Feb 23 2022
Standards, China, and the Industrial Revolution
[Featured image: a Han dynasty bronze ruler]
As a general principle, in manufacturing, you need to do the work the same way every time if you want the output to be consistent. In some cases, like extracting metals from ores, you need to tweak processes to produce consistent output from raw materials of varying compositions. Then the tweaks themselves must be executed consistently so that the response to a particular variation in ore content is always the same.
Standards are an area where China had a 2,000-year headstart but neither the scientific nor the industrial revolutions occurred there.
Jun 12 2022
Perspectives On Probability In Operations
The spirited discussions on LinkedIn about whether probabilities are relative frequencies or quantifications of beliefs are guaranteed to baffle practitioners. They come up in threads about manufacturing quality, supply-chain management, and public health, and do not generate much light. Their participants trade barbs without much civility, and without actually exchanging on substance.
The latest one, by Alexander von Felbert, is among the more thoughtful, and therefore unlikely to inspire rants. I do, however, fault it with using words like “aleatory” or “epistemic” that I don’t think are helpful. I am trying to discuss it here in everyday language, and to apply the concepts to numerically specific cases, with an eye to operations.
While there are genuinely great and not-so-great ideas, the root of the most violent disagreements is elsewhere, with individuals generalizing from different experience bases. You may map probability to reality differently depending on whether you are developing drugs in the pharmaceutical industry, enhancing yield in a semiconductor process, or driving down dppms in auto parts. The math doesn’t care as long as you follow its rules, and it doesn’t invalidate other interpretations.
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By Michel Baudin • Data science • 0 • Tags: Bayesian Statistics, data science, Probability, statistics