“Talk to the parts, not to the engineers” - Dorian Shainin (1914 - 2000)
About Dorian Shainin
Dorian Shainin (1914 –2000) was an American engineer of Japanese descent who was interested in improving quality and reliability; he also established several fundamental quality paradigms. In addition to his career as a professor at the University of Connecticut from 1950 to 1990, he was also a consultant to numerous well-regarded industrial companies: NASA, Rolls-Royce, Exxon, Ford, Polaroid, Hewlett-Packard, AT&T, and more.
A number of quality assurance and improvement methods can be credited to Shainin’s work. Many of these methods were integrated into the SIX SIGMA quality philosophy from the 1990s onwards, with the advent of electronic data processing and the possibilities of using statistical methods.
The methodology: Shainin variable and component comparison
Shainin variable and component comparison is an effective, simple and therefore sustainable method developed by and named after Dorian Shainin; unfortunately, it is rarely used today.
As early as the 1950s, Dorian Shainin discovered that – in most cases – it is only a few factors that play an important role in production processes. Shainin dubbed these factors the ‘RedX’. Another term, Pink X, was later added to describe factors that are less dominant or that, in interactions with other factors, contribute significantly to process variation.
The methods
- Variable comparison for process improvement and
- component exchange for product improvement
The training covers practical examples with one and with several Red Xs; it includes cases with and without interactions, all of which are discussed in detail.
The training also highlights the limitations of Shainin's methodology.
Learning objectives
- You will become familiar with the Shainin variable comparison and component exchange methods.
- You will learn under which conditions this type of experimental design can be used.
- You will become familiar with the fundamental value D/d ≥ 5 and will be able to determine this value based on the available experimental data.
- You will be able to identify Red X and Pink X using experimentation.
- You will learn the essential phases of the experimental design methodology according to Shainin.
- You will learn alternative methods for situations where the D/d ≥ 5 condition is not met.
The core content of the one-day training is learning about the Shainin variable and component comparison methodology. The methodology allows the user to identify Red X and Pink X using simple means. The method should nevertheless be considered in the context of today’s tools.
Variable comparison and component exchange are two methods that fall under the category of simple experimental strategies: With little effort, they allow the identification of the most important factors, which in turn makes it possible to recognize the causes of process variation and product failures.
According to Shainin, root-cause analysis is a pragmatic approach for quickly and systematically isolating errors and identifying the root cause(s).
The advantage of the Shainin method are obvious:
- In line with actual practice, with a limited scope of experimentation
- Error-oriented
- Pragmatic troubleshooting
- A systematic, structured and comprehensible approach
The goal is to develop a technical and functional understanding of the process and of how the relevant parameters interact. This can explain how and why a desired product or process result is produced. It is then also possible to explain how, why and under which conditions errors occur. Building on this understanding of function, we develop functional hypotheses and test them using corroborative measures.
The Shainin method is often used in the context of other effective methods today.
Shainin helps to focus the limited resources available within companies. Potential supplementary methods include process analysis in the form of process maps, cause-effect matrices, Ishikawa diagrams and, in particular, TRIZ functional analysis, as well as the innovative solution principles from TRIZ.
Engineers, production supervisors, quality assurance personnel, and anyone interested inor responsible for fast, effective, and practical test procedures.