Monte Carlo Simulation

Monte Carlo Simulation is a statistical technique that allows for the modeling of complex systems and processes by using random sampling to obtain numerical results. It is often employed to estimate the probability of different outcomes in situations that involve uncertainty, thereby enabling better decision-making.

The method works by running a large number of simulations (or trials) with randomly generated inputs to assess how these inputs influence the outcome. The results are then aggregated to provide insights into the range and likelihood of possible results.

Examples:

• Finance: Monte Carlo simulations are used to assess the risk and return of investment portfolios by simulating different market conditions and Asset price movements.
• Project Management: In project scheduling, Monte Carlo methods can predict project completion times by accounting for uncertainties in task durations and resource availability.

Cases:

• Insurance: Insurers use Monte Carlo simulations to model the likelihood of claims and determine appropriate premiums based on risk assessments.
• Manufacturing: Companies apply Monte Carlo simulations to optimize production processes by analyzing variations in machine performance and Supply Chain dynamics.