Chapter 03 – Forecasting
1. Forecasting techniques generally assume an existing causal system that will continue to
exist in the future.
2. For new products in a strong growth mode, a low alpha will minimize forecast errors when
using exponential smoothing techniques.
3. Once accepted by managers, forecasts should be held firm regardless of new input since
many plans have been made using the original forecast.
Chapter 03 – 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.
5. Forecasts help managers plan both the system itself and provide valuable information for
using the system.
6. Organizations that are capable of responding quickly to changing requirements can use a
shorter forecast horizon and therefore benefit from more accurate forecasts.
Chapter 03 – Forecasting
7. When new products or services are introduced, focus forecasting models are an attractive
option.
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.
9. Forecasts based on time series (historical) data are referred to as associative forecasts.
Chapter 03 – Forecasting
10. Time series techniques involve identification of explanatory variables that can be used to
predict future demand.
11. A consumer survey is an easy and sure way to obtain accurate input from future customers
since most people enjoy participating in surveys.
12. The Delphi approach involves the use of a series of questionnaires to achieve a consensus
forecast.
Chapter 03 – Forecasting
13. Exponential smoothing adds a percentage (called alpha) of last period’s forecast to
estimate next period’s demand.
14. The shorter the forecast period, the more accurately the forecasts tend to track what
actually happens.
15. Forecasting techniques that are based on time series data assume that future values of the
series will duplicate past values.
Chapter 03 – Forecasting
16. Trend adjusted exponential smoothing uses double smoothing to add twice the forecast
error to last period’s actual demand.
17. Forecasts based on an average tend to exhibit less variability than the original data.
18. The naive approach to forecasting requires a linear trend line.
Chapter 03 – Forecasting
19. The naive forecast is limited in its application to series that reflect no trend or seasonality.
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.
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.
Chapter 03 – Forecasting
22. In order to update a moving average forecast, the values of each data point in the average
must be known.
23. Forecasts of future demand are used by operations people to plan capacity.
24. An advantage of a weighted moving average is that recent actual results can be given
more importance than what occurred a while ago.
Chapter 03 – Forecasting
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25. Exponential smoothing is a form of weighted averaging.
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.
27. The T in the model TAF = S+T represents the time dimension (which is usually expressed
in weeks or months).
Chapter 03 – Forecasting
28. Trend adjusted exponential smoothing requires selection of two smoothing constants.
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.
30. A seasonal relative (or seasonal indexes) is expressed as a percentage of average or trend.
Chapter 03 – Forecasting
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.
32. Removing the seasonal component from a data series (de-seasonalizing) can be
accomplished by dividing each data point by its appropriate seasonal relative.
33. If a pattern appears when a dependent variable is plotted against time, one should use time
series analysis instead of regression analysis.
Chapter 03 – Forecasting
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.
35. The sample standard deviation of forecast error is equal to the square root of MSE.
36. Correlation measures the strength and direction of a relationship between variables.
Chapter 03 – Forecasting
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.
38. In exponential smoothing, an alpha of 1.0 will generate the same forecast that a naïve
forecast would yield.
39. A forecast method is generally deemed to perform adequately when the errors exhibit an
identifiable pattern.
Chapter 03 – Forecasting
40. A control chart involves setting action limits for cumulative forecast error.
41. A tracking signal focuses on the ratio of cumulative forecast error to the corresponding
value of MAD.
42. The use of a control chart assumes that errors are normally distributed about a mean of
zero.
Chapter 03 – Forecasting
43. Bias exists when forecasts tend to be greater or less than the actual values of time series.
44. Bias is measured by the cumulative sum of forecast errors.
45. Seasonal relatives can be used to de-seasonalize data or incorporate seasonality in a
forecast.
Chapter 03 – Forecasting
46. The best forecast is not necessarily the most accurate.
47. A proactive approach to forecasting views forecasts as probable descriptions of future
demand, and requires action to be taken to meet that demand.
48. Simple linear regression applies to linear relationships with no more than three
independent variables.
Chapter 03 – Forecasting
49. An important goal of forecasting is to minimize the average forecast error.
50. Forecasting techniques such as moving averages, exponential smoothing, and the naive
approach all represent smoothed (averaged) values of time series data.
51. In exponential smoothing, an alpha of .30 will cause a forecast to react more quickly to a
large error than will an alpha of .20.
Chapter 03 – Forecasting
52. Forecasts based on judgment and opinion don’t include
53. In business, forecasts are the basis for:
Chapter 03 – Forecasting
54. Which of the following features would not generally be considered common to all
forecasts?
55. Which of the following is not a step in the forecasting process?
Chapter 03 – Forecasting
56. Minimizing the sum of the squared deviations around the line is called:
57. The two general approaches to forecasting are: