime and quality control, both key concerns of the C-suite. Other ‘unicorns’ could be applications around buffer inventory, warehouse management and logistics management, which have been particularly important since the start of the pandemic.

When it comes to quality control, AI-powered camera solutions will help to automate visual quality inspection (rather than using humans). Using AI, the cameras could identify not just major defects, but also smaller defects that don’t require shutting down the entire production line — maybe scratches on the packaging or the wrong colour saturation on the product labelling. While none of these are major defects, they affect the overall quality of the product or brand.

In the case of a major product defect, AI could help determine what type of problem it is and which department can fix it. Traditionally this has been done by a skilled operator who has the experience to assess defects and perform quality assessments. AI can absorb decades’ of data and reproduce that same skill set, “so it cuts down the time it takes to solve quality assurance and quality control,” says Bourgogne.

While IT directors need to build a business case for AI in manufacturing, they also need to explain to the C-suite that it takes time to deploy AI and to truly assess its impact — up to two or three years. And basing its success solely on financial metrics would also be a mistake.

“Like any new tool, AI defines a new way of doing things, So you have to use the first project as a bit of a lighthouse.”

Clement Bourgogne, VP Strategic Programs at Scale AI, Canada’s artificial intelligence supercluster.

Its success will be defined by the momentum it creates within the organization to continue adopting AI and to increase its scope in different facilities or business units.

Using AI to build more flexibility and agility into operations can help manufacturers better respond to fluctuating supply and demand, ultimately helping to create ‘future-forward’ factories that can better withstand disruptions and economic downturns.

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Made possible through the
financial support of
Gouvernement du Québec
Gouvernement du Canada