# Monte-Carlo Simulations in inventory management

I am using Monte-Carlo simulations in Microsoft Excel to determine optimum reorder points and safety stock levels. I have the demand patterns of the last one year of the product. Using that I can construct a cumulative distribution function of the demand to draw random samples from and construct a table of demand on each day for a whole year.

One problem that I found was that the simulation is based on demand patterns alone. That is, if the company did not forecast at all, then the individual runs of the simulation would generate the demand pattern that they can expect in a year. However, if the company is able to forecast demand with 100% accuracy, then there would be no need to keep a safety stock (or very little of it). Forecast accuracy is something I am not sure how to incorporate in my model. There are formulas for calculating safety stock such as using the Mean Absolute Deviation from the forecasted demand but I would like to develop a simulation model that takes into account forecast accuracy.

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– prubin
Commented Jan 4, 2022 at 16:51

Why not build the forecasting directly into your simulation? So, in each period $$t$$, you generate a forecast $$y_t$$ using whatever method you want (moving average, exponential smoothing, etc.), and choose an order quantity based on the forecast and the current estimate of the standard deviation of the forecast error. Then generate the random demand, calculate the forecast error, and update the estimates of the SD of the FE.