In AI Meets Sustainability, published by The Manufacturing Frontier, Dr. Wilhelm Klein argues that synthetic data for quality control paired with AI offers a powerful lever for manufacturers trying to reduce waste, boost efficiency, and meet sustainability goals.
Manufacturing often accounts for a massive share of global material waste—up to 40% in some estimates. When defects are missed, when products are over-scrapped, or when rework is frequent, that waste compounds. Traditional inspection practices frequently plateau around ~80% detection accuracy, which isn’t enough in high-precision or regulated sectors.
That’s where synthetic data and AI-driven inspection systems come in. By simulating rare defects, variable lighting, and unusual materials, synthetic datasets allow models to be trained in a broader variety of conditions—even before those conditions appear on the line. This reduces the need for large collections of labelled defect images, speeds up deployment, and makes inspection more adaptable to new product variants.
Another key point in the article is the role of human expertise. Even with high-automation, involving experienced inspectors or operators in reviewing edge cases (human-in-the-loop) helps maintain reliability and prevents drift. In dynamic manufacturing settings, this hybrid model balances speed with accuracy.
At Zetamotion, our Spectron platform is built around these principles: we use synthetic data-augmented training, work with human feedback loops, and aim for high detection accuracy with minimal real-defect data. For manufacturers looking to improve yield or reduce scrap, our manufacturing inspection service can help with pilot programs and scaling up.
