Chapter 7

Problem 7-1:

We utilize a static model with level, trend, and seasonality components to evaluate the forecasts for year 6. Initially, we deseasonalize the demand and utilize regression in estimating the trend and level components. We then estimate the seasonal factors for each period and evaluate forecasts. EXCEL Worksheet 7-1 provides the solution to this problem.

The model utilized for forecasting is:

The deseasonalized regression model is:

= 5997.261 + 70.245 t

The seasonal indices for each of the twelve months are:

Month S.I

JAN 0.427

FEB 0.475

MAR 0.463

APR 0.398

MAY 0.621

JUN 0.834

JUL 0.853

AUG 1.151

SEP 1.733

OCT 1.778

NOV 2.124

DEC 1.095

For example, the forecast for January of Year 6 is obtained by the following calculation:

F61 = [5997.261 + (61) * 70.245] * 0.4266 = 4386

The quality of the forecasting method is quite good given that the forecast errors are not too high.

Problem 7-2:

Worksheet 7-2 compares the four-week moving average approach with the exponential smoothing model (alpha = 0.1). In a four-week moving average model the weight assigned to the most recent data is 0.25 whereas in the case of the exponential smoothing model the weight assigned is 0.1. The following graphs depict the results from the two models.

For this specific problem, it is evident that the moving average model is more responsive than the exponential smoothing approach due the difference in weights allocation (0.25 and 0.1). Using MAD as a measure for forecast accuracy it can be concluded that the moving average model (MAD = 9) is slightly more accurate than the exponential smoothing model (MAD = 10) in evaluating forecasts