How Will AI Continue to Impact Industrial Automation in 2023?

Artificial intelligence (AI) has become a game-changer in numerous industries, ranging from agriculture to health care. However, some of the most notable improvements have come in manufacturing and similar realms as people attempt to use AI in industrial automation. Here are some of the continued effects of the technology you can expect to see in 2023.

1. Enabling Continuous Improvement

The leading manufacturing brands will have the best chance of retaining their dominance if leaders look for weaknesses and investigate options for reducing those shortcomings. Artificial intelligence can help such enhancements happen, especially if companies use industrial data-collection and analysis platforms to pinpoint problems.

For example, consider the efforts made by executives at P&G. They entered into a multiyear partnership with Microsoft to see how using AI in industrial automation could help them achieve various benefits that collectively lead to a competitive advantage.

AI investments are part of an overarching digital transformation strategy. They allow personnel to monitor production in real-time, improve resource usage and more. In one example, employees can keep an eye on diaper production as machines assemble each layer of the products. Elsewhere, they use machine learning and analytics to predict paper towel lengths.

Since P&G has so many well-known brands under its corporate umbrella, maintaining quality control is essential to staying profitable. If people buy their favourite household goods and notice inconsistencies with each purchase, many will probably lose patience and switch to brands they perceive as more reliable.

One reason AI works so well in helping companies target shortcomings is that algorithms can process huge quantities of data and do so much faster than people could by themselves. That means artificial intelligence can often spot patterns individuals would never notice. This doesn’t make people less valuable in finding and fixing issues. However, AI works to complement their knowledge and judgment.

2. Reducing Burdensome Workloads

Many industrial processes are incredibly complex and detail-oriented. The nature of the work poses some definite problems. For starters, even the most conscientious employees eventually get tired and may miss flaws in items coming off production lines. Another issue is the potentially time-consuming knowledge-transfer process needed to train new employees for these roles.

Using AI in industrial automation can’t overcome these obstacles. However, when applied to the right use cases, it could reduce them. Consider the redesign of MetalSpector, a metal-inspection solution sold by Toshiba. While figuring out how to enhance the product, representatives took a customer-focused approach. They became primarily interested in how people use MetalSpector rather than only improving how the product worked.

One of the enhancements involved using AI to grade steel. MetalSpector now uses an AI inference model that improves its analysis skills automatically with every use. However, human inspectors ultimately decide if the steel meets minimum standards. People working in steel-inspection roles can continue doing measurements as usual. As they do, the associated information naturally contributes to the algorithm’s training without requiring extra steps.

The AI upgrades also have a training component useful for people in junior-level inspection positions. For example, the algorithms can show trainees real-world image samples and teach them to check for non-metallic inclusions in the steel.

Customers that used this improved Toshiba offering said it reduced the workloads of senior inspectors while providing junior-level workers with valuable new skills. Also, one of the product's primary selling points is that the automation component reduces users’ pain points.

This is an excellent example of how using AI in industrial automation does not necessarily mean leaving a task wholly to technology. Instead, the best cases often involve combining the capabilities of automation and human knowledge.

3. Unlocking Greater Productivity

One of the main reasons why decision-makers pursue AI in industrial automation plans is because they believe it can help their workforces get more done in less time without risking injuries or decreased quality levels. Sometimes, that happens when companies add cobots to assembly lines. Such a change could free workers from ergonomically unfriendly tasks. It might also allow a company to switch to a 24/7 production schedule.

Cobots have built-in features that make them slow down or stop when people get too close. Some also include soft, flexible exteriors so unintended contact won’t hurt anyone.

Antony Bourne, the senior vice president of industries at IFS, said many complicating factors mean humans often take longer than expected to become productive by themselves. That could be because they’re coming into the industry for the first time after graduating from a university program. Plus, people in some industrial roles decided to look for other work. The pandemic was one of the factors shaping that choice.

Zebra Technologies Corp. specializes in industrial automation options. Many of its products feature specific designs and capabilities to help people get more done. For example, some of the company’s automated mobile robots (AMRs) cause people to become up to three times more productive. That’s because the machines are faster and can carry heavier payloads than competing models.

Implementing AI in industrial automation can span beyond robots, too. Food brand Mars is working with Accenture to use AI-powered digital twins for better planning. They show the effects of changes to products or the factory environment before happening in real life. People could use those insights to inform where they place new automated equipment.

An Exciting Year for AI in Industrial Automation

These are some benefits people can look forward to when exploring how to use AI for better results with industrial automation. However, individuals must think strategically about how to deploy the technology.

That requires setting a budget, researching service providers and assessing whether the industrial setting’s current layout supports the introduction of automated equipment.

Managers may need to readjust schedules to give people enough time to get used to working with automation. Some workers may also have questions or concerns about the technology implementation timeline. Being open and honest when addressing queries can help employees feel more confident about upcoming changes.

It’s also important for decision-makers to be clear about what they hope to achieve by using AI and automation. From there, they can set key performance indicators (KPIs) that connect to larger business goals. Having those metrics to track makes it easier to see whether the technological solutions have the expected benefits and to make adjustments if not.

These are some of the best practices to make a company’s AI and automation investments highly successful. The outcomes tend to motivate people to stay open to other upgrades later.