Accurate inventory forecasting and planning requires using different methods for different scenarios and sets of conditions. One method that’s useful during periods of change is the moving average.
What is a moving average?
In the simplest terms, a moving average is what it sounds like. Averages are “moved” to manipulate the data and produce a reliable forecast.
What does this mean in practice? Rather than considering every single data point in a set (e.g., 3 months of sales data), the data is divided into subsets (e.g., 1 month) and the average of each subset is taken. The point of doing this is to smooth out short-term fluctuations in the data.
Moving averages are usually calculated to identify the direction of a trend.
This can be done in a variety of ways, with the most common being simple and weighted moving averages.
Simple moving average forecasting is what we commonly think of by averaging. It can be used for a single period or multiple periods.
For example, let’s say it’s the end of March, so your first sales quarter is almost over. To get the simple moving average (SMA) you would divide the total sales from January – March by the number of periods, which in this case would be 3 (3 months), giving you a simple average number of sales per month. This number can be used to forecast the sales of the upcoming months or period.
The disadvantage of this method is that it treats all periods as being equal, which makes it not very realistic for real-world application.
The weighted moving average (WMA) method involves assigning a range of percentages to different periods. These percentages are determined based on the relation of previous data to current market trends.
For example, let’s say due to economic changes, in the past 3 months the demand for your products has spiked. From years of experience, you know demand will most likely continue to increase over the next few months. Using the WMA method you would assign the last 3 months a higher percentage — i.e., a greater weight — than the rest of the past year.
The type of forecast enables you to use the most recent and relatable data to produce an accurate prediction of what the next period of demand will look like.
In a changing market, companies use demand and inventory planning software to calculate weighted moving averages that enable them to forecast accurately and keep up with fluctuating demand and avoid overstock and stockouts.