Establishment of cross-sectoral networks provides an affordable solution to help manufacturers fast-track processes to keep pace with AI technologies and realize far-reaching efficiencies at scale.
Manufacturers led the way in automation. But they are now faced with the complex challenge of how to keep pace with the swift and ever-evolving nature of new AI technologies.
The vast majority of manufacturers in Australia and the U.S. are small to medium-sized enterprises. An Australian Industry Group report in 2019 categorized 87 percent of Australia’s 47,530 manufacturing companies as small (1—19 employees), 7 percent as medium (20—199 employees) and 1 percent as large (200+ employees). Similarly, a 2019 SCORE survey defined 98.6 percent of American manufacturing companies as small to medium-sized enterprises.
Size poses unique challenges to successful AI adoption because innovation capacity is naturally limited by a small employee base.
To combat this issue, the Queensland AI Hub is developing a small to medium-sized enterprise program to identify lighthouse organizations demonstrating best practice in the creation and/or adoption of AI technologies and using them as the anchors for the formation of new communities of practice. The network will begin in Queensland and expand with our international partners, to be established later this year.
These lighthouse organizations do not have to be manufacturers to provide valuable lessons for manufacturing; they only need to ‘get’ AI. Best of all, building these communities enhances the internal innovation capacity of smaller manufacturers, letting them access resources and reduce associated costs and risks.
Establishment of cross-sectoral networks provides an affordable solution to help manufacturers fast-track processes to keep pace with AI technologies and realize far-reaching efficiencies at scale. The networks let newbies learn from successes and mistakes of other sectors leading the way in AI implementation.
Years ago, I worked for a metals manufacturing research center, CAST, which set up a best-practice program for smaller, local aluminum die casters. The resulting communities of practice model let the firms share information, such as energy usage, to help benchmark and improve their internal processes without the concern of giving away their competitive advantage.
The moral of the story? To harness the benefits of AI at scale requires manufacturers to strengthen their innovation capacity and, importantly, broaden their communities of practice to learn from other sectors that are successfully applying AI in different ways.
AI will fundamentally change manufacturing for the better, particularly in the area of product and process development. We can now use AI, in the form of evolutionary algorithms, to design materials for specific purposes and then integrate the new material into the design of new AI-imagined products and associated manufacturing processes.
The future will see ever-smarter machines applied to tasks that were once impossible for robots (opening doors, climbing stairs). While humans will continue to oversee the production process, there will be little need for them to be on-site, accelerating the trend toward lights-out manufacturing. Computer vision, combined with advanced sensor data, will pinpoint production problems before they occur. Robots will order parts and materials (or create them on-site) and conduct just-in-time repairs. Real-time quality control will mean defects will be detected and dealt with as they occur.
To get to that new reality quickly, manufacturers need to leverage the experience gained in other sectors experimenting with AI.
For more information visit: www.qldaihub.com
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