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This article shows how to make leadership aware of all costs and not just the tangible ones. It helps quality professionals to quantify the Total Cost of Quality and derive a compelling business case from that quantification.

The automotive industry is innovating to produce safer and more efficient vehicles. Quality standards are vital, as small defects can lead to serious safety issues. Digital twins and augmented reality (AR) are transforming quality management. This article explores their impact and associated challenges.

Calibration is essential for maintaining quality and safety in industries like pharmaceuticals, food and beverage, and chemicals. The future looks to digital certificates and AI technology to make calibration processes faster and more efficient. This article discusses how AI is set to transform calibration.

Regulatory bodies like the FDA and MHRA are adapting guidelines to accommodate the evolving AI landscape, stressing the importance of innovative compliance approaches alongside traditional software regulations, including audit trails, electronic records, and signatures.

Manufacturers thrive in a competitive market by prioritizing customer needs: price, quality, and delivery. However, long-term success hinges on quality, which ensures products perform as expected from manufacturing through to post-sale. While price is influenced by costs and market dynamics, and delivery by productivity, quality is key to building trust and customer loyalty.

The hype around "big data" has mainly aimed at niche market sales without delivering expected benefits. Similarly, the business world's obsession with Lean Six Sigma has shown minimal returns on investment. In contrast, over the last 25 years, I've leveraged small Excel files to significantly cut costs and increase profits, often by millions of dollars, through a reliable sequence I discovered.

The software converts cameras and embedded devices to fully compliant GigE Vision GenICam devices without investing in any additional hardware.

We explore two critical applications of AI and ML in quality management: predictive quality analytics and automated quality inspections.

Statistical Process Control (SPC) is evolving to not just detect defects, but also to predict and prevent issues. Modern factories use more sensors and collect more data, allowing SPC to analyze real-time patterns and forecast potential issues.

In today's tech-driven world, companies use software to collect data, but the analysis can be flawed. Charts with only specification limits, arbitrarily chosen warning and action limits, and misused Process Behavior Charts contribute to misinterpretation.