Tricky product problems are often sticky and chewy like chewing gum: Whatever you do, you can not get rid of them. In practice, they survive because conventional problem solving has repeatedly failed, both in product engineering and in production. The good, highly significant news: Using Statistical Engineering, tricky product problems can be solved quickly and sustainably.
In my professional life, I solved more than 100 tricky quality problems that had survived conventional problem solving of all kinds. For example, an automaker had problems with the timing belt noise of an internal combustion engine. Many conventional attempts to solve that problem in product development, ramp-up, and ongoing production did not improve anything. The problem remained very, very expensive, both in the field and in the factory. Using Statistical Engineering, I was able to uncover the root cause and eliminate the failures sustainably within a few weeks. The application of Statistical Engineering makes it possible to make the detection of the root cause compelling already during planning, for oneself and for third parties. Only the identified root cause allows to find the best problem solution in terms of function, speed, quality, costs, and risks.
The limitations of conventional product problem solving and the associated waste are not always so obvious. Augustine, CEO of several aerospace companies, noted that the cost of testing low-cost products is immensely high compared to expensive ones. Low costs of prototypes seduce to an approach of multiple trials with subsequent errors. This is usually a high and concealed waste.
You can acquire the methodology of Statistical Engineering in four ways. For example, I give lectures on Statistical Methods each winter semester at RWTH Aachen University. Or you can find them in the reference book Statistical Engineering, available at Amazon. Or I advise you in solving specific tricky quality problems. Or opt for on-the-job in-house training where selected engineers solve several tricky problems that affect your company’s KPI.