# Category Archives: Forecasting

## Sources of Error

Sources of Error Errors can come from a variety of sources. One common source of which many forecasters' are unaware is caused by the projection of pa t trends into the future. For example, when we talk about statistical errors in regression analysis. we are referring to the deviations of observation from our regression line. It is common to attach a confidence band to the regression line to reduce the unexplain

## FORECASTING MODEL SAVES L.L. BEAN \$300,000 IN LABOR ANNUALLY

FORECASTING MODEL SAVES L.L. BEAN \$300,000 IN LABOR ANNUALLY L.l. Bean, the outdoor mail-order company located in Free port, Maine, depends on customer telephone orders for 72 percent of its business. Scheduling telephone operators at its call centers is therefore a critical element in its success. Having too few operators results in long customer waiting times and the real possibility of losing ·customers

## Forecasting Errors in Time-Series Analysis

Forecasting Errors in Time-Series Analysis When we use the word error we are referring to the difference between the forecast value and what actually occurred. So long as the forecast value is within the confidence limits as we discuss below in Measurement of Error this is not really an error. However common u age refers to the difference as an error. Demand generated through the interaction of a number of fact

## Determining Alpha (a) with Adaptive Forecasting

Determining Alpha (a) with Adaptive Forecasting A key factor to accurate forecasting with exponential smoothing is the selection of the proper value of alpha (a). As stated previously, the value of alpha can vary between 0 and 1. If the actual demand appears to be relatively stable over time, then we would select a relatively small value for alpha, that is, a value closer to zero. On the other hand, if the actu

## Trend Effects in Exponential Smoothing

Trend Effects in Exponential Smoothing As . tared earlier an upward or downward trend in data collected over a eloquence of time periods causes the exponential forecast to always lag behind (that is to be above or below) the actual amount. Exponentially smoothed forecasts can be corrected some hat b including a trend adjustment. To correct for the trend we now need two smoothing constants. In addition to the sm

## Exponential Smoothing

Exponential Smoothing in the tow previous forecasting methods that have just been presented, a major issue h the need to continually carry a large amount of historical data. Never the less in man apparitions (perhaps even in most) the most recent data points tend to be more indicative of the future in comparison to those in the distant past. If this premise is valid-that the importance of data diminishes as the

## Weighted Moving Average

Weighted Moving Average Whereas the simple moving average gives equal weight to each component of the moving average database a weighted moving average allows each element to be weighted by a factor where the sum of all the weighting factors equals one. The formula for a weighted moving average forecast is An additional constraint when using this equation for the weighted moving average forecast is

## FORECASTING SALES AT TACO BELL REDUCES LABOR COSTS

FORECASTING SALES AT TACO BELL REDUCES LABOR COSTS Labor is  major cost component at Taco Bell, a large fast food chain specializing in Mexican food averaging 30 percent of sales. Scheduling the proper number of workers for a given time period is therefore critical in the highly competitive fast-food industry. Too many workers results in excessive costs and reduced profits. Too few workers on the other han

## Simple Moving Average

Simple Moving Average any seasonal characteristics, a simple moving average can be very useful in identifying a trend within the data fluctuation. For example, if we want to forecast sales in June with a five-month moving average, we can take the average of the sales in January, February, March. April, and May. When June passe. the forecast for July would be the average of February, March, April, May, and June.

## Time-Series Analysis

Time Series Analysis Time-series forecasting models attempt to predict the future based on past data. Fer example sales figures collected for each of the past six week can he used to forecast vale for the seventh week. Quarterly sales figures collected for the past several year can he u-ed to forecast the sales in future quarters. We present here three types of time-series forecasting models: (a) simple moving