In an article from Quality Magazine, it states that the "convergence of automation and quality is redefining how manufacturers detect defects, maintain standards and continuously improve processes."
The article states that "Technologies that once seemed futuristic, such as AI-driven inspection systems and mobile robots working autonomously on the factory floor, are rapidly becoming standard tools in the arsenal of manufacturers committed to improving quality, efficiency, and resilience."
But how effective are these technologies right now?
According to an article from Innovation News Network, it states that "AI-enhanced quality assurance is revolutionizing manufacturing by providing greater precision, efficiency and sustainability through real-time defect detection, automated processes and predictive maintenance. However, challenges remain regarding data quality and ethical considerations."
The article states that "AI-enhanced quality assurance can provide unprecedented levels of precision, efficiency and waste reduction. However, while the technology is transformative, it can still take time before it's fully effective."
In an article from the Harvard Business Review, it states that "For all the enthusiasm around generative AI, there's a hurdle that is limiting its adoption: the technology's tendency to make things up, leave things out, and create so many possibilities that it is hard to figure out which ones will be effective. For that reason, the vast majority of companies now employ human reviews and stand-alone testing tools or services to address generative AI's deficiencies. However, both of those quality-control methods are expensive, and they can handle only a fraction of generative AI's total output."
In an article from Quality Digest, it states that "Effective quality control reduces material waste, energy use, and downtime--supporting not only operational efficiency but also corporate sustainability goals. However, achieving the necessary level of precision--especially the critical leap beyond the approximately 80% defect detection accuracy often reached relatively easily in proof-of-concepts or early deployments--has proven a major hurdle."
It seems that human oversight matters and AI's current limitations are recognized. AI cannot yet replace human oversight and judgment.
While there is a shift of using AI in quality control, we are still in the initial phases, and as the algorithms improve, data stabilizes, and frameworks strengthen, AI's effectiveness will increase.
Helen Roush is Executive Vice President of Paperitalo Publications.
|
