Systems and Delays

In terms of control theory, the order "delay" is actually a simple filter. What leads to bad outcomes is exactly the described scenario, when the control response too fast for the system, overcorrecting and oscillating. Increasing the order filtering dampens the control response.

It strikes me that a problem with this analysis is with the modeled example system itself. The assumption that stock should follow immediately preceding demand is valid but typically wrong.

Pursuing the intellectual mechanics of a system is obscuring the error of the wrong system!

Instead stock levels should take into account lead time, historical seasonal demand, and a subjective value. The observation and order frequency should be in months not days. While computing allows near “real time” everything, that doesn’t mean it should necessarily be applied.

Just-in-time thinking needs to take the reality of the system (in this case the supply chain) and other factors like hedging opportunities?

For people interested in the related math, "Nonlinear Dynamics and Chaos" by Steven Strogatz is a great book! Pdfs abound online.

Reminds me of this piece I read on hacker news over a decade ago:

https://mcfunley.com/whom-the-gods-would-destroy-they-first-...

Do people actually do systems design with control theory? I know the authoritative question to possibility - yes, my company does it. But I'm wondering about say, an industry standard formalism to it, such as like how DDIA is household

I think the source of confusion is that the "response delay divisor" isn't a delay at all. This parameter is the inverse of amplification. If you dampen a signal more then it will oscillate less.

Reminds me of The Beer Game https://en.wikipedia.org/wiki/Beer_distribution_game

very interesting read, key takeaway, delays are not always bad! Definitely something to think about in 2026