Manufacturer : User Solutions, Inc
OM-ForecastingMore than 25% of U.S.corporations use some form of exponential smoothing as a forecasting model. Smoothing models are relatively simple, easy to understand, and easy to implement, especially in spreadsheet form. Smoothing models also compare quite favorably in accuracy to complex forecasting models. One of the surprising things scientists have learned about forecasting in recent years is that complex models are not necessarily more accurate than simple models.
The simplest form of exponential smoothing is called, appropriately enough, simple smoothing. Simple smoothing is used for short-range forecasting, usually just one month into the future. The model assumes that the data fluctuate around a reasonably stable mean (no trend or consistent pattern of growth). If the data contain a trend, use the trend-adjusted smoothing model (TREND SMOOTH).
Exponential smoothing with a trend works much like simple smoothing except that two components must be updated each period: level and trend. The level is a smoothed estimate of the value of the data at the end of each period. The trend is a smoothed estimate of average growth at the end of each period.
To explain this type of forecasting, let's review an application at Alief Precision Arms, a company that manufactures high-quality replicas of the Colt Single-Action Army revolver and other revolvers from the nineteenth century. Alief was founded in 1987 and experienced rapid growth through about 1994. Since 1994, growth has slowed and Alief is uncertain about the growth that should be projected in the future.
The worksheet was developed to help Alief compare several different types of trend forecasts. This worksheet can produce a linear or straight-line trend, a damped trend in which the amount of growth declines each period in the future, or an exponential trend in which the amount of growth increases each period in the future.
TRENDSMOOTH is a robust model, relatively insensitive to smoothing parameters provided that they are approximately correct.
Regular seasonal patterns appear in most business data. The weather affects the sales of everything from bikinis to snowmobiles. Around holiday periods, we see increases in the number of retail sales, long distance telephone calls, and gasoline consumption. Business policy can cause seasonal patterns in sales. Many companies run annual dealer promotions which cause peaks in sales. Other companies depress sales temporarily by shutting down plants for annual vacation periods.
Usually seasonality is obvious but there are times when it is not. Two questions should be asked when there is doubt about seasonality. First, are the peaks and troughs consistent? That is, do the high and low points of the pattern occur in about the same periods (week, month, or quarter) each year? Second, is there an explanation for the seasonal pattern? The most common reasons for seasonality are weather and holidays, although company policy such as annual sales promotions may be a factor. If the answer to either of these questions is no, seasonality should not be used in the forecasts.
Our approach to forecasting seasonal data is based on the classical decomposition method developed by economists in the nineteenth century. Decomposition means separation of the time series into its component parts. A complete decomposition separates the time series into four components: seasonality, trend, cycle, and randomness. The cycle is a long range pattern related to the growth and decline of industries or the economy as a whole.
Two worksheets are available for seasonal adjustment. MULTIMON uses the ratio-to-moving average method to adjust monthly data. ADDITMON uses a similar method called the difference-to-moving average method to adjust monthly data. It may be necessary to test both of these worksheets before choosing a seasonal pattern.
Regular seasonal patterns appear in most business data. The weather affects the sales of everything from bikinis to snowmobiles. Around holiday periods, we see increases in the number of retail sales, long distance telephone calls, and gasoline consumption. Business policy can cause seasonal patterns in sales. Many companies run annual dealer promotions which cause peaks in sales. Other companies depress sales temporarily by shutting down plants for annual vacation periods.
Usually seasonality is obvious but there are times when it is not. Two questions should be asked when there is doubt about seasonality. First, are the peaks and troughs consistent? That is, do the high and low points of the pattern occur in about the same periods (week, month, or quarter) each year? Second, is there an explanation for the seasonal pattern? The most common reasons for seasonality are weather and holidays, although company policy such as annual sales promotions may be a factor. If the answer to either of these questions is no, seasonality should not be used in the forecasts.
Our approach to forecasting seasonal data is based on the classical decomposition method developed by economists in the nineteenth century. Decomposition means separation of the time series into its component parts. A complete decomposition separates the time series into four components: seasonality, trend, cycle, and randomness. The cycle is a long range pattern related to the growth and decline of industries or the economy as a whole.
Two worksheets are available for seasonal adjustment. MULTIMON uses the ratio-to-moving average method to adjust monthly data. ADDITMON uses a similar method called the difference-to-moving average method to adjust monthly data. It may be necessary to test both of these worksheets before choosing a seasonal pattern.
For over 12 years User Solutions, Inc. has been the easy choice for thousands of companies worldwide.
Operations Manager - Saver Package contains many business Excel spreadsheets: forecasting, production planning, inventory management, job scheduling, quality control, analysis of waiting lines, and more.
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