The Monte Carlo Method Simulation
At its core, Monte Carlo simulation is a computational technique used to approximate the probability of outcomes by running a large number of simulations using random sampling. It provides a way to model the impact of risk and uncertainty in quantitative analysis and decision-making processes. The method is based on the principle of statistical sampling, where random variables are repeatedly sampled to determine the range of possible outcomes and their respective probabilities.
It can be said that the Monte Carlo method is a way of predicting results that can assist in decision-making in a logistics process or project of an enterprise. There are, therefore, two aspects to consider in its application: scalability and precision.
Mathematically, a simulation results successfully if there is numerical convergence, otherwise it may not be applicable or the data scale must be resized, for example: if a simulation is for a period of one year, it must then be extended to five years.
