Improved quality and lower quality costs are not the main drivers of industrial transformation programs. Quality leaders are largely absent from the high-level planning of industrial transformation initiatives, and digital transformation projects tend to miss the quality mark, instead focusing on improved efficiency, faster delivery, and greater efficiency. greater variety. Without a quality voice at the table, industrial transformation programs miss out on one of the key benefits of quality: predicting quality to avoid defects.
In a recent LNS research report, on average less than 50% of organizations with an ongoing industrial transformation initiative had a quality manager on the team. The results of industrial transformation programs are also light on quality. A Forbes report on “100 Stats on Digital Transformation and Customer Experience” shows that the top three reported benefits are improved operational efficiency, faster time to market, and the ability to meet changing customer expectations.
While quality is certainly related to meeting changing customer expectations and efficiency, the graph below in Figure 1 shows that the reasons for quality are well below the list of observed impacts.
Quality programs have historically used tools and strategies, including measurement sampling, failure mode and effect analysis, control plans, and statistical process control to create a people-based network to to prevent and detect faults. The challenge for quality leaders in the digital age is to translate the network of people into manufacturing technology.
Industrial transformation efforts historically under-represent operations and quality functions, even though the factory is where the change is most felt. Factories are seldom at the center of defining or carrying out the industrial transformation program, although they are the site of industrial operations. The graphic below shows this problem. Even though it sits near the top of the rankings, quality is still represented in less than 50% of industrial transformation teams, and operations leadership is even weaker. Progress has been made in recent years, but not enough.
The answer here is that quality leaders need a seat at the table and a voice in the development and design of industrial transformation solutions.
So why aren’t they at the table?
It’s hard to see how digital solutions can solve real quality issues. How can quality leaders learn about digital technology and apply it to the manufacturing quality situation? It’s a challenge for sure. Many software products are positioned as a digital transformation solution. A quick tour of the sponsor floor at a manufacturing and tech show reveals several that aren’t intuitive as a digital transformation, at least not in the manufacturing space. This means that there is a broad and inclusive definition of what constitutes a digital transformation solution, making it difficult for the newbie to discern a solution that works for their situation. The way to solve this problem is to reverse the order of operations and start with the problem that needs to be solved, and then look for potential solutions that are suitable for that business problem.
A continuous process manufacturer has implemented a digital transformation pilot initiative architected and led by the quality leader. The initiative understood product quality as a key aspect of the success of the program. This company had unacceptable levels of product quality complaints and defects, which is why digital product monitoring was implemented and integrated into the platform as part of the project. Digital monitoring tools were related to important aspects of product quality analyzed from customer complaints and scrap reports. The result was a 50% reduction in defects in the pilot phase of the project.
Figure 3, from LNS Research, shows that Quality 4.0 is the same as “traditional” quality approaches, with just better and faster analysis and decision making and a more connected environment. This definition of quality 4.0 lacks a key potential for the quality leader: prevention through prediction.
The main mission of the quality function is to build systems and methods which prevent faults. We absolutely must prevent faults from escaping the customer, so we put in place some basic elements that warn us of impending faults that may arise. Control plans are a collection of control points which should give a good result if all prescriptions are followed. Statistical process control incorporates a set of rules to quickly warn us of an impending defect. Sampling plans attempt to thwart patterns and give some level of assurance to our decision making regarding the accept / reject decision.
Old habits die hard
Until now, the weak point of this system has been the person. Getting people to perform consistently is notoriously difficult. Sometimes everyone has a bad day, wants time off, or is distracted by family or family issues or conflicts with co-workers. An inspector, on a good day, is only 80% effective, at best.
The digital age requires original thinking, but old habits die hard. I visited an agricultural products factory in California in 2019. That factory (and indeed the entire company) had been on a digitization journey for years. The production lines have been heavily digitized. The quality control function was digitized and semi-automated but not connected.
Reviewing the progress they had made on digitization, I found that the quality function behaved in exactly the same way as the pre-digitization: they were doing the same tests, with the same frequency, with the same level of understanding of what in the process led to a good or bad quality result. There had been no change in the quality function throughout this scanning journey, even the sample rate was the same.
All of the tools and approaches that are integral to the defect prevention effort are based on the past. We sample products after they are manufactured, collect data and plot points on a control chart once the data exists, sometimes long after that. We apply statistical process control rules to previously generated data to quickly warn of patterns that could lead to faults. All elements of our defect prevention strategy are retrospective after the defect has been created.
We try to drive the car looking in the rear view mirror.
The digital age gives us a unique opportunity to move from a strictly reactive approach to proactive event management and problem prevention.
The opportunity for the future of quality in the digital age lies in the development of the predictive power of a fully characterized process and its quality results. The best representative of this mindset is the control plane. The control plane represents what is called in Six Sigma the transfer function (Y = f (x)). The transfer function is the formula of process variables that must be controlled to get a good result.
To be effective, the control plan should include only the things that are most essential to getting a good product, and not the things that are easiest or most practical to measure. Go over the control plane keeping in mind the following question: “Are these elements really serving as a proxy for something that is not measurable in the old paradigm?” “
If the elements of the control plan are approximations of what should really be controlled, there is now an opportunity to correct this using digital technology. For example, if the control plan lists an inspection point for the cooling tank temperature, that measurement is actually an approximation of the product temperature, a step away from the actual variable that matters. It was the best we could do at the time, so we did it. In our example, product temperature is a key control for a few different quality characteristics of the final product. However, if we could use a laser thermometer to continuously measure product temperature at many points in the manufacturing process and report that data to a machine learning system, now we had meaningful data directly from the variable that counts for a quality result.
The digital age gives us the unique opportunity to redefine what is possible, to relate the points of product and process quality to real control variables to achieve a good result. Over time, as the “library” of process models is filled and used, these models will improve and become more reliable, confidence in the machine learning system will increase to give appropriate guidance. Second, what was previously inconceivable is now possible; a quality prediction which ultimately leads to real prevention. Predictive quality will be at least 10 times cheaper than current prevention methods, just as decades-old and widely accepted predictive maintenance practices have proven.
James wells is Senior Consultant at Quality in Practice, a consulting and training firm specializing in continuous improvement programs, and specializing in quality fundamentals, including the application of digital solutions to common manufacturing challenges. He has led quality and continuous improvement organizations for over 20 years in various manufacturing companies. Wells is a Certified Black Belt Master and Certified Lean Specialist.