Quality 4.0: A Trend within a Trend
THE INDUSTRIAL INTERNET OF THINGS ALLOWS COMPANIES TO TAP INTO MORE DATA THAN EVER BEFORE.
If you talk to anyone in manufacturing today about the future of the industry, you’re likely to hear a quizzical, “Do you mean Industry 4.0?”
Industry 4.0 got its name from the Fourth Industrial Revolution, which brings together the automation from previous industrial revolutions with digital data from rapidly emerging technologies like sensors and advanced analytic techniques into what is being called “cyber-physical systems.” This evolution is causing the digital transformation of modern business in products, manufacturing, and supply chains. Today, companies are about to gain access to more data which in turn will help them make more informed strategic decisions about their businesses.
The Importance of Quality—and the Inherent Disconnect
If you look at any survey taken of manufacturers, the top priority is almost always to improve quality. End customers demand quality products so OEMs demand quality performance all the way down their supply chains. In today’s manufacturing environment, there’s no tolerance for delivering anything but a quality product. The risk to the company’s reputation is too great.
There is often a disconnect on the importance of quality with executives at manufacturing companies, however. Traditionally, quality has been considered a function of the quality department, not of the entire organization. In this paradigm, since quality has been grouped under production, budgets to fund quality management systems go by the wayside for more or better production machines. This same department often gets passed over, relegated to only a policing function, responsible for catching bad parts before they get shipped to customers. Avoiding the cost becomes important in producing quality parts, but the quality department doesn’t get the funding needed to avoid making bad parts, just plenty to continue finding those that have already been produced. Investing in processes and tools to ensure first-time quality has historically been much harder to justify.
Thankfully, this is changing. The Industrial Internet of Things (IIoT), an outcome of Industry 4.0, allows companies to tap into more data than ever before, giving executives visibility into the value of quality as a business strategy. Imagine that the sales and marketing department has learned that quality performance is a competitive differentiator, so those executives clamor for and support it. This pressures the R&D and engineering departments to design products that perform and last better than their competitors—in addition to being innovative. Of course, operations can now see the amount of time they spend chasing deviations, returns, and warranty claims so they collaborate with production and quality for an answer. The quality manager, who isn’t responsible for producing products, just validating them, must account for all of the bad quality that is now visible to the whole company.
What is Quality 4.0?
Dan Jacob, Research Director and Principal Analyst for Quality with LNS Research, a leading manufacturing research and advisory firm, coined the term 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. 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.
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 resolution 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.
Using Quality 4.0 to Fill the Skills Gap
The rapid growth in the manufacturing sector has been met with a serious shortage of trained workers. Manufacturers find themselves hiring people with no manufacturing experience just to fill open jobs. 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. For example, metal stamper Ralco Industries uses cloud manufacturing ERP for rigorous control of initial job setup that doesn’t allow production to begin until all required details are in place. This not only gives the company inherent quality control, but also gives production teams the kind of tools that makes their jobs fulfilling, and they stay.
Systems with built-in quality management also result in greater automation in production environments. MFC Netform, a highly automated supplier of transmission components, uses automated 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.
The Value of 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.