Predictive Quality

This whitepaper delves into the evolving landscape of manufacturing quality. It emphasizes the need for better methods to improve quality, despite decades of progress towards improved process control. The paper highlights the delicate balance manufacturers face in maintaining both high throughput and quality which often can lead to either the shipment of “good enough” parts under the pressures of production demands, or high scrap rates and containment costs.

We critically examine the limitations of current quality management methods, including the reliance on quality gates and statistical process control (SPC), which often lack the flexibility and depth required for modern manufacturing complexities. The paper underscores the significant costs associated with quality-related issues, including increased cycle times, excess shipping costs, and inflated scrap and rework rates, further exacerbating the challenges in a dynamic macroeconomic environment.

Addressing the need for a paradigm shift, the paper describes how artificial intelligence, advanced data platforms, and cloud computing can be leveraged for a predictive quality model. This new approach shifts the focus from detecting defects at quality gates to predicting and preventing them at earlier stages, thereby operationalizing quality control within the manufacturing process itself. The paper posits that this “shift left” has been proven to not only improve product quality and reduce costs, but empower engineers and quality teams with advanced tools, transforming the traditional manufacturing quality paradigm into a more efficient, effective, and forward-thinking model.