Nov 22 2016
If Talk Of Probability Makes Your Eyes Glaze Over…
Few terms cause manufacturing professionals’ eyes to glaze over like “probability.” They perceive it as a complicated theory without much relevance to their work. It is nowhere to be found in the Japanese literature on production systems and supply chains, or in the American literature on Lean. Among influential American thinkers on manufacturing, Deming was the only one to focus on it, albeit implicitly, when he made “Knowledge of Variation” one of the four components of his System of Profound Knowledge (SoPK).
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 • • Tags: Bayesian Statistics, data science, Probability, statistics