LNS Research Quality 4.0: The Quality Innovation Foundry

Use Cases Continue to Emerge as Market Matures

Quality 4.0 is a leading-edge initiative within Industrial Transformation. Manufacturers using this initiative reap real value: improved quality, costs, efficiency, market share, and brand recognition. Quality 4.0 use cases enable new approaches and answers to traditional organizational and value chain quality challenges.

Quality 4.0 blends new technologies with traditional quality methods to arrive at new optimums in operational excellence, performance, and innovation. Successful innovators are investing in this initiative to improve operations and quality in particular. Why?

LNS conducted a global survey around use cases associated with Quality 4.0. This research found that 55% of companies are at some stage in implementing initiatives for it within their industrial transformation.

This report shares use cases that the market is adopting and provides details on the exact status of Quality 4.0. 

Complete the form on this page to read the eBook and learn more about the transformational potential of moving quality from a standalone process executed by a low-profile department to a core part of a company’s fabric.

Throughout this report, you will learn the impact and effort associated with 33 use cases and illustrate the impact/effort index across them. However, getting a sense of the big picture is just as important for
leaders across industrial companies.

Quality 4.0 and digitalization- foundations for new business models

This allows corporations to be interconnected, seamless, and empowered. Our research on IX readiness showed that transformation doesn’t come from technology, but rather from process changes enabled by collaborative technologies. This report shows that it is equally true for Quality 4.0 specifically. The most fundamental process change is to move quality to a standalone process. This process will, over time, allow the organization to focus on real business improvement as it automates its role in collecting, aggregating, contextualizing, and interpreting data.


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