# Moving Average Sales Forecast Template

In our daily life, we come across a lot of mathematical or analytically problems revolving around average. For example, we calculate our average marks such as 67.8% or we may calculate average monthly income of a business. Therefore, we may interpret that simple average gives us a picture of our past activities in a single numeric form, such as our average marks or average monthly income of business. Unlike Simple average, Moving average gives us an opportunity to forecast the future results on basis of previous results.

Moving average has two types, i.e. Simple Moving Average and Weighted Moving Average. In the following paragraphs, both types of moving averages and forecasting mechanism will be dealt with in detail.

Here is preview of a good quality Moving Average Sales Forecast Template,

At the outset, it would be wiser to plan summary of a daily life problem and solve it through forecasting with simple moving average and weighted moving average, turn by turn. Consider following table and suppose that we have actual demand data of Leather Jackets for last five (05) years and we wish to forecast demand for next five (05) years. It would be assumed that in the current problem all remaining factors, such as weather, public choice, etc. remain unchanged.

 Year Actual Demand of Leather Jackets Demand Forecast 2008 120 2009 103 2010 105 2011 84 2012 114 2013 105 2014 102 2015 102 2016 102 2017 105

Fè Forecast for period

N è Number of periods used for forecast (05 in our example)

S è Actual Values

Forecast demand for 2013 has been calculated as: (120 + 103 + 105 + 84 + 114) / 5 = 105.20

Forecast demand for 2014 has been calculated as: (103 + 105 + 84 + 114 + 105.20) / 5 = 102.24

If we assign weight to each value in calculating moving average, it is called weighted moving average. In our aforementioned example, if we give preference to the most recent data and less preference to old data, forecasting may be obtained through weighted moving average. Consider the following slight changes in above given example:

 Year Actual Demand of Leather Jackets Weight Demand Forecast with weighted moving averagew t-i = weight given to data at period t-i 2013 2014 2015 2016 2017 2008 120 1 2009 103 2 1 2010 105 3 2 1 2011 84 4 3 2 1 2012 114 5 4 3 2 1 2013 5 4 3 2 103 2014 5 4 3 102 2015 5 4 103 2016 5 103 2017 104

Forecast for 2013 is calculated as: (120*1+103*2+105*3+84*4+114*5) / (5+4+3+2+1) = 103

Forecast for 2014 is calculated as: (103*1+105*2+84*3+114*4+103*5) / (5+4+3+2+1) = 102

While calculating weighted moving average, care must be taken that in simple moving average we divide by the number of periods used for forecast, but in weighted moving average division is made by sum of weights associated with each relevant period used for forecasting.

In the concluding arguments, it is better to explain that as simple average is a single value, moving average (whether simple or weighted) is a series of numbers which give an insight into future, i.e. forecast based upon historical values.