Forcasting
1. Forecasting techniques generally assume an existing causal system that will continue to exist
in the future.
TRUE
Forecasts depend on the rules of the game remaining reasonably constant.
2. For new products in a strong growth mode, a low alpha will minimize forecast errors when
using exponential smoothing techniques.
FALSE
If growth is strong, alpha should be large so that the model will catch up more quickly.
3. Once accepted by managers, forecasts should be held firm regardless of new input since many
plans have been made using the original forecast.
FALSE
Flexibility to accommodate major changes is important to good forecasting.
4. Forecasts for groups of items tend to be less accurate than forecasts for individual items
because forecasts for individual items don’t include as many influencing factors.
FALSE
Forecasting for an individual item is more difficult than forecasting for a number of items.
5. Forecasts help managers plan both the system itself and provide valuable information for using
the system.
TRUE
Both planning and use are shaped by forecasts.
6. Organizations that are capable of responding quickly to changing requirements can use a
shorter forecast horizon and therefore benefit from more accurate forecasts.
TRUE
If an organization can react quicker, its forecasts need not be so long term.
7. When new products or services are introduced, focus forecasting models are an attractive
option.
FALSE
Because focus forecasting models depend on historical data, they’re not so attractive for newly introduced
products or services.
8. The purpose of the forecast should be established first so that the level of detail, amount of
resources, and accuracy level can be understood.
TRUE
All of these considerations are shaped by what the forecast will be used for.
9. Forecasts based on time series (historical) data are referred to as associative forecasts.
FALSE
Forecasts based on time series data are referred to as time-series forecasts.
10. Time series techniques involve identification of explanatory variables that can be used to
predict future demand.
FALSE
Associate forecasts involve identifying explanatory variables.
11. A consumer survey is an easy and sure way to obtain accurate input from future customers
since most people enjoy participating in surveys.
FALSE
Most people do not enjoy participating in surveys.
12. The Delphi approach involves the use of a series of questionnaires to achieve a consensus
forecast.
TRUE
A consensus among divergent perspectives is developed using questionnaires.
13. Exponential smoothing adds a percentage (called alpha) of last period’s forecast to estimate
next period’s demand.
FALSE
Exponential smoothing adds a percentage to the last period’s forecast error.
14. The shorter the forecast period, the more accurately the forecasts tend to track what actually
happens.
TRUE
Long-term forecasting is much more difficult to do accurately.
15. Forecasting techniques that are based on time series data assume that future values of the
series will duplicate past values.
FALSE
Time-series forecast assume that future patterns in the series will mimic past patterns in the series.
16. Trend adjusted exponential smoothing uses double smoothing to add twice the forecast error
to last period’s actual demand.
FALSE
Trend adjusted smoothing smoothes both random and trend-related variation.
17. Forecasts based on an average tend to exhibit less variability than the original data.
TRUE
Averaging is a way of smoothing out random variability.
18. The naive approach to forecasting requires a linear trend line.
FALSE
The naïve approach is useful in a wider variety of settings.
19. The naive forecast is limited in its application to series that reflect no trend or seasonality.
FALSE
When a trend or seasonality is present, the naïve forecast is more limited in its application.
20. The naive forecast can serve as a quick and easy standard of comparison against which to
judge the cost and accuracy of other techniques.
TRUE
Often the naïve forecast performs reasonably well when compared to more complex techniques.
21. A moving average forecast tends to be more responsive to changes in the data series when
more data points are included in the average.
FALSE
More data points reduce a moving average forecast’s responsiveness.
22. In order to update a moving average forecast, the values of each data point in the average
must be known.
TRUE
The moving average cannot be updated until the most recent value is known.
23. Forecasts of future demand are used by operations people to plan capacity.
TRUE
Capacity decisions are made for the future and therefore depend on forecasts.
24. An advantage of a weighted moving average is that recent actual results can be given more
importance than what occurred a while ago.
TRUE
Weighted moving averages can be adjusted to make more recent data more important in setting the
forecast.
25. Exponential smoothing is a form of weighted averaging.
TRUE
The most recent period is given the most weight, but prior periods also factor in.
26. A smoothing constant of .1 will cause an exponential smoothing forecast to react more quickly
to a sudden change than a smoothing constant value of .3.
FALSE
Smaller smoothing constants result in less reactive forecast models.
27. The T in the model TAF = S+T represents the time dimension (which is usually expressed in
weeks or months).
FALSE
The T represents the trend dimension.
28. Trend adjusted exponential smoothing requires selection of two smoothing constants.
TRUE
One is for the trend and one is for the random error.
29. An advantage of “trend adjusted exponential smoothing” over the “linear trend equation” is its
ability to adjust over time to changes in the trend.
TRUE
A linear trend equation assumes a constant trend; trend adjusted smoothing allows for changes in the
underlying trend.
30. A seasonal relative (or seasonal indexes) is expressed as a percentage of average or trend.
TRUE
Seasonal relatives are used when the seasonal effect is multiplicative rather than additive.
31. In order to compute seasonal relatives, the trend of past data must be computed or known
which means that for brand new products this approach can’t be used.
TRUE
Computing seasonal relatives depends on past data being available.
32. Removing the seasonal component from a data series (de-seasonalizing) can be accomplished
by dividing each data point by its appropriate seasonal relative.
TRUE
Deseasonalized data points have been adjusted for seasonal influences.
33. If a pattern appears when a dependent variable is plotted against time, one should use time
series analysis instead of regression analysis.
TRUE
Patterns reflect influences such as trends or seasonality that go against regression analysis assumptions.
34. Curvilinear and multiple regression procedures permit us to extend associative models to
relationships that are non-linear or involve more than one predictor variable.
TRUE
Regression analysis can be used in a variety of settings.
35. The sample standard deviation of forecast error is equal to the square root of MSE.
TRUE
The MSE is equal to the sample variance of the forecast error.
36. Correlation measures the strength and direction of a relationship between variables.
TRUE
The association between two variations is summarized in the correlation coefficient.
37. MAD is equal to the square root of MSE which is why we calculate the easier MSE and then
calculate the more difficult MAD.
FALSE
MAD is the mean absolute deviation.
38. In exponential smoothing, an alpha of 1.0 will generate the same forecast that a naïve forecast
would yield.
TRUE
With alpha equal to 1 we are using a naïve forecasting method.
39. A forecast method is generally deemed to perform adequately when the errors exhibit an
identifiable pattern.
FALSE
Forecast methods are generally considered to be performing adequately when the errors appear to be
randomly distributed.
40. A control chart involves setting action limits for cumulative forecast error.
FALSE
Control charts set action limits for the tracking signal.
41. A tracking signal focuses on the ratio of cumulative forecast error to the corresponding value
