Whether it’s food and beverage, plastics, pharmaceuticals, or oil and gas, technology professionals need the right skills to keep up with manufacturing.
There is a skills gap in the manufacturing industry, with about 2.1 million jobs in the U.S. likely to be unfilled by 2030, according to Deloitte and the Manufacturing Association. May 2021 Report. Additionally, a survey conducted in December found that approximately 46% of 360 manufacturing decision makers 2022 Visual Component Report They were unable to invest in training programs to teach their staff new technical skills.
That’s probably because manufacturers have been slow to adapt their machines and workflows to cutting-edge technology, said Artem Krupenev, vice president of strategy at Augury, a company that uses artificial intelligence (AI) to optimize production processes.
“Manufacturing has a very physical environment.” Krupenev Said. “When you try to use technology to improve some part of the environment, you have to take that environment into account.”
Some companies are moving manufacturing operations back to the United States from China and Southeast Asia, and companies are struggling with labor costs, retraining, and knowledge transfer from retired employees. Krupenev Added.
In Salesforce Generative AI Snapshot Research SeriesIn a survey of 4,000 full-time workers across industries, 66 percent of respondents said their workforce lacks the skills to successfully implement generative AI.
Technologists need to acquire the right skills to prepare for advances in autonomy and generative AI. But technology is changing white-collar manufacturing jobs more than jobs that involve physically operating machines. Krupenev: “It’s the jobs of data scientists, process engineers, and other types of engineering that will be dramatically enhanced by AI…The cost of performing some of these tasks has become significantly lower than the cost of performing maintenance tasks.”
Digitizing machines requires training, says Vignesh Ravikumar, a partner at venture capital firm Sierra Ventures. “There is a demand for digitalization on the factory floor, but we found that employees were not trained to use the software and machinery.”
Gain insights into machine health using AI
Engineers need to learn how to use generative AI as co-pilots or advisors to study how machines work and inform decisions within production processes. Krupenev “For those entering the manufacturing space, the big opportunity is understanding how to train and leverage generative AI models and applications to get the right context and gain meta-level insights across production.”
He also recommends learning about personas within generative AI. rapid engineering, This includes creating prompt patterns and structuring the text to be understood by the generative AI model. “It’s an area of great opportunity,” he said. “It’s like adding a decision maker to help make business-level decisions across the organization.”
Practical experiment of AI in manufacturing industry
Ravikumar advised setting up practice tests specific to the manufacturing industry. “Learn to implement robotics and AI software. It’s not easy and depends on your environment. Taking the time to learn now and setting up a test environment will give you an edge.”
Tools like ChatGPT can help teach engineers how to code and integrate AI into their products.
It would also be beneficial to learn how computer vision technology works to program robots in factories. “Historically, programming robots to perform tasks has been a challenge, but improvements in computer vision are making it easier for engineers to program robots to suit their needs. ” Ravikumar Said.
There is also a course called Teach generative AIAccording to , courses are not standardized and can take at least six months to start, so you can easily fall behind in your skills. Krupenev. Therefore, you should try it yourself.
Krupenev As a foundation, we recommend taking courses in generative AI from services like Coursera, edX, and LinkedIn Learning. “That skill set is what is needed today to catalyze change across every part of industry and every part of manufacturing.”
Delivering AI capabilities as a service makes it easier for engineers to experiment with new AI tools, said Patrick Matos, chief product officer and co-founder of DeepHow, an AI-powered video platform for skilled workforce training.
“AI is becoming more accessible because there are different tools and platforms available for experimentation,” Matos says. “Many AI capabilities are available as services or APIs, making it possible to seamlessly integrate AI into your products without requiring deep AI knowledge.”
