Once the stuff of science fiction, artificial intelligence (AI) in manufacturing is now revolutionizing industries. According to an MIT survey, about 60% of manufacturers already use AI, although the U.S. lags behind Europe, China, and Japan. Besides, EY conducted a survey of more than 500 CEOs of leading manufacturing companies. AI was regarded as crucial to success by 86% of CEOs. However, only 30 successfully scaled AI and other emerging technologies.
While AI is used in many industries, it impacts manufacturing more than any other sector. Here's how:
AI in manufacturing uses the intelligence of machines to perform human-like tasks autonomously, which becomes a good fit because there are large quantities of data to analyze in a manufacturing environment.
Though "robots" are believed to replace workers who perform repetitive tasks, AI allows people and robots to collaborate to produce a large variety of products. In fact, many robots are now being programmed for collaborative efforts.
Nicknamed "cobots," these robots work alongside their human colleagues and take instructions from their human counterparts with simple language.
AI also frees personnel to spend time on non-repetitive tasks, such as designing, modifying, and solving issues. Of course, in the long run, as more jobs are displaced, many workers will have to be empowered to take on higher-skilled tasks like programming or maintenance.
AI is revolutionizing manufacturing because it can detect significant patterns in massive amounts of data much quicker than human capacity and respond to that information. This data analysis offers tremendous benefits for manufacturing companies.
Artificial intelligence was conceived as far back as the 1950s. However, it only entered popular consciousness and gained acceptance when machine learning processes allowed AI to discover patterns in a body of data to perform without specific programming (i.e., when robots could start learning and evolving for themselves).
Without machine learning algorithms, computers can only be used to perform preprogrammed tasks, which makes them simple machines. However, many tasks, especially those involving perception, can't be limited to specific rules and instructions. Regarding manufacturing, robots can only take on human jobs if they have a sense of perception and the ability to learn.
Machine Vision is one of these applications that makes sense of perception a reality. It's easy for manufacturers to develop more sensitive and better-trained cameras than the human eye. However, AI can identify patterns in the images and take actions based on them. Machine Vision can also train a robot to sense what's happening in its immediate environment and avoid dangers and disruptions, helping humans steer clear of obstacles.
An example of this technology is the automotive industry's adoption of self-driving vehicles with features like advanced emergency braking systems. This same technology can be applied to self-driving forklifts and conveyors so they can avoid obstacles and prevent workplace accidents.
The role of AI is to make manufacturing "smarter" by employing sensors and, as mentioned, collecting and analyzing data quickly. The data can then be used:
These prevalent trends in the manufacturing industry make the need for more efficient production more important: shorter time-to-market deadlines and more complex products. Considering these trends, any improvement in speed and efficiency that AI provides while maintaining quality can help manufacturers remain competitive or even outpace competitors.
Most manufacturing companies contend with high capital investments and slim profit margins, which is why cost savings are critical to success. In manufacturing, ongoing maintenance of machinery and equipment represents a significant expense and a negative impact on the bottom line. In addition, studies show unplanned downtime costs manufacturers $50 billion annually, and machinery failure causes much of this unplanned downtime. That's why predictive maintenance has become a cost-saving solution and another example of how AI is used in manufacturing.
Here's how it works:
AI in manufacturing is also being used to improve both precision and quality. Let's take some examples to explain to you how:
AI in manufacturing can also help improve supply chains by assisting companies in anticipating and adapting to market changes. This gives management a massive advantage by allowing them to make strategic decisions versus reacting to outside factors.
Imagine estimating demand for a product by looking at patterns in multiple factors that may impact your business (e.g., location, socioeconomic issues, weather, consumer behavior, etc.) and optimizing staffing, inventory, energy consumption, and raw materials to meet that demand.
This is a prime example of how AI is used in manufacturing as a collaborative tool. Over time, the algorithms can be analyzed concerning any factors that may impact the business and help management make strategic decisions that save time and money.
Data collection and analysis are already being used in many industries to predict consumer behavior and generate highly personalized communications to customers or potential customers. A recent study at Indiana University found that machine learning algorithms could even accurately analyze Twitter feeds to scan public sentiment and determine stock market movements.
Many experts believe AI ushers in a new era beyond the Information Age. While AI in manufacturing is already reaping numerous benefits, it's still in its early stages. The number of applications seems limitless.
According to a study conducted by Price Waterhouse Coopers, adoption of predictive maintenance technology among manufacturing companies is at 78%, adoption of manufacturing execution systems is at 73%, adoption of digital twins is at 60%, and finally, adoption of robotic process automation is at 59%. Meanwhile, the adoption of artificial intelligence is trailing behind at only 29%. Why? It seems that manufacturers are focusing on technology primarily geared towards cutting costs. However, experts predict that market demand and competitive pressure will necessitate the adoption of AI in manufacturing to not only cut costs but elevate productivity.
In addition to manufacturer hesitancy, there is currently a lack of skills to support this technology. AI expertise is in short supply but has great demand. IBM predicts that demand for data scientists will grow by 93% in the coming years, and demand for machine learning experts will grow by 56%. Companies are currently finding it challenging to fill specialized roles; some provide advanced training to their expert staffers.
Despite the risks and growing pains of adopting a new form of technology on a mass scale, AI is already making significant inroads across the globe and continues to grow. In fact, AI in manufacturing is expected to grow from $1.1 billion in 2020 to $16.7 billion by 2026 – that's a compound annual growth rate (CAGR) of 57%! With that said, it's time to create a smart factory of the future.