Monte Carlo simulation is a powerful tool that can be used to simulate complex systems and scenarios in a relatively simple way. It is named after the famous Monte Carlo Casino in Monaco, where the roll of dice or spin of a roulette wheel is determined by random chance. Similarly, the Monte Carlo simulation uses random numbers to simulate the behavior of a system or scenario many thousands of times, collecting data and analyzing the results to provide valuable insights into potential outcomes.

This approach can be applied to a wide range of real-world conditions, from financial investments and market forecasting, to the design of new technologies and the optimization of manufacturing processes. By running thousands of simulations and analyzing the data, Monte Carlo simulation can help decision-makers to identify risks, improve decision-making, and ultimately achieve better outcome

RSI’s patented predictive analytics technology leverages Monte Carlo simulation to build probability functions for both our clients’ demand pattern and replenishment supply chain. Demand and supply information vary on each day of the simulation utilizing values from each of these probability functions. Values are represented based on probability, so a demand value closer to average demand for the item will be represented more often than a value in either tail of the distribution.

Ultimately, this method ensures the answers are robust as they have been ‘stress tested’ through many simulation scenarios to ensure the answer has the highest probability of being the right answer. In addition, Monte Carlo simulation in RSI delivers a distribution of possible outcomes around the answer. This distribution effectively acts like control limits for critical metrics like ‘average days on hand’.