Feb 11 2022
Scientific Thinking and Manufacturing Improvement
“Scientific thinking” appears more and more in discussions of Lean, Kaizen, or TPS. What is it? Well, it’s the way scientists think. In reality, however, talk to actual scientists about PDCA, DMAIC, the 8D, A3 thinking, Why-Why analysis, TRIZ, or even statistical design of experiments, and their eyes glaze over. Most will have no idea what these methods are. This is true for physicists, chemists, biologists, or even economists. If you elaborate, they will dismiss these tools as trivial or devoid of any connection with their work.
Improving how things are made does make the world a better place but it’s not science. By growing a body of knowledge that is our greatest asset as a species, scientists make another contribution, that we should recognize as different.
- Science Versus Technology
- Scientists About Science
Science Versus Technology
As discussed in an earlier post, manufacturing is more about technology and management than science. Technology gets objects to do what we want them to; managers work with people. Science is about understanding how nature works, not about making things work.
It is all in the old joke about a scientist, a high-level engineer, and a basic engineer each designing a bridge:
- The scientist’s bridge collapses but he explains why.
- The high-level engineer’s bridge collages and he doesn’t know why.
- The basic engineer’s bridge holds but he doesn’t know why.
When you design a bridge, you use Civil Engineering, which draws several branches of physics:
- Classical mechanics, for the forces applied to the bridge structure from its own weight and from its intended use.
- Strength of materials and elasticity, to calculate how the elements of the structure will bend, compress, expand, twist, and vibrate in response to the load.
- Fluid dynamics, for the response of the bridge to the flow of water and to wind.
- Seismology, if the bridge is in an earthquake zone.
From the Gard bridge to Tacoma Narrows
The goal is to design a bridge that will do its job and last, and science provides tools to do it. It is, however, a technological goal, not a scientific one. It’s about building something, not finding laws of nature, and the relationship between the two is not straightforward. None of the science underlying today’s civil engineering was available to the designers of the bridge over the Gard river, 2000 years ago in Southern Gaul:
As an aqueduct, it stopped delivering water 1400 years ago but still stands. On the other hand, most of today’s science was available to the designers of the Tacoma-Narrows bridge near Seattle in 1940. Yet it collapsed under the effect of a sustained 40 mph wind three months after opening:
We are used to thinking of technology as the application of preexisting science and it’s often true but there are many cases where it’s not. Gunpowder predates chemistry and steam engines thermodynamics. Just because something works doesn’t mean it’s based on science. When science catches up, however, improvements follow. Managers and engineers like to wrap their ideas in the mantle of science but it’s neither always justified nor always necessary.
Scientists About Science
Collectively, scientists are responsible for setting what society accepts as the laws of nature — that is, theories that can be used for predictions that pan out. And these theories help when trying to build something. It’s a body of knowledge that grows with new discoveries that scientists must recognize from among many claims, which requires them to be both open-minded and rigorous. They must neither rigidly stick to established ideas nor accept any half-baked theory, nor yield to religious, economic, or political pressure. Walking this tight rope has always been a challenge, and figuring out how to do it properly has occupied thinkers since Aristotle.
Richard Feynman’s Short Version
Richard Feynman, lecturing at Caltech in 1964, had a short version of the scientific method:
His sentence, “If it disagrees with experiments, it’s wrong,” is key, because he didn’t say that it’s right if it does agree with experiments. Disagreement with experiments or observations proves a theory wrong but agreement doesn’t prove it right. Astronomer Arthur Eddington’s observation in 1919 that the mass of the sun deflected light supported Einstein’s theory of General Relativity but didn’t prove it.
Deming’s Thinking About Truth
W. Edwards Deming got a Ph.D. in theoretical physics at Yale in 1928. He trained to be a scientist but didn’t work in research afterward.
Deming’s Statement About True Values
In his 1986 foreword to Shewhart’s Statistical Method from the Viewpoint of Quality Control, he made a surprising statement:
“There is no true value of anything. There is, instead, a figure that is produced by application of a master or ideal method of counting or of measurement. This figure may be accepted as a standard until the method of measurement is supplanted by experts in the subject matter with some other method and some other figure.
There is no true value of the speed of light; no true value of the number of inhabitants within the boundaries of (e.g.) Detroit. A count of the number of inhabitants of Detroit is dependent upon the application of arbitrary rules for carrying out the count. Repetition of an experiment or of a count will exhibit variation. Change in the method of measuring the speed of light produces a new result.”
What Did He Really Mean?
Of course, results change when you take multiple measurements of the same variable, even when you use the same method but does it mean that there is no true value of anything? The speed of light in vacuum and the number of people present at any time within the boundaries of Detroit exist independently of our ability to measure them. We know these quantities only through measurements that give you rational numbers with margins of error but these measurements are supposed to be approximations of something that is real.
Deming is more specific in A System of Profound Knowledge (p. 104) where he says “there is no true value of any characteristic, state, or condition that is defined in terms of measurement or observation.” That’s why you need theories. Results do change when you take multiple measurements of the same variable, even when you use the same method. It doesn’t mean, however, that there is no true value of anything.
The Length Of A Rod
The length of a rod is a simple example, because it is an object we design to have a specific length. The rod concept is a first-level abstraction, just one step from the physical object. When we pile abstractions on top of other abstractions to come up, for example, with unit costs for manufactured goods or with ratios like Return On Net Assets (RONA). We have a much harder time mapping these to physical reality.
We need a theoretical model within which the numbers have a meaning, which was the point of Safety Stocks: Beware of Formulas. Formulas can always produce numbers. Adjusted Gross Income (AGI) is, by definition, the result of a formula based on a taxpayer’s declarations of economic activity. What makes it true is validation by the tax authorities. Unlike the length of a rod or the speed of light, the AGI has no existence outside the rules used to calculate it and no one could invent a more accurate way to calculate, because it is nothing but what the rules say it is.
The Double Helix
Scientists’ concerns are not easy for others to understand but popularization books like James Watson’s The Double Helix or Francis Crick’s What Mad Pursuit provide a useful window. The structure of DNA may be the most celebrated discovery of the past 70 years but, back at Cambridge in 1953, how did Crick and Watson know they had it right?
Crick And Watson’s Evidence
Based on Watson’s account, they had circumstantial evidence:
- The structure was compatible with the X-ray diffraction pictures of crystallized DNA taken by Rosalind Franklin.
- They knew that DNA molecules are built from four bases, A, T, G, and C for short. Crick & Watson’s structure explained Erwin Chargaff’s observations that the four bases were contained in pairwise matching quantities in any DNA, where every molecule has as many As as Ts, and as many Gs as Cs.
- The structure was an acid, while the one earlier proposed by Linus Pauling wasn’t.
- It was compatible with what chemists knew about distances and relative orientations of atoms within molecules
- It provided a plausible means of replication by “unzipping” the two strands of the double helix.
Whose Shoulders Did They Stand On?
From their own accounts, Crick and Watson were neither chemists nor biologists, yet, together, as Crick put it, they “found the secret of life.” It was, indeed, the greatest discovery in biochemistry, ever. It makes you wonder how they thought. They stood on the shoulders of many others:
- Linus Pauling had drawn attention to helical structures for biological molecules. He had also pioneered the use of physical 3D models, as opposed to formulas on paper flatland.
- Lawrence Bragg, Crick and Watson’s lab director in Cambridge, had invented X-ray diffraction techniques in crystallography.
- Maurice Wilkins and Rosalind Franklin had applied X-ray diffraction to DNA.
- Crick and Watson relied on chemists to validate their molecule geometry.
They pulled together all these elements into a simple theory that explained all that was known up to that point. It has, since, aged well.
Advances in physics, chemistry, or biology have challenged scientists to find new ways to validate theories. Meanwhile, science has expanded into new domains like human beings and the structures they create. These include nutrition, psychology, sociology, economics. These “sciences of the human” are more difficult. Results that are neither trivial nor false are harder to come by than in physics or chemistry.
Science, Data, and Intellectual Property
Data is the lifeblood of research. Collecting it is expensive, tedious, and time-consuming. For the advancement of science, ideally, scientists should share all this data. In reality, they are less than enthusiastic about it, at least initially. They first want to take a crack at analyzing the data, making it talk, and publishing their results. “You are not a team player,” the others will say to the ones who collect the data. Those who took the trouble don’t want freeloaders using their data to take the lead, and the glory.
Then, once scientists publish results, others come out with numerous claims and counterclaims of priority. Where economic fallout is at stake, these disputes degenerate into lawsuits. Unless they are in a truly obscure specialty, scientists must both cooperate and compete with others. It affects the methods they use.
Scientific research is a different world, with different rules that we need to acknowledge. Scientists compete for glory, not money or power. Science delivers a middle-class lifestyle but not riches. Science does not lead to power either, and scientists almost never turn into effective politicians. Scientists compete for recognition from peers, research grants, tenures, awards and prizes. This competition is as fierce as any in business or politics but it is a different game.
#engineering, #management, #manufacturing, #scientificthinking, #technology
February 11, 2022 @ 10:49 am
Thanks Michel for another provocative post. However, I would modify your statement of “scientific thinking” as the way scientists should think. Regardless there are many reasons their eyes would glaze over when they see what people call PDCA because most PDCA is PDCA in management rhetoric alone. In practice it is more often PDHM, which is Plan, Do, ignore the output and just Hope and Move on. Or it might be PDCRAS which like PDHM is Plan, Do then Record And Save the data. Or it could be PDCI, which is Plan, Do, Check and Ignore or finally when people are actually moved to action, but don’t really know what to do (that is they cannot separate common cause variation from special cause variation) then you get PDCT which is Plan, Do, Check and Tinker.