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Build Reliable Sales Estimates During the Pandemic

Updated: May 1

Monte-Carlo simulation as a technique for modeling demand




In this article, we will give an overview of the practical application of Monte Carlo simulations to build a sales forecast model. We will describe how to create a Monte-Carlo simulation to predict the probability distribution of a sales forecast for a particular product or service in a particular market. In WINNOVA, we use Monte Carlo simulation where we calculate the probability of a sale on each simulated day and run the simulation thousands of times. The result is a probability distribution of all possible outcomes, so we have to do it hundreds of thousands of times! If we analyze the Monte-Carlo simulations, the resulting probability distribution resembles a normal distribution.

When it comes to forecasting, rather than simply replacing uncertain variables with a single average, Monte Carlo simulation may prove to be a better solution, especially if we use multiple values. Monte-Carlo simulations calculate scenarios based on the definition of simulation parameters. They use the selected distribution and its parameters, automatically generate thousands of scenario iterations, usually in just a few seconds, and then use them in a series of iterations.

You can run them in a series of iterations, changing the underlying parameters used to simulate the data. You can also perform multiple iterations of the same scenario with different parameters to change their use for simulating data, and then perform them again in different scenarios without changing them. Monte-Carlo simulations show that each variable contributes to a particular outcome of the model. Monte Carlo simulations allow analysts to see exactly which input factors have values for certain results. They allow you to specify the variables and the relationship between them, and then use them to predict future sales of a particular product or service. Monte-Carlo simulation allows you to simulate dice to get more accurate predictions. It can help you to mimic combinations of variables in the real world and give you a reliable probability of the results of these combinations. In such situations, it can be applied to analyze the effects of randomness introduced into the model by such variables.


Conclusion

The past few months certainly haven’t been easy for businesses. Many had to shut their doors due to the coronavirus pandemic. While some may never reopen, others are ramping up their business — and hoping they don’t have to shut down again. This needs rapidly validating models, creating new data sets, and enhancing modeling techniques. Getting this right will enable companies to successfully navigate demand forecasting, asset management, and coping with new volumes.

For more effective forecasting, businesses need to examine how the pandemic has changed customer demand, habits and expectations, and how it has affected market supply of materials, goods and services. Which industries are thriving and which are suffering? Which businesses will either temporarily or permanently change how they operate and are there any that have gone away permanently? All these factors will affect staffing, financing and other management decisions, so it is vital to have access to up-to-date, pertinent data in order to achieve more reliable forecasting results.




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