Tricky process problems are like diamonds: very robust, very old and very expensive. They resist pragmatic problem solving in product engineering and production because of repeated erroneous assumptions about the root causes. The good and highly significant message: Planned problem solving by Statistical Engineering forces the uncovering of the real root cause also from the point of view of third parties.

Knowing the real root cause allows you to find the best problem solution in terms of function, speed, quality, cost, and risk.

In my professional life, I solved more than 100 tricky quality problems that had survived pragmatic problem solving. For example, an automotive supplier had far too many 0 km and field failures in mass production. The pragmatic analysis of multiple complaints by means of 8D reports, Failure Tree Analysis, and operational know-how improved nothing. The customer became very critical. By application of Statistical Engineering, I was able to uncover the real root cause in the early start of production and to permanently eliminate the failures.

Another example: A car manufacturer painted body parts made of new plastic in a novel process. Sometimes paint bubbles formed. The problem had been unresolved since the product was developed. A masterand solved it in the factory by means of Statistical Engineering. Initially, he had in mind an approach using Design of Experiments. However, he chose Statistical Engineering, because the rapid success was forced and thus predictable.

For the quick and sustainable solution of tricky process problems Statistical Engineering is often more efficient and cheaper than machine learning. Statistical Engineering is applicable even if prior knowledge and data are missing. It simultaneously accesses all principles of human learning and accelerates it. In contrast, machine learning requires existing data, ignores causality, and applies only one of five learning principles at a time, as the computer scientist Pedro Domingos describes in “The Master Algorithm.”

The effort for Statistical Engineering is low. It includes practical learning based on specific problems with the help of textbook, training or advice. On the other hand, machine learning always requires one-time expenses and continuous maintenance costs.

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.