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Moving Averages
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Calculates The Trend In a nutshell, the moving averages technique allows a business to mathematically calculate (then graph) sales trends. Not just use a “line of best fit”.
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How does knowing sales trends help business?
The business can use the trend to help make the following business decisions: How much of a product to stock. Seasonal changes Product life cycle changes Staffing numbers. Roster creation Future training requirements Hiring and redundancy The amount of capital to invest in the business How quickly new products / extension strategies should be launched
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What factors influence the trend?
Seasonal Fluctuations – these are regular repeated variations Cyclicial Fluctuations – Caused by booms and recessions Random Fluctuations – Can occur at any time and cause unusual or un-predicted sales. E.g. San Lu sales in China
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How is the trend calculated?
Week Days Sales Revenue 1 Tue 6000 Wed 5200 Thu 7150 Fri 7990 Sat 12650 2 6400 5150 7280 8015 12890 3 6490 5300 7200 8100 12910 4 6600 5480 7290 8340 13400 First of all the business will group sales data based on an obvious “seasonal” change. E.g Trading days in a week (5 period data) Quarters in a year (4 period data) Hours in a trading day (8 period data. Etc.
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Calculating the Moving Total
Week Days Sales Revenue 5 period moving total 1 Tue 6000 Wed 5200 Thu 7150 Fri 7990 Sat 12650 38990 2 6400 39390 5150 39340 7280 39470 8015 39495 12890 39735 3 6490 39825 5300 39975 7200 39895 8100 39980 12910 40000 4 6600 40110 5480 40290 7290 40380 8340 40620 13400 41110 Week Days Sales Revenue 5 period moving total 1 Tue 6000 Wed 5200 Thu 7150 Fri 7990 Sat 12650 38990 2 6400 39390 5150 39340 7280 39470 8015 39495 12890 39735 3 6490 39825 5300 39975 7200 39895 8100 39980 12910 40000 4 6600 40110 5480 40290 7290 40380 8340 40620 13400 41110 The first step is to calculate the moving total. In this case it is a 5 period moving total. What are the missing values?
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Calculating the Trend (Moving Average)
Week Days Sales Revenue 5 period moving total Trend 1 Tue 6000 Wed 5200 Thu 7150 7798 Fri 7990 7878 Sat 12650 38990 7868 2 6400 39390 7894 5150 39340 7899 7280 39470 7947 8015 39495 7965 12890 39735 7995 3 6490 39825 7979 5300 39975 7996 7200 39895 8000 8100 39980 8022 12910 40000 8058 4 6600 40110 8076 5480 40290 8124 7290 40380 8222 8340 40620 13400 41110 Week Days Sales Revenue 5 period moving total Trend 1 Tue 6000 Wed 5200 Thu 7150 7798 Fri 7990 7878 Sat 12650 38990 7868 2 6400 39390 7894 5150 39340 7899 7280 39470 7947 8015 39495 7965 12890 39735 7995 3 6490 39825 7979 5300 39975 7996 7200 39895 8000 8100 39980 8022 12910 40000 8058 4 6600 40110 8076 5480 40290 8124 7290 40380 8222 8340 40620 13400 41110 The next step is to calculate the trend. This involves centering the data against the middle of that time period. What are the missing values?
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Graphing the Trend The trend can now be graphed.
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Seasonal Variation Week Days Sales Revenue 5 period moving total Trend Seasonal Variation 1 Tue 6000 Wed 5200 Thu 7150 7798 -648 Fri 7990 7878 112 Sat 12650 38990 7868 4782 2 6400 39390 7894 -1494 5150 39340 7899 -2749 7280 39470 7947 -667 8015 39495 7965 50 12890 39735 7995 4895 3 6490 39825 7979 -1489 5300 39975 7996 -2696 7200 39895 8000 -800 8100 39980 8022 78 12910 40000 8058 4852 4 6600 40110 8076 -1476 5480 40290 8124 -2644 7290 40380 8222 -932 8340 40620 13400 41110 Week Days Sales Revenue 5 period moving total Trend Seasonal Variation 1 Tue 6000 Wed 5200 Thu 7150 7798 -648 Fri 7990 7878 112 Sat 12650 38990 7868 4782 2 6400 39390 7894 -1494 5150 39340 7899 -2749 7280 39470 7947 -667 8015 39495 7965 50 12890 39735 7995 4895 3 6490 39825 7979 -1489 5300 39975 7996 -2696 7200 39895 8000 -800 8100 39980 8022 78 12910 40000 8058 4852 4 6600 40110 8076 -1476 5480 40290 8124 -2644 7290 40380 8222 -932 8340 40620 13400 41110 The moving average data can be compared to the actual sales data for that time period to work out the seasonal variation. Actual sales – trend sales Calculate the missing values
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Average Seasonal Variation
Week Days Sales Revenue 5 period moving total Trend Seasonal Variation Average Seasonal Variation 1 Tue 6000 Wed 5200 Thu 7150 7798 -648 Fri 7990 7878 112 80.00 Sat 12650 38990 7868 4782 2 6400 39390 7894 -1494 5150 39340 7899 -2749 7280 39470 7947 -667 8015 39495 7965 50 12890 39735 7995 4895 3 6490 39825 7979 -1489 5300 39975 7996 -2696 7200 39895 8000 -800 8100 39980 8022 78 12910 40000 8058 4852 4 6600 40110 8076 -1476 5480 40290 8124 -2644 7290 40380 8222 -932 8340 40620 13400 41110 Week Days Sales Revenue 5 period moving total Trend Seasonal Variation Average Seasonal Variation 1 Tue 6000 Wed 5200 Thu 7150 7798 -648 Fri 7990 7878 112 80.00 Sat 12650 38990 7868 4782 2 6400 39390 7894 -1494 5150 39340 7899 -2749 7280 39470 7947 -667 8015 39495 7965 50 12890 39735 7995 4895 3 6490 39825 7979 -1489 5300 39975 7996 -2696 7200 39895 8000 -800 8100 39980 8022 78 12910 40000 8058 4852 4 6600 40110 8076 -1476 5480 40290 8124 -2644 7290 40380 8222 -932 8340 40620 13400 41110 Once the seasonal variation has been calculated, a business can work out on average how much above or below the trend that time period usually is. Once again, calculate the missing values.
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How is all this useful? For example a Dairy might find out from calculating the trend that milk sales are increasing by 15 bottles a week. This would need to be reflected in their stock rates. However, they might also be able to predict by the seasonal variation that Monday’s (on average) sells 8 bottles less than the trend. This would also need to be reflected in their stocking rates. A business could also draw up their staff roster based on the average seasonal variation information.
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More Complex Moving Average Calculations
When data is collected over an even period (e.g. 4 quarters in the year) it is not possible to centre the data. To overcome this problem add two of the 4 period moving totals together to make an 8 period moving total (that will have gained data from 5 different time periods. As this is now an odd number the data can now be centered. Year Quarter Sales Revenue ($000) Four-quarter moving total Eight-quarter moving total Trend 2001 1 200 2 190 3 175 209.4 4 280 845 206.1 2002 185 830 1675 203.9 179 819 1649 199.0 168 812 1631 194.4 248 780 1592 191.9 2003 180 775 1555 188.8 164 760 1535 183.4 158 750 1510 215 717 1467 Year Quarter Sales Revenue ($000) Four-quarter moving total Eight-quarter moving total Trend 2001 1 200 2 190 3 175 209.4 4 280 845 206.1 2002 185 830 1675 203.9 179 819 1649 199.0 168 812 1631 194.4 248 780 1592 191.9 2003 180 775 1555 188.8 164 760 1535 183.4 158 750 1510 215 717 1467
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