Cory McNeley is a Managing Director at UHY Consulting.
The United States is experiencing a manufacturing renaissance, driven by the integration of AI and automation into manufacturing execution systems (MES). The combination of hardware—such as sensors and vision devices—with AI-powered software helps factories of all kinds run more smoothly. Manufacturers can now see the whole picture around the clock, tracking every step in production to boost both quality and efficiency.
AI manufacturing models constantly improve quality control by analyzing data patterns from tools, video feeds, sensors and the products themselves. This continuous monitoring and analysis offer a level of oversight no human team could maintain. In the past, manufacturers could only test a few items here and there. Now, AI can oversee the entire process 24/7, catching defects in real time, every time, and helping ensure only the highest-quality products make it out the door.
The automotive and metal manufacturing sectors have already seen the biggest impact from AI-driven quality control, which can catch defects that even the most experienced workers might miss. For example, these systems check paint coatings for microscopic air bubbles or uneven textures that could lead to rust and verify that all bolts are in place and torqued correctly before a car leaves the production line.
In pharmaceuticals, AI scans pills and capsules for tiny cracks, missing coatings or contamination before packaging. Electronics manufacturers use AI to inspect circuit boards for microscopic cracks or faulty soldering, which could cause failures down the line. Identifying these issues early significantly reduces waste and rework.
Cutting Costs With Predictive Maintenance
Traditional maintenance schedules rely on fixed intervals, often leading to unnecessary downtime or unexpected failures. AI is flipping the script on maintenance by predicting problems before they happen. Instead of waiting for a machine to fail or replacing parts on a fixed schedule, smart sensors flag small changes that hint at trouble ahead. That means fewer unexpected shutdowns and less wasted time, as well as lower repair costs.
For instance, sensors might detect increases in vibration or deviations in cut time which could signal potential issues. AI can then determine whether early or delayed maintenance may be necessary, which can prevent costly unplanned downtime. These systems can also help workers maintain consistency across different product lines. Switching between multiple parts raises the risk of applying the wrong technique; AI can ensure workers follow the correct processes, cutting the chance of errors.
Overcoming Implementation Challenges
The high implementation cost of integrating AI into MES is probably the most significant hurdle at the moment. Processing vast amounts of visual and sensor data in real time takes extensive—and expensive—computing power. But costs are dropping by the month, especially for hardware.
Companies can cut costs by focusing on high-value areas for quicker ROI and taking a phased approach to implementation. Instead of overhauling entire systems all at once, companies should start by defining clear objectives for AI-driven quality control and concentrate on areas where the systems can address known pain points.
Another challenge, as with all AI systems, is data quality. AI MES systems are only as good as the data they process, and in high-end manufacturing, even the tiniest fluctuations—for example, a few degrees change in temperature that causes metal to expand or contract—can lead to expensive defects. Poor data management can result in false error detection or AI “hallucinations.” AI is powerful, but it’s not infallible.
That’s why manufacturers should prioritize clean data and robust master data management programs. People are still key, and AI should augment human expertise, not replace it. Proper oversight will help ensure that AI systems operate the way they’re supposed to instead of introducing new errors.
AI As Partner, Not Replacement
One of the biggest concerns about AI adoption is its impact on jobs. In MES, however, it helps workers do their jobs better, not replace them. Think of AI systems like power tools: They don’t eliminate the need for skilled workers, but instead make their jobs easier and more efficient.
When employees are more productive and produce higher-quality work, job satisfaction improves. AI also helps new workers adapt quickly by guiding them through processes and reducing the learning curve. Call it “co-skilling,” with workers and AI working together toward better outcomes. The result? Happier workers and better products.
Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
Read the full article here
