Tolerance stack and Monte Carlo explorer

Assumptions to check

• The supported formula is a two-term sum.
• Worst-case bounds are bounded endpoints.
• The local Monte Carlo model uses independent uniform distributions and the shown seed.

Different methods answer different questions

Nominal, worst-case, RSS, and Monte Carlo are not competing names for one universal tolerance result. Each relies on a different assumption. Worst case evaluates every endpoint, RSS assumes independent small-error terms, and Monte Carlo expresses a chosen distribution and sample count.

This engine keeps those distinctions visible. The random sample is deterministic for the entered seed so test and review cases can be reproduced locally, but its distribution is only as credible as the input assumptions.

Use the method that fits the decision

Use a bounded worst case when every limit must be satisfied. Use RSS only when independence and the meaning of each entered spread are defensible. Use the sampled result to explore a stated distribution, not to claim yield or qualification without an appropriate production model.

State what the range means before using it

Use the nominal sum as a reference only. A worst-case range is appropriate when each input may independently sit at a stated endpoint and every outcome must satisfy a requirement. RSS is meaningful only when the spreads are independent and its statistical interpretation is justified. The deterministic Monte Carlo result is useful for exploring the entered uniform distributions and reproducing a review case, but it is not a yield prediction without credible distributions, correlation, and acceptance criteria.

Keep the formula, units, tolerance definitions, correlations, sample count, seed, and pass or fail limit with the result. This starter engine supports a two-term sum so its assumptions are visible. Extend the analysis carefully for products, ratios, nonlinear transforms, temperature drift, correlated references, and selection bins. Validate the leading contributors with actual component data and measurements before basing production risk on a simulated spread.

Common mistakes

• Calling an RSS result a guaranteed range.
• Hiding correlation between input terms.
• Using a random sample without recording the distribution or seed.

Model limit and handoff

Document distributions, correlations, limits, and priorities, then validate the leading design with data sheets and representative measurement.

FAQs