7 use cases of AI in manufacturing
According to the Annual Manufacturing Report 2018, 92% of senior manufacturing executives believe that “Smart Factory” digital technologies, including AI, will enable them to increase their productivity and empower staff to work smarter. However, there is a significant gap between ambition and execution: 58% of business and technology professionals are researching AI solutions but only 12% are actively using them.
According to Accenture and Frontier Economics, It is estimated that by 2035, AI-powered technologies could increase labor productivity by up to 40% across 16 industries, including manufacturing. AI could add an additional 3.8 trillion dollars GVA in 2035 to the manufacturing sector, which is an increase of almost 45% compared to business as usual.
“AI will perform manufacturing, quality control, shorten design time, and reduce materials waste, improve production reuse, perform predictive maintenance, and more.”
Andrew Ng, the co-founder of Google Brain and Coursera.
Looking for inspiration on how you also can benefit from this technology? Here are 7 of the most successful AI use cases in manufacturing:
1. Quality monitoring
Finding flaws in products is a time consuming process and it requires experienced human resources wasting valuable time to continuously inspect the quality of each manufactured piece. Machine vision allows machines to “see” the products on the production line and spot any imperfections. Thanks to AI algorithms the visual inspection process can be fully automated finding even microscopic flaws in products, recognizing defects, marking them, and sending alerts.
2. Predictive maintenance
Technologies such as sensors and advanced analytics embedded in manufacturing equipment enable predictive maintenance by responding to alerts and resolving machine issues. By analyzing this data artificial intelligence systems can draw conclusions regarding a machine’s condition and detect irregularities in order to make accurate predictions on the future state of each machine and suggesting corrective actions.
3. Generative design
Generative design is a process that involves a program generating a number of outputs to meet specified criteria. Designers or engineers input design goals and parameters such as materials, manufacturing methods, and cost constraints into generative design software to explore design alternatives. AI programs can learn from each iteration what works and what doesn’t and present the best solutions from a wide range of options in a short time. This can change the way a company delivers value to the customers and improve efficiency of processes.
4. Digital twins
A digital twin is a virtual representation of a factory, product, or service. The representation matches the physical attributes of its real-world counterpart through the use of sensors, cameras, and other data collection methods. To make digital twins work AI integrates the data gathered by smart components about the real-time condition status or position of physical items and matches it with the data of their digital representation. This pairing of the virtual and physical worlds allows analysis of data and monitoring of systems to head off problems before they even occur, prevent downtime, develop new opportunities and even plan for the future by using simulations.
5. Reducing environmental impact
AI could help to transform manufacturing by reducing, or even reversing, its environmental impact. Using AI technologies, factories can monitor in real-time the manufacturing waste and flag for instance toxic components in industrial waste water before it is released in the nature. AI can also help developing new eco-friendly materials and help optimize energy efficiency.
6. Raw materials management
AI can help figure out the right time to buy raw materials by making accurate price forecasts and combining them with the stocks data and utilization frequency. It can also automate the whole process of buying resources by watching the prices in the market and making optimal orders that are aligned with the manufacturing needs and the market data.
7. Robotics
Conventional robots need to be explicitly programmed to carry out every task it’s made for. AI will eliminate the need to program these robots by using learning techniques to teach any manufacturing task. This will transform the manufacturing robots industry and reduce the costs of setting up factories.
Conclusion
Artificial intelligence is a game-changing technology for any industry. As the technology matures and costs drop, AI is becoming more accessible for companies. In manufacturing, it can be effective at making things, as well as making them better and cheaper. The manufacturing industry has always been eager to embrace new technologies – and doing so successfully. Now, with AI adoption, they are able to make rapid, data-driven decisions, optimize manufacturing processes, minimize operational costs, and improve the way they serve their customers. This doesn’t mean that manufacturing will be taken over by the machines – AI is now an augmentation to human work and nothing can be a substitute of human intelligence and the ability to adapt to unexpected changes.