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Table of Contents
Here's a quick outline of this guide for those only interested in certain portions—just in case you'd like to skip ahead to those areas.
(1.2) Weighted Moving Average Sales Forecast - (Technical)
Exploring the Three Forecasting Methods & The "Best" Sales Forecasting Method
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Sales Forecast Example Media File
This article will be using the Sales Forecast Examples media file (click to download).
The media file contains an example of a sales forecast for each of the three short-term sales forecasting methods. It is recommended to download these examples before continuing to read this article in order to follow along with this guide on how to forecast sales.
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Introduction to Sales Forecasting
A sales forecast can either be a calculated or estimated value of units or dollars which represents the predicted future sales of a business for the next period. A period represents the length of time a business operates and is being used to calculate a sales forecast. The length of a period is determined by the company and the forecast will be a prediction of either the number units that will be sold or the amount of dollars which will be made, based on how you set up your forecast, for the next period of the same length used in the forecast.
There are three types of short-term sales forecasting techniques, including:
- Simple Moving Average Forecasting
- Weighted Moving Average Forecasting
- Exponential Smoothing Forecasting
Each of the three forecasting techniques in this guide will be explained using the media file mentioned in the previous section, which features an example of a sales forecast with complete calculations.
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(1.1) Simple Moving Average Sales Forecast
On the first page of the media file, labeled "1.1 - Moving Average," there is a complete example of a sales forecast using the simple moving average forecasting method. This method is the easiest forecasting method, since it only entails calculating an average of either the number of units sold or the amount of money made, based on the actual sales data. The number of periods is represented by the letter "n," which is decided by the company. For this example, n = 4 periods.
Table 1 visually explains how the calculated forecast for a future period is found by simply averaging the previous "n" periods, or the previous 4 periods in this example. The first four periods' actual sales are averaged in order to calculate the forecast for period five, indicated with red. For period 6, periods two through five are averaged, as indicated in blue. For period 7, periods three through six are averaged, indicated with green. Finally, for period 8, periods four through seven are averaged, as indicated in yellow.
As one may easily see, the future sales forecast is calculated by averaging the number of "n" periods, moving one period at a time; which is why it's called the moving average sales forecast. Table 2 shows the exact calculations required to find the sales forecasts in this example.
Continue onto Page 2 to read how to calculate a sales forecast using the Weighted Moving Average and Exponential Smoothing sales forecasting techniques.
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This is a complete guide to forecasting with the weighted average forecast method and the exponential forecast method, which are both explained in this article and sales forecast example download media file for Excel, so you can see a working forecast example and work with numbers, in order to create your own forecast. This is page 2 of this 3 page article, which also explains simple moving average and tips to finding the best forecasting method for your company.
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(1.2) Weighted Moving Average Sales Forecast
The second page of the media file, labeled "1.2 - Weighted Moving Average," features a complete example of a sales forecast using the weighted moving average sales forecasting method. This forecasting method is similar to the simple moving average method, since it moves one period at a time once another period has completed; however, the new forecast is not calculated the same way.
In order to calculate a weighted moving average, weights are assigned to the each of the previous number of periods, represented as "n." In this example, n = 4 periods. The weights for this example are given in Table 3.
Note: The sum of the weights of all the periods must equal to "1," or else you are not calculating a correct weighted moving average sales forecast.
The weights are multiplied against the actual sales data of the previous "n" periods. In this example, these weights are applied as shown in Table 4. Finally, the complete calculations for each forecast is displayed in Table 5.
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(1.3) Exponential Smoothing Sales Forecast
The final page of the media file, labeled "1.3 - Exponential Smoothing," features a complete example of a sales forecast using the exponential smoothing sales forecasting method. This method is completely different from the previous two sales forecasting methods.
First, the required equation to calculate a forecast with the exponential smoothing method is listed, followed by an explanation of the variables. To begin calculating the sales forecast with the exponential smoothing method, the first period's forecast must be known. This forecast should be already established or estimated, if it is a new company. Then, a company must figure out a good value for Alpha, α, which is the smoothing constant.
Alpha is multiplied to the difference between the previous forecast and previous actual sales. It works to allocate a portion of the gain or loss in sales to the previously forecasted value, representing the actual percentage of increase or decrease of sales due to natural market fluctuations. Alpha usually ranges between .3 - .4 for many businesses; however the value must remain between zero and one.
Table 6 shows the actual sales, the calculated forecast, and the difference between those two values; which is referred to as the "error" in the forecast. Table 7 explains the calculations for each period to calculate the individual exponential smoothing forecasts, in this example.
For more information on the three forecasting methods and the "best" forecasting method, please continue reading on Page 3.
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Learn which sales forecast is the best forecasting method for your business! This article introducing enreprenuers to the concept of sales forecasting explains the simple moving average, weighted moving average, and exponential smoothing forecast methods. With a file to download and follow along with as you read this guide on how to forecast sales, you can be forecasting sales for a business in only a few minutes!
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Exploring the Three Sales Forecasting Methods
The simple moving average forecasting technique is a good technique for stable market business, such as bread or milk; since many consumers have patterns to go every so many days to the store and pick them up, almost like a routine every few days. For other markets, such as selling t-shirts, pet supplies, and other goods with fluctuating markets, it's best to use a more advanced sales forecasting method.
The weighted average forecasting technique is much better than the simple moving average forecasting technique, since every period does not have an equal weight in the total calculation of the forecast. By having descending weights for the previous number of periods being used in the forecast, the forecast is able to fluctuate with the market much better.
Finally, the exponential smoothing forecasting technique is an advanced technique; since the calculations are much more in-depth and require deep knowledge of both the company, the current market, and the overall industry to decide on an efficient value for Alpha to come up with accurate forecasts.
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The "Best" Sales Forecasting Method
The "best" sales forecasting method is totally unique for every company, since every company grows and operates differently. As mentioned above, a general rule to follow is using simpler averaging techniques to forecast a more stable market; while using more advanced forecasting techniques to forecast markets more vulnerable to fluctuations.
Now that you have knowledge of the three different types of sales forecasting methods and a media file with a working example sales forecast for each of the three methods, you should be well on your way to creating a forecast for your business.
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Information: Author, Christopher Kochan, obtained his Bachelor's Degree in Operations and Supply Chain Management, taking courses specifically related to demand forecasting. The information provided in this article is based on the knowledge and experience gained through his studies.
All images courtesy of author.
Complete Working Example of a Sales Forecast for 3 Forecasting Methods
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