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Overview

A single simulation run is one random draw from your model’s behavior — one path through every stochastic primitive: distributional processing times, probabilistic routing, sampled arrivals. Monte Carlo runs the same configuration many times with different random seeds and reports the distribution of outcomes: central tendency, spread, and — through the charts you author — the shape of the tail. That’s the difference between “throughput is 847 units/day” and “throughput is 847 on average, and here’s how often it lands below 800.” Monte Carlo is the tool for single-configuration uncertainty quantification — it answers how tight is this number? To compare different configurations, use Experiments, which answer does this change move the metric?
Monte Carlo varies the random seed, not your parameters. For parameter sweeps (what if I add a CNC machine?), capture multiple Snapshots and compare them in an Experiment — optionally with a Monte Carlo batch per Snapshot.

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Before You Run

  • Stochastic content is required. A fully deterministic model and schedule produce N identical replications — if nothing re-rolls per seed, there is no variance to measure. Add distributional processing times, probabilistic routing, or randomized arrivals first.
  • KPIs are captured at queue time. The batch aggregates the KPIs defined on the model at the moment it’s queued; there is no way to add a KPI to a completed batch. Confirm the KPIs you want distributions for exist before queueing.

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Running a Batch

Monte Carlo has its own Monte Carlo section in the left rail, and is also reachable from the Monte Carlo button in the top bar of any Run’s Results page — whether that’s a plain run or a Snapshot’s run inside an experiment. The Run Monte Carlo modal takes an N Runs input — the number of seeds to sweep:
  • Default 100 — a solid starting point for central-tendency claims on most models.
  • 200+ — when you care about tail percentiles (P95 and beyond); tail estimates from small batches are noisy.
  • Hard maximum 1,024 per batch — queue multiple batches if you need more.
A batch runs against the model and, if your model uses one, its schedule — the schedule is optional, not required to launch a batch. Batches run asynchronously in the background; seeds land on the results page as they complete. Wall-clock time is typically several minutes, growing with seed count and model complexity.

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Reading Your Results

  • Per-KPI summary cards show the mean, standard deviation, min, max, and median (P50) for every KPI on the model. These render from a system-seeded summary query — no authoring required. Tail percentiles like P95 and P99 are not on the cards; those come from distribution or CDF charts that Dexter authors when you ask.
  • Distribution charts — histograms per KPI, box plots, and any Monte Carlo–scoped charts you author — render below the summary cards.
  • Per-seed rows click through to that replication’s full Run Results page — event log, per-run charts, everything — which is how you chase an outlier seed and understand what made it extreme.
  • Only successful seeds count. If some seeds fail, they’re excluded from every aggregate — the summary cards and every chart are computed over the seeds that completed. A batch with partial failures still reports as succeeded; the batch summary carries the total, successful, and failed seed counts, so check them before reading the sample size behind a statistic.

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Building Charts

Monte Carlo has its own chart-authoring view, distinct from Run charts — and in practice you build these charts by asking Dexter:
  • MC charts visualize across replications: histograms, box plots, scatters of one KPI against another, cumulative distribution curves.
  • Queries run against a per-seed KPI view — one row per seed and KPI — the same surface ad-hoc SQL uses. Filter by the exact KPI names from the batch summary.
  • MC charts auto-evaluate against every batch for the model, past and future: author a chart once and it appears on the results of every batch that has already run.
  • Author them through Dexter, the same way as Run and Experiment charts: describe what you want and Dexter writes the query.

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Interpreting Results

  • Tight distributions are reassuring. A small spread relative to the mean means the model is robust to stochastic re-rolls under this configuration.
  • Wide distributions are a signal. Stochastic primitives are driving material variance; a number quoted from a single run is misleading without a range around it.
  • Tails matter for SLAs. Central tendency doesn’t tell you how often you miss a commitment; percentiles do.
  • Identical distributions across two batches with a change between them mean the change doesn’t move the metric under this level of noise — often a genuinely useful finding.
Check convergence before quoting a tail percentile. If a P95’s rolling mean across seeds is still trending at the last seed, the number isn’t stable — run more seeds or quote a less tail-sensitive metric.

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Common Pitfalls

  • Confusing Monte Carlo with Experiments. Many seeds under one configuration versus many configurations. Use both when both questions matter.
  • Reading P95/P99 off too few seeds. Those percentiles come from distribution charts you author, not the summary cards — and tail estimates from 50 seeds are noisy enough to invert conclusions. Bump to 200+ and re-check convergence.
  • Adding a KPI after queueing. The batch only aggregates KPIs that existed at queue time. Define the KPI, then queue a fresh batch.
  • Results — the Run-scoped surface where individual seed runs render
  • Experiments — comparing multiple Snapshots side by side, with Monte Carlo per Snapshot
  • Snapshots — immutable model captures for comparison cases
  • Insights — durable dashboards independent of any single simulation