Forecasting: Moving Average and Exponential Smoothing Tool

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  • Topic: Moving average, Time series analysis, Exponential smoothing
  • Pages : 2 (421 words )
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  • Published : April 7, 2013
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Forecasting
In order for a business to be successful it must come up with the most accurate forecast possible so they can plan for the demands. There are forecasting tools that assist with making calculations to receive the best outcome by your company’s needs. The tools are moving average, weighted moving average and exponential smoothing.

The moving average takes the total of actual demand for previous months then divides by the number of months added. The number of months that is used can be predefined such as using the previous three months. This is the simplest and easiest calculation but often is not accurate since it can have a lag in spotting trends (Murphy).

The weighted moving average is similar to the moving average but it places weights on each period usually with more recent periods weighing more. For example if you are averaging the past three months with the most recent month being the most valuable you would multiply the last month by .3 and then month before that at .2 and then the first month by .1 then by adding them together you would get the average with more of an emphasis on the month with the most weight (Career Education Corporation, 2010).

Lastly there is the exponential smoothing tool. The exponential smoothing tool is a moving average which is ideal for forecasting smaller items. It uses the most recent demand along with the most recent forecast using a weight between 0 and 1 (Bozarth, 2011). In other words the forecast is the current forecast plus an adjustment for the error rate. This tool is very useful to take in considerations trends occurring.

Before a company selects a forecasting technique it should consider that what every technique used can be implemented correctly and maintained. Then the company should adjust parameters so that they match the company’s situations. As well as continually monitoring the forecast for accuracy, if the forecast being calculated is way off it may be best to implement a...
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