Artificial intelligence (AI) is making a splash across the board, infusing various industries with unprecedented ways to innovate. Manufacturing is no exception. Increasing levels of automation and interconnectivity have changed longstanding processes within the industry. One primary challenge manufacturers now face is implementing these cutting-edge technological advances into existing workflows. When executed well, AI in manufacturing boosts productivity and streamlines operations.
Specifically, machine learning is poised to become particularly impactful in manufacturing. This AI application allows systems to improve over time without the need for explicit reprogramming or interference from humans. There are many uses for machine learning in manufacturing. But let’s take a closer look at a few stand-out use cases that really make a difference in a company’s bottom line.
Preventing Costly Equipment Breakdowns
When a machine breaks down somewhere in the manufacturing process, it’s not as simple as wheeling in a replacement. Repairs tend to be costly and time-consuming. And worse, one defective component can halt the entire process—negatively impacting productivity and revenue in real time. There are a number of causes for machine breakdown in manufacturing: operator error, system error, insufficient maintenance and environmental causes.
Implementing AI-driven manufacturing data analytics complete with machine-learning algorithms helps decision-makers track uptime and downtime, patterns and causal relationships. In turn, this can help manufacturers better understand when machines are likely to break down, when various equipment needs maintenance and the real implications of system failure. Thanks to the very nature of machine learning, data analytics for manufacturing provides increasingly relevant insights—aided by simple feedback from business users within the company.
In fact, manufacturers’ usage of machine learning specifically to improve predictive equipment maintenance will increase 38 percent over the next 5 years by some estimates.
Changing the Nature of Manufacturing Jobs
Part of automation is figuring out how humans and artificial intelligence-based systems can work together to solve problems, complete tasks and optimize workflows. Implementing machine learning in manufacturing changes the very nature of manufacturing jobs in several ways. First of all, it enables automation, which in turn boosts productivity. But half of organizations also cite the ability to “refocus people’s efforts on non-repetitive tasks that benefit from human intervention” as a reason for prioritizing automation. In other words, machine learning and AI can free up humans to work on more complex tasks while automation handles more routine ones.
Furthermore, implementing and maturing manufacturing analytics over time opens up exciting job possibilities for data scientists, IT specialists, engineers and more. As Artificial Intelligence and machine learning take a more active role in shaping manufacturing as we know it, it will be crucial to employ the right mix of people to keep these systems running smoothly.
Reducing Procedural Inefficiencies
Business users used to have to input specific queries to get answers from stored data. But machine learning and AI-driven data analytics have made it possible to pull helpful insights, even without having a specific question in mind. Algorithms are capable of diving deep into data to identify trends and anomalies, delivering them to human users in simple language.
As Manufacturing Business Technology writes, “This provides manufacturers with complete transparency into how processes are working in real life, enabling them to pinpoint business process inefficiencies.”
This phenomenon, known as process mining, allows companies to identify and then correct inefficiencies that may have been lurking beneath the surface, just out of sight of the naked eye.
Long story short: Machine learning in manufacturing has huge potential for functionalities like preventing breakdowns even before they occur, creating new jobs and reducing overall inefficiencies.