3 Important Ways AI Will Shape Manufacturing in 2025

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3 Important Ways AI Will Shape Manufacturing in 2025

3 Important Ways AI Will Shape Manufacturing in 2025

By on Jan 13, 2025 in Cloud Computing, Manufacturing

Industry 4.0 is a term that describes how new technologies are reshaping traditional industries. It’s a vision of interconnected systems, real-time analytics, and automation working together to revolutionize manufacturing.

Today, we’re going to drill down into one part of this new industrial paradigm: artificial intelligence. Specifically, we’re going to look at the ways that artificial intelligence (AI) has already started improving industrial processes—as divorced from the AI hype and hyperbole as we can.

Quality Control

As you know, maintaining high-quality standards is crucial. When quality control is treated as an afterthought, it leads to inefficiencies and missed defects. AI has already started reshaping how manufacturers tackle the challenge of building repeatable, trustworthy quality control monitoring.

Defect Detection and Reducing Human Error
Effective quality control is a repetitive, detail-oriented process that demands precision and consistency. Even then, despite your best efforts, 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 poor quality issues.

AI is shaping quality control in manufacturing

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

One of the weakest links in this entire chain is the human element. No matter how good or experienced they may be, humans make mistakes. Sometimes it’s fatigue, and sometimes it’s cognitive limitation. And it’s not just physical errors, either. Communication mistakes, such as poorly recorded or misunderstood quality control findings, can balloon into serious problems if they’re not addressed.

That’s where AI-powered quality control systems step in. It helps minimize two of the biggest issues in human-driven quality control—consistency and accuracy.

AI systems won’t get tired, distracted, or overwhelmed like their human counterparts. The rare calibration or sensor issues notwithstanding, the truth is that AI-powered systems are already performing reliably better than traditional quality control processes.

In a typical implementation of AI-powered quality control, cameras on assembly lines capture product images in real time, and AI algorithms analyze them. This results in a quality control mechanism that can consistently apply the same standards with higher accuracy, resulting in fewer errors and better outcomes.

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

Real-time Monitoring
Whereas traditional quality checks happen at the end of production, AI allows manufacturers to monitor quality as products are being made. This is shifting quality control from a separate and secondary step to a central position in the production lifecycle.

AI tools, guided by sensors and cameras, father data as products move along the production chain, then analyze this information immediately. Any problematic patterns or issues are flagged and escalated, enabling remediation on the spot. Without these timely fixes, even the smallest issues can spiral out of control and impact the rest of the production process.

Predictive Maintenance

Keeping equipment running smoothly is a top priority in manufacturing. In fact, according to one engineering survey, 80% of manufacturing plants cite aging equipment or machine breakdowns as the leading cause of operational inefficiency. That’s why predictive maintenance can be such a game changer.

This is an infographic that explains how downtime affect manufacturing technology.
While preventative maintenance is all about educated guesses based on OEM recommendations or typical usage patterns, preventive maintenance takes the guesswork out of proactive maintenance with data-backed decisions.

This approach is beneficial because it’s context specific. Even if you have multiple components of the same make and model, depending on where and how they’re being used will result in different levels of wear and tear.

Implementing 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. Simultaneously, machine learning algorithms constantly analyze patterns to detect early signs of wear or failure.

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.

Reduce Downtime and Save Costs
Predictive maintenance offers two main benefits to manufacturers.

On the one hand, it helps avoid unforeseen equipment failures that can halt production and cause significant losses—one of the worst-case scenarios for any manufacturer. On the other hand, it prevents a company from intervening too early, which optimizes resource and budget allocations.

Predictive maintenance focuses your maintenance interventions at just the right time. By using context-specific data, it pinpoints exactly when a machine is likely to need maintenance—well before a failure but not too early.

This helps manufacturers prevent sudden breakdowns without jumping the gun and spending unnecessarily on premature fixes.

Manufacturing Design and Problem Diagnosis

Designing products and solving complex problems in manufacturing have always been challenging because of the need to balance performance, cost, and reliability. Even small flaws or miscalculations can cause costly delays or failures. However, failing to innovate can lead to stagnation. In this landscape, AI is lowering this barrier to innovation by eliminating risks and reducing costs.

Digital Twins
Digital twins are the exact replicas of a real-life product schema that allows manufacturers to test changes and processes in virtual space, without have to spend the time or money building a physical mock-up.

Want to evaluate the addition of a new component, or how much additional stress an existing product can take?

You can do all that and more digitally without risking any adverse impact on your production or incurring high costs. In fact, companies using digital twins have reported a reduction in total development times by 20% to 50%.

This is possible because the digital twin can simulate the behavior of its real-world counterpart in real time. Integrated data from sensors, historical records, and predictive analysis allow AI to do this. As a result, manufacturers can iterate, experiment, and gain deeper insights while saving valuable time and money.

Enable Faster Problem Solving

Traditionally, finding the root cause of issues means working backward from the pain point. That means hours of manual effort gathering data and troubleshooting across the entire manufacturing chain.

All in all, this laborious process involved a lot of inefficient guesswork and trial and error.

AI helps speed up this process tremendously. It integrates with various production systems to continuously monitor and collect real-time data from sensors, machines, and processes. By analyzing this data with advanced algorithms, AI tools can now spot patterns and quickly identify where issues originate, enabling faster course correction and reduced downtime.

AI in Action: Real-World Cases from The Cutting Edge of Manufacturing

Case 1: Bosch – Predictive Maintenance and Problem Solving
Bosch uses AI-powered predictive maintenance to reduce breakdowns and enhance efficiency in its manufacturing plants.

At one of its automotive facilities, AI systems monitor spindle bearings and analyze billions of data points from the manufacturing execution system (MES). It then helps identify early signs of wear and pinpoints the root causes of quality deviations. Another plant in Charleston uses a root-cause analysis to investigate causal relationships that can lead to rejects at the end of the production process. AI software helps by analyzing the massive amounts of data collected during production to find patterns and pinpoint the reasons behind any issues. Source

Case 2: BMW Group – AI-Driven Quality Control
As part of a strategy to make its factories smarter, BMW Group has developed AIQX (Artificial Intelligence Quality Next), a custom AI-powered quality control platform.

The platform integrates sensor technology and real-time analytics to detect various production issues, including missing components, variant discrepancies, and assembly faults. The automated system then sends immediate feedback to production-line workers through smart devices, enabling on-the-spot corrections. At its Dingolfing plant, BMW has even extended its AIQX platform to perform audio-based quality checks, identifying anomalies in driving noises with high precision.

Case 3: Tata Steel – Digital Twin for Manufacturing Operations
Tata Steel is leveraging AI-powered digital twins to drive innovation in the steel industry. It is testing the AI-powered digital twin technology to support remote factory management and identify process failures that could impact quality before production. It is also using the digital twin approach to help test advanced technologies that can replace traditional steel making to meet the EU’s advanced sustainability directives by 2050.

Embrace Manufacturing Technology With a Trusted Partner

The Astute Technology Management team has a long track record of helping manufacturers in Columbus and Cincinnati adopt new technologies and stay ahead of the competition. If your businesses could benefit from a partner to help you navigate the rise of AI, IIoT, and other new manufacturing technologies, contact us anytime at [email protected] or (614) 389-4102. We look forward to speaking with you!