Across a broad variety of applications, manufacturers are adopting AI and machine learning tools at a rapid pace. “Smart manufacturing,” a combination of AI and industrial IoT devices, is expected to develop into a $320 billion industry by the end of 2020.
Here are four manufacturing trends to watch or highlight today:
The dream of fully-automated manufacturing isn’t new, but reality started to take shape around the same time as the German government’s 2011 introduction of “Industry 4.0,” or I4. Laying out what’s considered to be the fourth industrial revolution, the key principles of I4 rely heavily on AI-assisted tasks: interoperability, information transparency, technical assistance, and decentralized decision-making to help monitor, record, and analyze the entire manufacturing process.
Major conglomerates and manufacturers like GE and Siemens are linking design, engineering, manufacturing, supply chain, distribution, and services together into single global systems that are intelligent and stable. New business models like MaaS (manufacturing-as-a-service) and PaaS (production-as-a-service) allow for large-scale customization of manufactured goods, using AI to process manufacturing cloud data to determine relevant information like the materials used in product design.
AI-enabled sensors are making the manufacturing process safer and more accurate. Machine vision tools like IBM Cognitive Visual Inspection, an AI-assisted camera that’s more sensitive than the naked eye, are used for finding manufacturing defects and improving productivity.
These technologies are enabling the rise of “cobots,” collaborative robots that can work alongside humans and understand instructions in plain English. Combined with the same motion-sensing technology as self-driving cars, cobots and machine vision-enabled manufacturing tools can sense and understand the world around them, preventing accidents and operating more efficiently.
Across the supply chain, AI is creating a broader picture of market conditions to help forecast manufacturing decisions. Machine learning is predicted to reduce supply chain forecasting errors by 50%, and improvements to forecasting will reduce lost sales by 65%.
AI can take into account geography, socioeconomic conditions, economic cycles, political activity, and even the weather to accurately predict product demand. By monitoring social media, algorithms can identify product trends that brands otherwise may not as easily find.
AI-assisted sensors are contributing to predictive maintenance, forecasting possible equipment breakdowns and recommending preemptive actions to keep machinery in working condition. From distributors to suppliers, sensors can be embedded throughout every step of the manufacturing process, ensuring equipment is optimized to its maximum efficiency.
Going one step further, virtual replicas called digital twins are being used to predict and gather data from virtual representations of real-world objects.
Manufacturing’s AI revolution will continue to streamline the entire manufacturing process by making it more accurate, reliable, and safe. Continued adoption of AI-enabled IIoT devices will continue to drive market growth, anticipated to reach $232 billion by 2023.
The proliferation of AI means many low-wage factory jobs will be replaced with technology, especially in developing countries like India and China. However, concerns about AI completely replacing workers appear to be overblown, as Gartner predicts AI will end up creating more jobs than it eliminates by 2020.