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.
CEO and Co-founder of Maddox AI
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.
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.
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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