A supply chain risk simulator is a tool that models disruption scenarios — port strikes, raw material shortages, geopolitical conflicts, natural disasters — and calculates their probabilistic impact on supply chain operations. SCRRS uses Bayesian inference to update risk probabilities as events unfold, Monte Carlo simulation for stochastic scenario testing, and Value-at-Risk (VaR) analytics to quantify financial exposure at the SKU level.

Bayesian risk analysis updates prior probability estimates (your baseline risk levels) with new evidence (disruption events) using likelihood ratios. In SCRRS, when a disruption occurs, the system converts priors to odds, multiplies by the event's likelihood ratio for delay, stockout, and overstock, and converts back to posterior probabilities. This gives the updated probability of each risk outcome, accounting for your structural decisions (supplier count, transport mode, inventory buffer) and active mitigations.

Supply chain VaR quantifies the maximum expected financial loss from disruptions at a given confidence level. In SCRRS, it is calculated as Order Volume × Item Value × Risk Probability for each SKU line. This helps supply chain leaders prioritise mitigation investments based on total exposure magnitude rather than probability alone — a $500K loss at 80% probability demands different action than a $5M loss at 10%.

SCRRS includes 45 calibrated disruption scenarios across 9 categories: Production (5), Transport Routes (5), Warehouse (5), Supplier (5), Factory (5), Politics (5), Finance (5), Compliance (5), and Country-level risks (5). Each scenario has 6 mitigation strategies with cost and effectiveness parameters, giving you 270 possible mitigation decisions to optimise against.

Yes. SCRRS is 100% free, requires no signup, and runs entirely in your browser. No data leaves your device — all computation happens client-side using JavaScript. You can also upload your own CSV dataset for custom SKU-level risk analysis. The tool is provided by Mathnal Analytics as part of our free diagnostic suite for supply chain professionals.

Start with the Global Twin tab: set your supplier count, transport mode, and inventory buffer to establish baseline priors. Use the Crisis Injector to queue disruption events, then click "Advance Month" to run the Monte Carlo simulation. The Bayesian gauges update in real-time showing P(Delay), P(Stockout), and P(Overstock). Switch to the Mitigation Engine tab to assign and optimise mitigation strategies. Use the Data Analytics tab to load the built-in dataset or upload your own CSV for SKU-level VaR analysis.

Monte Carlo Sim mode runs a 12-month stochastic simulation where random events are injected by the environment each month, capital is consumed, trust erodes, and you must react in real-time — modelling the unpredictability of real supply chains. Manual Sandbox mode lets you hand-pick specific disruption scenarios, apply mitigations, and calculate the total expected loss without randomness — ideal for deterministic scenario planning and "what-if" analysis before presenting to leadership.