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:

• To make improvements to streamline production through increased digitization, interconnected devices, and enhanced systems.
• Management can also make decisions that improve efficiency based on real-time data. For instance, after employing AI and analyzing data, one large manufacturer of specialty gears could remove two steps in the manufacturing process by reconfiguring its operations, which not only improved the speed of production but also reduced costs by nearly $2 million annually.

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:

• By processing continuous data streams from sensors, AI can detect patterns and apply analytics to predict problems and alert maintenance before major issues occur.
• Sensors inside the machine can monitor sounds (e.g., grinding gears and belts wearing out) or detect wear on a tool before it malfunctions.
• AI in manufacturing can also predict how much life is left on a machine or one of its components. So, instead of an expensive appliance or part breaking down that causes downtime or replacement, AI enables preventive maintenance and repair. It can also extend machinery's remaining useful life (RUL) by scheduling focused maintenance.

AI in manufacturing is also being used to improve both precision and quality. Let's take some examples to explain to you how:

• AI algorithms can notify teams of emerging production faults that may cause quality issues, allowing them to address problems early on.
• The two trends we discussed earlier – shorter deadlines and higher product complexity – make it harder to maintain high-quality levels, especially for manufacturers bound to meet strict regulations and compliance standards.
• AI in manufacturing enables teams to collect data about the use and performance of products in the field. Product developers then use this information to make strategic engineering or design changes.
• Another example of how AI is used in manufacturing is by allowing more precise process design, problem diagnosis, and resolution when defects occur. This is done through "digital twins," replicas of a physical part, a machine tool, or the part being made.
• As an exact representation, a digital twin can help detect defects and illustrate how the part or machine will behave if a fault occurs. AI in manufacturing can also perform nondestructive testing (NDT), which can be very expensive.
• When combined with complementary technologies such as virtual reality (VR) and augmented reality (AR), AI in manufacturing can lead to better products through generative design. Generative design involves designers or engineers input design goals into an algorithm (generative design software). The brief generally includes information, such as material types, available production methods, budget constraints, and timing restraints. The algorithm then explores every possible configuration and suggests an optimal solution. In other words, AI in manufacturing can reduce design time and optimize processes, allowing manufacturers to improve speed and precision.
• AI in manufacturing can also improve quality through "zero touch." Humans make errors, and the more people working on an assembly line, the more opportunities there are for mistakes. While automation and robotics eliminate human error, AI ensures that even the slightest deviations are immediately detected. Combined with other digital technology, AI can enable "zero-touch" operations and, therefore, zero defects.

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.

• For instance, "machine-to-machine" communication is currently a hot topic in how AI is used in the manufacturing industry. The benefits associated with machine-to-machine communications are too broad and diverse to explain in detail in this article. However, many problems can be avoided when one machine can use AI to detect an issue with another machine.
• From the design process and production to the supply chain and strategic decision-making, AI is poised to change manufacturing forever. However, there are currently limitations to AI that are preventing its widespread adoption. Some of the benefits we discussed, such as predictive maintenance and digital twins, can be handled with machine learning and advanced analytics without AI – but AI takes them to the next level.