This is the Electe Review. Today, a simulation can run flawlessly and still mislead you because output precision can exceed assumption quality. Start with the retailer in the article. Peak season is coming, and demand could land anywhere between 800 and 1400 units. The spreadsheet says 100. That sell looks precise, but it has answered a narrower question than the business asked. Land near the low end, and cash sits in unsold stock and markdowns. Land near the high end, and you pay in stockouts, lost sales, and customer trust. Monte Carlo simulation replaces the single number with a distribution. Draw a value from an input distribution. Run the model, store the output, repeat. The question changes from how many will we sell to what inventory level gives an acceptable balance between shortage and excess? The article's Python example is deliberately crude. 10,000 seasons, demand drawn uniformly between 800 and 1,400, inventory fixed at 1,1100 he shared. Units sold as the lower of demand and stock. Mechanics only. A production model has to justify the distribution. The method is not new. Buffon's needle, 1777. Ulam and von Neumann at Los Alamos on OPA among neutron transport. The first computerized runs on ENIAC in 1948. Then the 1953 paper on equations of state. Now the skeptical part. The engine samples exactly as instructed. If the instructions describe the business poorly, the output inherits that weakness. A 2026 methods paper names the constraints. Slow convergence, dependence on accurate input distributions, computational cost. Three failure points follow. Input distributions set the ceiling, fit a neat curve to a short sales history, and you get a polished histogram and false confidence. Correlation shapes outcomes. Demand and price move together, cost and schedule move together. So independent draws invent combinations that never occur and omit the ones that hurt. Stopping rules protect the tails. A 90% interval narrows because sampling noise fell, not because the model improved. The mistakes are ordinary. Treating output as a point estimate, ignoring correlation, defaulting to a normal curve because it is familiar. Chasing iterations instead of better inputs. Skipping tail convergence checks. Uncertainty is not error, and more runs cure only the first. So pilot one decision, document assumptions and owners, validate against history. The question is never whether you ran enough iterations, it is whether the result is stable enough to act on. That's the review.