Quality 4.0 and the Industrial IoT

  • Modern Manufacturing

Quality is one of the top concerns of most manufacturers. End customers demand quality products so OEMs demand quality performance all the way down their supply chains. There’s no tolerance for delivering anything but a quality product.

However, quality has been considered a function of the quality department, not of the entire organization. But this is changing. The Industrial Internet of Things (IIoT) allows companies to tap into more data than ever before, giving executives visibility into the value of quality as a business strategy.

When everyone is looking at quality performance, everyone looks at the risks inherent in their individual responsibilities for delivering it. Now quality becomes everyone’s job.

What is Quality 4.0?

Quality 4.0 is about using technology to show that quality should really be a company-wide strategy with the executives at the helm driving performance. The term was coined by Dan Jacob, research director and principal analyst for quality with LNS Research.1

Quality 4.0 brings into focus the data required to monitor quality performance including the costs of both good and bad quality. Many companies have begun to increase the detail of the data they collect using sensors and analytics. Instead of inspecting parts as the primary quality activity, these companies inspect their suppliers’ quality and processes to circumvent downstream quality issues. Instead of waiting for a machine to wear out, companies monitor machines for symptoms of an impending problem and maintain them ahead of time to ensure high performance all the time. Instead of identifying that the root cause of a quality failure was poor performance by an operator, these companies proactively either train their operators more effectively or introduce automation for jobs that suffer from repeatability challenges.

Making Quality an Operational Requirement

The newfound interest in quality across the enterprise can justify putting in place better systems to keep the business producing quality products. By implementing systems that enforce control for quality needs, operators need less on-the-job training, and can get to work right away. Companies automate rigorous control of initial job setup so production will not begin until all required details are in place. This not only gives companies inherent quality control, but also gives production teams the kind of tools that makes their jobs easier.

Systems with built-in quality management also result in greater automation in production environments. Companies can automate quality inspection systems with lasers and air pressure tools to take multiple measurements on a single fixture which takes human interpretation—and the potential error—out of their quality processes. Inspection data is immediately compared to the specification and if a dimension is outside of the control limits, an alert is presented, and a notification is sent to a supervisor. The system can even initiate an upstream shutdown to prevent further non-compliant parts.

How Industrial IoT Enables Predictive Quality

This increase in automation, along with the tenets of Quality 4.0 and a significant drop in the price of sensors, has led to manufacturers deploying vast networks of sensors throughout their plants to gather even more data about operations. The goal is to monitor potential contributors to quality issues early and analyze the data for trends that provide actionable insights based on predictive analytics. One use case is monitoring machines for vibration, heat, or power draw—all of which can signal an impending failure. When a data stream begins to trend away from normal, a maintenance engineer can be alerted.

Even greater value comes from advanced analytics that can correlate data with future failures after the product has been put into use. Equipment manufacturers are beginning to monitor the machines they sell to identify patterns of normal and abnormal behaviors that they can then use to improve their understanding of failure modes. By identifying the root cause of the product failure with traceability back to the individual operations, operators, and machines involved in its manufacture, companies can identify production flaws that can be corrected to reduce or eliminate future warranty claims.

Industry 4.0 and the early benefits being realized with Quality 4.0 show that manufacturing companies can benefit from the use of technology to support improved quality performance. The cost of bad quality is avoidable with some investment in systems that support good quality. Technologies that enable increased control of operations and quality monitoring can not only result in higher yields, but also give employees better tools to do their jobs. And when quality is a company-wide strategy, everyone wins.

Visit Plex at the 2019 AIAG/SCAC Supply Chain and Quality Conference September 12-13 in South Carolina to learn more about how you can leverage Quality 4.0 in your own company. Register for the AIAG/SCAC conference.

Or, for more information from LNS on how leading manufacturers put quality first, and which seven risk-reduction best practices have the biggest ROI, read their latest research spotlight “Roadmap to Supplier Status: Think Risk Performance, Not Compliance.”

1 What is Quality 4.0? LNS Research. Jacob, Dan. July 2017. 
https://blog.lnsresearch.com/quality40

About the Author

Stu Johnson, Director of Product Marketing, Plex Systems

Stu Johnson has more than 25 years of experience in the manufacturing industry since beginning his career as a mechanical design engineer. He moved into the enterprise software space working in various roles providing solutions for global manufacturers in the consumer, aerospace, automotive, and heavy equipment industries. Currently, Stu serves as Director of Product Marketing for Plex Systems and focuses on the future of manufacturing software working with the Manufacturing Enterprise Solutions Association (MESA), Smart Manufacturing Workgroup exploring topics like the Industrial Internet of Things and Industry 4.0.

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