Two Important Ways AI Is Shaping Manufacturing

New technologies are dramatically reshaping traditional industries, such as manufacturing. Industry 4.0 is a term for this new vision of interconnected systems, real-time analytics, and automation working together to streamline production and lower costs.

Let’s explore an interesting part of this new industrial paradigm: artificial intelligence. There are several ways that artificial intelligence (AI) is already starting to improve industrial processes. Manufacturers must understand this paradigm shift if they wish to remain competitive.

1) Predictive Maintenance Gets Smarter

Keeping equipment operational is an obvious priority for manufacturers, which is why so many companies hire a business IT support firm to help them take control of their technology.

In fact, according to one engineering survey, 80% of manufacturing plants cite aging equipment or machine breakdowns as the leading cause of operational inefficiency, presenting a huge opportunity for AI and better technology support.

Why? Predictive maintenance, powered by AI, is supercharging traditional maintenance programs. Traditional maintenance is about guesswork and usage patterns, while preventative maintenance takes the guesswork out of maintenance with data-backed decisions.

Predictive maintenance begins with connecting data sources. Sensors can be added to key components to capture data points about how the product or component works. AI systems then collect data from your machinery, such as temperature, vibrations, and noise levels.

This approach leads to a clearer understanding of both individual machines and the entire network of connected assets instead of applying precautionary maintenance measures wholesale.

Prevent Downtime and Streamline Your Budget

There are a few primary benefits of predictive maintenance.

First, it helps avoid equipment failures that can slow your production line and cause significant financial damage—one of the worst-case scenarios for any manufacturer. Second, it prevents a company from spending too much money too early, optimizing resources.

Predictive maintenance focuses your maintenance, helping manufacturers prevent sudden breakdowns without jumping the gun and spending unnecessarily on premature fixes.

2) AI Enables More Consistent Quality Control

High-quality standards are crucial to any manufacturing operation. When quality is compromised, inefficiencies and missed defects soon follow. AI has already started reshaping how manufacturers tackle the challenge of building repeatable, trustworthy quality-control monitoring.

Reducing Human Error

Quality control is a detail-oriented process that requires precision and consistency. Even then, the most robust processes aren’t foolproof. The best-performing manufacturers spend about 10 to 15% of their total sales revenue on fixing mistakes and managing quality issues.

That’s a significant chunk of revenue lost to inefficiency.

Humans are one of the weakest links in this entire chain. No matter how experienced they may be, even the most skilled humans sometimes make mistakes, whether due to fatigue, cognitive limitations, or simple carelessness.

AI-powered quality control systems can help tremendously in this area. They minimize two of the biggest issues in quality control—accuracy and consistency.

These new systems won’t get tired, distracted, or overwhelmed like humans. In fact, AI-powered systems are already performing reliably better than traditional quality control processes.

Cameras can assemble capture product images in real time, and AI algorithms can analyze them. This results in a quality control mechanism that can consistently apply the same standards, resulting in better outcomes with fewer errors.

Additionally, their detailed logs can be seamlessly integrated into data pipelines, helping you identify recurring issues and address bottlenecks.

Real-Time Quality Control Monitoring

Traditional quality checks happen at the end of production, but AI allows manufacturers to monitor quality as products are being made in real-time. This shifts quality control from a separate and secondary step to a central position in the production lifecycle.

Guided by sensors and cameras, AI tools father data as products move along the production chain, and then analyze this information immediately. Problematic patterns get flagged and escalated so that humans can remediate them on the spot.

Conclusion: AI in Manufacturing Is Here to Stay

There’s no doubt that AI and other information technologies will continue to play a huge role in how manufacturing companies operate and compete. If your company would like to embrace AI, we encourage you to work with a managed IT services provider who can proactively deploy, monitor, and maintain your AI and IT infrastructure. Feel free to reach out to us at any time with your questions!