Dependent Events and Variation

When steps depend on one another and each varies statistically, delays accumulate and rarely cancel out, because a step can only catch up so far but can fall behind without limit. The combination of dependency and variation, not either alone, is what drags a system down.

Why it Matters

It explains why a chain of individually capable resources still underperforms: fluctuations at earlier dependent steps propagate and compound. The slowest dependent step paces everything, and buffers, not just average capacity, determine real output.

Signals

• A line that lags its theoretical capacity.
• Variability at one step rippling downstream.
• Gaps that never get made up while delays always do.

Benefits

Reframes performance around variation and dependency, motivating buffers at the constraint and reduced fluctuation rather than just faster averages.

Risks

Planning on average capacity as if steps were independent and steady; adding work without protecting the constraint from upstream variation.

Tensions

Reducing variation and adding buffers costs slack and inventory, which competes with lean utilization.

Examples

The boy-scout hike where the slowest walker and random pace gaps stretch the column; a software pipeline whose stages each vary, so end-to-end time exceeds the sum of average stage times.