Items Tagged with 'Quality 4.0'

This article explores the evolution of manufacturing data, the limitations of DMAIC in the Fourth Industrial Revolution, and introduces Binary Classification of Quality (BCoQ) and Learning Quality Control (LQC) systems as part of Quality 4.0.

Industry 4.0, or Technology 4.0, signifies the shift towards automation, data exchange, and advanced technologies like AI and IoT in manufacturing and beyond. Its value lies in aligning with organizational goals and strategic plans and enhancing employee efficiency, offering a comprehensive approach to modernizing operations across the board.

Quality 4.0 looks at how digitization improves industry processes. Despite over 10 years of focus, there's still no clear definition or knowledge base for Quality 4.0.

Quality 4.0 aligns quality management with Industry 4.0 to drive efficiencies, performance, and innovation. It's critical now more than ever to merge human skills with technology.

In the early 1980s, US automakers faced stiff competition from Japanese counterparts, prompting a shift towards prioritizing quality for customer satisfaction. This era marked the birth of ISO 9001 QMS, symbolizing a significant change in industry practices.

Labor shortages continue to pressure manufacturers, with some dedicating up to 20% of their workforce to manual inspection. Embracing Quality 4.0 with automated in-line inspections and AI process analytics could provide significant value.

Dr. Milton Krivokuca, our 2024 Quality Professional of the Year, shifted from manufacturing to education, guiding students to faster degrees and career advancement. He actively promotes quality assurance programs and stays updated on industry trends like Quality 4.0, proving his dedication and expertise.

The manufacturing landscape is undergoing a profound transformation in the era of Industry 4.0, characterized by the integration of digital technologies, automation, and data-driven decision-making. Quality 4.0 is at the heart of this evolution.

Quality 4.0 is still in a definition phase. In this article we present learning quality control, our vision.

For quality assurance purposes and to optimize the manufacturing workflow, machine vision is employed throughout the entire process to identify production errors, damage, or impurities early on.