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Lead-Time Stress Simulation

Simulation model — instantiates Sustainment-Reach Alignment

Replays a planned reach against surges and shocks in supply lead time and demand to find where the line breaks and how much buffer survives it.

A support line can look perfectly adequate on average and still fail the first time anything goes wrong, because reach failures are driven by variability, not means — a line sized to the average strands the front the week lead times double. Lead-Time Stress Simulation stress-tests a planned reach by replaying it many times against distributions of lead time, demand, and disruption rather than single expected values, and reports where and how often the front runs dry. Its distinguishing job is to find the robust crossover — the reach that still delivers net support under adverse conditions, which sits well short of the reach that pencils out on paper — and to size the buffers that would move it back out. It is offline analysis, a rehearsal of futures, not a live gauge of the line as it runs today.

Example

A consumer-electronics firm plans to expand contract manufacturing from one hub to sites on three continents, lengthening every replenishment path. On a spreadsheet of average lead times the plan clears comfortably. Before committing, the team runs a lead-time stress simulation: thousands of runs in which ocean lead times spike with port congestion, a customs hold adds a random delay, one supplier goes dark for a few weeks, and demand jumps on a launch — with these shocks allowed to correlate, because they tend to arrive together. The simulation surfaces what the averages hid: at the full three-continent footprint, a routine three-week lead-time spike leaves assembly lines starved more than ≈15% of the time — the plan is fragile well before it is, on paper, infeasible. It also shows that ≈three weeks of forward buffer on the two longest-lead components restores the safety margin, letting the firm expand with eyes open rather than discover the fragility in production.

How it works

  • Distributions, not point estimates. Lead time, demand, and disruption enter as ranges and probabilities; the model samples them across many runs.
  • Shocks may correlate. Congestion, delay, and demand surges are allowed to arrive together, because assuming independence is exactly how a plan underestimates its tail risk.[1]
  • Output is a risk profile. Rather than a single verdict, it reports the reach at which failure frequency crosses tolerance, which segments break first, and how much buffer restores the margin.

Tuning parameters

  • Shock set — which disruptions are modeled (lead-time spikes, outages, demand surges); omit one and the reach looks safer than it is.
  • Severity and correlation — how extreme the shocks and how tightly coupled; the correlation assumption is the highest-leverage and most-abused dial.
  • Horizon — how far forward each run plays; short horizons miss slow-building depletion.
  • Run count — more runs sharpen the tails but cost compute; too few and the rare, decisive failures never appear.
  • Failure definition — what counts as the front "running dry" (a stockout, a missed SLA, a service-floor breach); this sets where the crossover lands.

When it helps, and when it misleads

Its strength is exposing fragility that averages conceal and translating it into decisions a planner can act on: how far is robustly sustainable, which segment fails first, how much buffer is worth it. It is the natural rehearsal before committing to a reach, and the supplier of the robust crossover the pace and gate mechanisms then enforce.

Its failure modes are those of any simulation: it is only as good as its distributions and its correlation assumptions, and a tidy failure-frequency number invites false confidence in inputs that were guessed. The classic misuse is tuning the shocks until the model blesses a reach already chosen — softening severities or assuming independence to make a fragile plan pass. The discipline is to source distributions from real disruption history where it exists, stress the correlation assumption rather than defaulting it to zero, and treat the output as conditional on inputs that must be defended.

How it implements the components

Lead-Time Stress Simulation fills the archetype's rehearsal-and-limit components — the ones that probe reach under stress:

  • reach_scenario_library — it builds and maintains the library of stress scenarios (shocks, severities, correlations) that reach decisions are tested against.
  • crossover_threshold — it locates the crossover under adversity: the reach beyond which net delivered support goes flat or negative once variability is admitted.

It does not gauge the live line (line_health_monitor — Net-Supply Ratio Dashboard), do the deterministic capacity accounting (operating_front_definition, net_delivery_metric, reach_cost_model — Line-of-Support Capacity Model), or act on its findings by pacing or rescoping the front; it is offline rehearsal, and its results are inputs to those.

References

[1] The tendency of demand and lead-time variability to amplify as it travels up a supply line — known in supply-chain management as the bullwhip effect — is why buffers sized to average demand fail under stress, and why the simulation must model variance and correlation rather than means. ↩