Exponential smoothing is a sophisticated weighted moving average forecasting method that is still fairly easy to use.
It involves very little record keeping of past data. The basic exponential smoothing formula can be shown as follows:
New forecast = last period's forecastWhere α is a weight, or smoothing constant, chosen by the forecaster, that has a value between 0 and 1. Equation (4-3) can also be written mathematically as…
The concept is not complex. The latest estimate of demand is equal to our old estimate adjusted by a fraction of the difference between the last period’s actual demand and the old estimate. Let’s try a simple example.
In January, a car dealer predicted February demand for 142 Ford Mustangs. Actual February demand was 153 autos. Using a smoothing constant chosen by management of α = .20, we can forecast March demand using the exponential smoothing model.
Substituting our sample data into the formula, we obtain – the New forecast (for March Demand), which is equal to 142, plus .2 times 153 minus 142, which is equal to 142 plus 2.2, in the end equals to 144.2.
Thus, the March demand forecast for Ford Mustangs is rounded to 144.
Now, let’s try another exponential smoothing problem. We’ll do this one by by building a chart and calculating the values one at a time. Then, we’ll work the same problem using your Excel Worksheet Model.
During the past 8 quarters, the Port of Baltimore has unloaded large quantities of grain. The first quarter forecast was 175. Using a smoothing constant of .10 ( = .10), what is the forecast for quarter 9?
Note that our Forecast Value was given as 175. Let’s go ahead and put that value in our chart.
Now, let’s put our .10 smoothing constant that was given in the problem into place in our formula.
Now we’re ready to place our actual minors 1 period.
We’re almost done with our forecast for quarter 2! Now, let’s complete the formula by placing our Forecast Value of 175.
Now, let’s apply our formula to our remaining quarters!
Thus, as you can see, Using a smoothing constant of .10 ( = .10), the forecast for quarter 9 is 178.58.
As you can see, the exponential smoothing approach is easy to use, and it has been successfully applied in virtually every type of business. However, the appropriate value of the smoothing constant, α, can make the difference between an accurate forecast and an inaccurate forecast. You’ll want to keep in mind that the smoothing constant,, is generally in the range from .05 to .50 for business applications. It can be changed to give more weight to recent data (when is high) or more weight to past data (when is low).
Now that we have calculated a problem manually, you’ll be happy to know that our publisher has provided an Excel problem-solving worksheet for this problem, too. I encourage you to play with the numbers in the “actual” and watch how the forecast changes!
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