White Paper:
Reality Check in Quality Control:

Underestimated Weaknesses of Manual Inspections

Find out in our white paper how high the discrepancy is between the expectations of manual inspections vs. the reality experienced in the companies and how AI-based inspection systems can help to overcome the weaknesses of manual inspections.
maddox

In a survey of over 100 quality experts, we were able to demonstrate that while the limitations of manual quality control are generally recognized, their actual extent is often underestimated. Failure to adequately address these risks in manual inspection inevitably leads to high costs resulting from customer complaints or false rejects.

maddox
In our survey of more than 100 quality experts, we were able to demonstrate that although the limitations of manual quality controls are generally known, their true extent is often underestimated. If these risks are not adequately taken into account in manual control, this inevitably leads to high costs due to customer complaints or pseudo rejects.
CEO and Co-founder of Maddox AI

Peter Droege

In our discussions with potential customers at Maddox AI, we often come across the fact that many company representatives lack sufficient awareness of the true extent of weaknesses in their manual visual quality controls. The accuracy and performance of these controls is often significantly overestimated. This results in avoidable costs ensuing from selling defective parts to the customer or increased scrap by falsely rejecting good parts.
Peter Droege
CEO and Co-founder of Maddox AI
Peter Droege

In our discussions with potential customers at Maddox AI, we often come across the fact that many company representatives lack sufficient awareness of the true extent of weaknesses in their manual visual quality controls. The accuracy and performance of these controls is often significantly overestimated. This results in avoidable costs ensuing from selling defective parts to the customer or increased scrap by falsely rejecting good parts.

AI-Based Support for Quality Experts

AI-based inspection systems offer a solution and can achieve inspection accuracies comparable to those of an ever-attentive human inspector. Given that human inspectors' defect annotations serve as the basis for AI-based inspection systems, the limitations of manual controls play a pivotal role in developing robust AI systems. Even the most advanced AI algorithm cannot achieve optimal inspection accuracy if it relies on inconsistent training data. Long story short: Without a consistent defect definition, the developed AI model will not be accurate.

The good news is that solutions such as Maddox AI support quality experts with various digital tools to create a consistent defect definition and thus consistent training data. A high quality training dataset ultimately leads to highly accurate AI models.

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White Paper

Why Only Few Companies Actually Use AI-Based Inspection Systems

Find out in our white paper why only a few companies are using AI systems in regular operation, although the majority is convinced of the great added value of the systems.
maddox
Dozens of studies have already examined the adoption rate of artificial intelligence (AI) in companies. The vast majority come to the same conclusion: companies have recognized the great potential for optimization, but still rarely use AI systems in regular operations. Read White Paper now