How Flow Works and other curiosities
summary
Work arrives and moves as information through value streams, with an example showing 5 days of value work buried under 42 days of wait for a 47‑day lead time; shared test environments cost queues and are recommended shared test environments nuked from orbit. Queuing theory (MM1/MMC), congestion collapse and Little’s‑law effects explain why AWS scales by making C→∞. An agent-based simulation (prototype in C inspired by NetLogo) compares single‑piece flow to 100% batching and shows batch size reduction yields orders‑of‑magnitude more cumulative value.
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chapters (11)
- 0:00Introduction and context
- 3:19Work flows as information
- 10:00Value stream complexity
- 18:05Shared test environments problem
- 20:40Queuing theory explained
- 25:58Generative science introduction
- 28:46Congestion collapse in teams
- 32:14Queue size vs cycle time
- 35:44Batch size reduction benefits
- 37:07Simulating work with agent models
- 40:35Summary and LEGO analogy
