Monte Carlo Simulation

simulation Monte Carlo is a computational technique that models the probability of different outcomes by running thousands of simulations with random variable inputs. In Amazon FBA analysis, it models the range of possible financial outcomes by varying price, volume de ventes, ACOS, and costs simultaneously.

Why Monte Carlo Simulation Matters for Amazon Sellers

Traditional financial projections use single-point estimates that create false confidence. simulation Monte Carlo reveals the full probability distribution of outcomes, showing the likelihood of profitability and the downside risk — critical information for investment decisions.

How RIDGE Analyzes Monte Carlo Simulation

RIDGE runs 10,000-iteration simulation Monte Carlos as a core analytical component, generating P10 (pessimistic), P50 (median), and P90 (optimistic) financial projections. This is a key differentiator from tools that only provide single-point estimates.

Exemple Pratique

A simulation Monte Carlo for a resistance band product might show: P90 (optimistic): $11,200/month revenus, P50 (median): $5,100/month, P10 (pessimistic): -$900/month. This reveals a 67% probability of profitability and a 10% chance of net loss.

Questions Fréquemment Posées

Why is Monte Carlo better than average-based projections?+

Average-based projections hide risk. A product with 'average' expected revenus of $5,000/month might have a 30% chance of losing money — information that averages do not reveal. Monte Carlo shows the full probability distribution.

What variables does RIDGE model in Monte Carlo?+

Key variables: prix de vente (competitive dynamics), daily volume de ventes (demande uncertainty), ACOS (advertising efficiency), COGS (fournisseur variability), and FBA fee changes. Each variable has a probability distribution derived from market data.

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