In situations where resources are limited to gathering real data would be too expensive or impractical, Monte Carlo simulations can help forecast results and probabilities. 

Testing potential improvements can get complicated when working with multiple suppliers in different steps of a process. Using a Monte Carlo Simulation can help illuminate the results you’d like to see.

How do you commit to realistic forecasts and timelines when resources are limited or gathering real data is too expensive or impractical? Can simulated data be trusted for accurate predictions? That’s when Monte Carlo simulation comes in.

In the manufacturing industry, unknown risk can create considerable concern and hand-wringing.

Monte Carlo simulations, named for the city in Monaco, were developed during the 1940s with the Manhattan Project.