In supply chain management it’s important to be able to measure the accuracy of your demand forecasts. Inaccurate demand forecasting can lead to the accumulation of excess stock or, the reverse – issues with product availability. Both are unwelcome problems for inventory planners!
Improving forecasting accuracy should be a key objective for any inventory planning team. In short, accurate demand forecasting helps you:
You can calculate the accuracy of your inventory demand forecasts by comparing the original forecast with the actual demand for those items. If you find your forecasting accuracy is poor you can consider the following actions to improve it:
Forecasting accuracy can also be affected by ‘outliers’ or ‘fliers’ in the data. An outlier is a data point that is not considered to be part of the overall pattern of demand for an item. It is less predictable and usually a one-time event. Unusual demand can be caused by known events e.g. a large, one-time order or a flash clearance sale. Or they can be caused by events which you have no knowledge of e.g a customer going out of business, natural disasters etc.
A demand outlier can either be overly high or overly low compared to other data points in the data series.
Since fliers can affect and skew the accuracy of demand forecasting data, it can be useful to discard them from your overall forecasting calculations. However, this isn’t always the case, and on closer inspection some outliers may turn out to be a genuine demand pattern. Therefore, they often need examining to see if their usual behaviour can be explained.
It’s critical to flag and monitor demand outliers to prevent the distortion of your inventory forecasts over time. By identifying outliers you can improve forecasting accuracy and prevent over or under-stocking. Ultimately reducing the risk of either tying-up capital in excess inventory or experiencing out-of-stock situations.
There are a number of ways to detect demand outliers – from the very simple, to the very complex.
A basic method is to visualise the data and spot the fliers. A more complex option is to use statistical calculations. In this case an outlier is usually determined by the number of standard deviation away from the mean.
In the example above you can see historical data for a 12 month demand period and then the forecast looking forward.
The red bar has been highlighted as a potential flier, due to its obvious standard deviation away from the normal average demand.
As an inventory planner you have the option to remove this outlier from your future demand forecast and base your forecast on the blue bars. In the example above the orange line reflects this decision.
Or you could choose to ignore this outlier in your data, if you believe this demand pattern to be ‘normal’. For example, you may look back over data from previous years and see the same pattern occur annually. In this case you would include the outlier in your forecast.
Regularly reviewing every item in your warehouse to calculate forecast error, spot outliers and understand the causal factors can be a time-consuming job. However, it’s possible to use software to do this for you. Demand forecasting software, such as EazyStock, will automatically generate demand forecasts and then go back and check their accuracy – on a daily basis! It can also flag fliers in your demand data to ensure they do not accidentally influence your forecasts and alter your forecasting accuracy.
If you have an Enterprise Resource Planning (ERP) system or a Warehouse Management System (WMS) investigate whether this functionality is available to you.
With a proper system in place to track and flag demand fluctuations, you can remove the risk of negatively impacting your demand forecasting accuracy.
If you find it a challenge to accurately forecast demand it may be time to consider demand forecasting software, such as EazyStock. EazyStock will simply plug-into your ERP or business system and provide an added level of inventory management functionality.
Contact EazyStock to schedule a demo and discover how easy it is to use dynamic demand forecasting methods to increase inventory performance and profitability.
First published on 08 January 2018, updated June 2021.