Essay on Forecasting: Exponential Smoothing and Fast Food

610 Words Feb 28th, 2012 3 Pages
Choose one of the forecasting methods and explain the rationale behind using it in real life.

I would choose to use the exponential smoothing forecast method. Exponential smoothing method is an average method that reacts more strongly to recent changes in demand than to more distant past data. Using this data will show how the forecast will react more strongly to immediate changes in the data. This is good to examine when dealing with seasonal patterns and trends that may be taking place. I would find this information very useful when examining the increased production of a product that appears to be higher in demand in the present than in the past Taylor (2011). For example, annual sales of toys will probably peak in the months of
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Give an example of when each would be used.

The time series model is based on using historical data to predict future behavior Taylor (2011). This method could be used by a construction work, retail store, fast food restaurant or clothing manufacturer to predict sales for an upcoming season change. For example, new homebuilders in US may see variation in sales from month to month. But analysis of past years of data may reveal that sales of new homes are increased gradually over period of time. In this case trend is increase in new home sales.

The causal model uses a mathematical correlation between the forecasted items and factors affecting how the forecasted item behaves. This would be used by companies who do not have access to historical data therefore they would use a competitors available data. For example, the sales of ice cream will increase when the temperature outside is high. You will see more and more people going to the stores buying ice cream, freeze pops and other cold items when it is hot. When it is cold you will see more people buying coffee, hot chocolate, and cappuccino.

What are some of the problems and drawbacks of the moving average forecasting model?
One problem with the moving average method is that it does not take into account data that change due to seasonal variations and trends. This method works better in short run

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