Why Average-Case Design Fails in Distribution Centers

Most distribution centers are designed for average conditions but operated under peak ones. The gap between these two states is where capital gets destroyed.

The Average-Case Trap

Conventional capacity planning relies on averaged demand, nominal labor attendance, and steady-state throughput assumptions. These figures are convenient — they reduce a design problem to a single number. But operations rarely behave like averages. Peak periods, labor shortfalls, and inbound surges are not edge cases; they are the conditions under which capital commitments are tested.

A facility sized to the average will perform adequately most of the time, and fail precisely when failure is most costly — during a seasonal peak, a labor disruption, or an unexpected demand surge.

Designing for the Distribution, Not the Point Estimate

An uncertainty-first design process treats variability as a foundational input rather than a post-hoc adjustment. This means:

• Modeling demand as a distribution, not a single forecast
• Evaluating designs against P50, P90, and P95 scenarios
• Quantifying the cost of underperformance at each percentile
• Making the trade-off between average-case efficiency and peak-case resilience explicit

What This Looks Like in Practice

Consider a facility evaluated under three design scenarios: the as-built baseline, a P50 design optimized for typical conditions, and a P95 design sized for high-demand periods. The P50 design may show stronger headline throughput numbers. But under P95 demand — the conditions that matter most for service commitments — the P95 design maintains performance within tolerance, while the P50 design’s gap widens substantially.

The choice between these designs is not a question of which number is “correct.” It is a question of which risks the organization is prepared to accept, and whether those risks are being made explicit before capital is committed.

The Cost of Silence

The most expensive assumptions are the ones nobody states out loud. When a design is presented as a single throughput figure, decision-makers cannot evaluate the risk they are implicitly accepting. When it is presented as a range — with the assumptions and their sensitivities made explicit — the same decision-makers can weigh the trade-off deliberately, and defend that choice later if conditions diverge from the average.

That is the difference between a design that happens to work, and one that was engineered to endure.