Risk-Weighted ROI Monte Carlo Simulation Starter Model

This model bridges traditional capital budgeting with modern risk analytics by replacing deterministic assumptions (e.g., 'unit cost = $12.50') with probability distributions (e.g., Normal(μ=$12.50, σ=$0.80) or Triangular(min=$11.20, mode=$12.30, max=$14.10)). Core inputs—such as equipment CAPEX, maintenance OPEX, production yield, scrap rate, demand volume, and selling price—are parameterized using empirical data or expert judgment, enabling thousands of randomized trials to generate a full distribution of possible ROI outcomes. The 'risk-weighted' aspect goes beyond standard deviation or Value-at-Risk (VaR); it incorporates decision-theoretic adjustments—such as certainty-equivalent ROI (using exponential utility), downside deviation penalties, or conditional value-at-risk (CVaR) scaling—to reflect how risk aversion impacts strategic prioritization. In manufacturing contexts, the model supports comparative evaluation of automation upgrades, line expansions, or supply chain resilience investments, where uncertainty stems from machine reliability, labor variability, material volatility, and regulatory shifts. Validation is performed via convergence diagnostics (e.g., stable 95% confidence intervals after ≥10,000 iterations) and stress-testing against historical failure modes (e.g., simulating unplanned downtime events sampled from Weibull-distributed MTBF data).