How Quality 4.0 is changing operations for manufacturers

Quality 4.0 combines traditional qualities with Industry 4.0 technologies and concepts to improve manufacturing operations.

By Chris Vavra September 5, 2023
Courtesy: Chris Vavra, CFE Media and Technology

Quality 4.0 insights

  • The concept of Quality 4.0 emphasizes the integration of emerging digital technologies, such as robotics, augmented reality, digital twins, blockchain and AI, into quality management processes.
  • Quality 4.0 not only focuses on technological advancements but also recognizes the crucial role of human workers as “human sensors” on the shop floor and empower them with modern tools, smart onboarding and training.

Industry 4.0 is affecting how devices connect with one another and changing how people work. While more information is good, it’s a question of effectively utilizing the data and making better decisions In his presentation “Quality 4.0: The Emerging Role Of AI And Digital Technologies Are Transforming Quality Management,” at the Automotive Smart Manufacturing 4.0 USA Summit 2023 in Detroit, Chris Kuntz, vice president of marketing at Augmentir, said Quality 4.0 is the next step, which emphasizes three steps.

  1. Use of emerging digital technologies within the realm of quality and quality management. This includes robotics, augmented reality (AR), digital twins, blockchain, artificial intelligence (AI) and more coming together to make systematic improvements.

  2. Achieving step change improvements in quality. Kuntz said Quality 4.0 is designed to transform traditional continuous improvement programs such as lean, six sigma and total quality management (TQM)

  3. Improve across the entire quality value chain. Quality 4.0, when used properly, improves all aspects including R&D, procurement, manufacturing, logistics and services. The technology, augmenting human workers’ capabilities, can help make operations more seamless and effective.

Augmenting the connected worker

“Quality 4.0 is not all about technology,” Kuntz said. “Workers are human sensors on the shop floor. They play a key role in identifying quality issues. The problem is they’re very disconnected and using outdated technologies. The worker itself is, for the most part, using the technology they were using 20 years ago.”

Giving workers modern tools while also encouraging them to hone their senses can help resolve safety issues and share timely insights across the value chain to encourage continuous improvement.

Quality 4.0 not only focuses on technological advancements but also recognizes the crucial role of human workers as "human sensors" on the shop floor. Courtesy: Chris Vavra, CFE Media and Technology

Quality 4.0 not only focuses on technological advancements but also recognizes the crucial role of human workers as “human sensors” on the shop floor. Courtesy: Chris Vavra, CFE Media and Technology

An augmented, connected worker, Kuntz said, can help in four ways:

  1. Better smart onboarding and training. Digitize and automate onboarding and training processes to help make workers skills and operational faster to offset a constantly-changing workforce.

  2. Smart upskilling and reskilling. Use AI-driven insights to help determine who needs to be upskilled or reskilled to improve productivity and engagement.

  3. Smart guidance. Provide personalized guidance to each worker that meets their needs and different levels of experience.

  4. Smart support. Support workers in the flow of work with curated knowledge and remote expert support. AI bots also can provide autonomous support.

AI’s role in quality management

Manufacturers today are focused on many of the same goals to establish resiliency in their manufacturing operations, said Rajeev Kalamdani, manager of IIoT analytics, Ford Motor Company, in his presentation “Making Use Of AI For Better Quality Management.”

Kalamdani said, “Quality 4.0 can improve process discrimination but it needs to be complimented with process improvements.”
At the same time, they also are faced with many of the same business and technical challenges that make scaling solutions across an enterprise difficult.

Business challenges include remote and/or dispersed teams, sustainability, dealing with the skills gap and improving profitability and return on investment (ROI).

Technical challenges include the information technology/operational technology (IT/OT) divide, data accessibility and cleanliness and pilot purgatory.

These challenges, Kalamdani said, will only grow as the computing landscape evolves. It doesn’t matter if data is being gathered at the edge or in the cloud. There is more data than ever and wading through all of it is very difficult.

AI and machine intelligence, combined with a robust and secure data estate, can provide manufacturers with capabilities that span the entire manufacturing ecosystem.

Transformation at scale, Kalamdani said, is hard, but learning from prior efforts goes a long way. Those efforts are enhanced when there is a long-term plan that starts small and thinks big and focuses on building capabilities rather than approaching Quality 4.0 as a vague idea or notion.

Chris Vavra, web content manager, CFE Media and Technology,

Author Bio: Chris Vavra is senior editor for WTWH Media LLC.