Forecasting Accuracy: How to Manage Demand Outliers
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Forecasting accuracy is the process of calculating the accuracy of your forecasts with regard to actual customer demand for a product. Outliers in forecasting are data points that are not considered to be part of the overall pattern of demand, for example, sales transactions that result from an end of year clearance sale do not represent typical demand for a given period.
Since outliers can affect and skew forecast accuracy, it can be useful to exclude them from your overall forecasting calculations. For example, in EazyStock’s demand forecasting module, you can automatically exclude outliers to improve forecast accuracy.
Whereas some fluctuations like seasonality of demand can be tracked in patterns over time, outliers are less predictable. Unusual demand can be caused by events of which you have knowledge of (e.g., sales promotions, large one-time orders, etc.) or can be caused by events of which you have no knowledge of (competitor promotions, customer going out of business, natural disasters, etc).
Why is it important to keep a close eye on demand outliers? If you do not properly flag or monitor outliers in your demand patterns, you will overtime distort your inventory forecasting accuracy which could inevitably create large quantifies of excess inventory that costs you money and hits your profitability margin.
Example of Demand Outliers Being Flagged in EazyStock’s Inventory Optimization Software
For instance, in the example above EazyStock can automatically flag demand outliers in your data sets to ensure they do not accidently influence your forecasts and alter your forecasting accuracy. Be sure that you have a proper system in place to track and flag inventory demand outliers, or you run the risk of inflating your forecasting to levels that will not be healthy for your business in the long run.
There are different ways to detect outliners, but the important thing is that you incorporate a process to flag outliers in your forecasting or it can have a significant negative impact on the accuracy of your forecasts. Unusually high or low demands need to be identified as that demand should not to be used when forecasting calculations.
Need help with your demand forecasting accuracy?
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