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| 1.
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Capacity should be at least double forecasted demand to guarantee adequate resources. |
| | A) | True |
| | B) | False |
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| 2.
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Forecasting individual items tends to be more accurate than forecasting aggregated (grouped) items. |
| | A) | True |
| | B) | False |
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| 3.
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If quantitative data is available on which to base a forecast, it is unnecessary to consider qualitative information. |
| | A) | True |
| | B) | False |
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| 4.
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The Delphi technique is a forecasting model, developed in India, which incorporates the use of multiple regression. |
| | A) | True |
| | B) | False |
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| 5.
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In a good forecast, about half of the forecast misses should be randomly scattered above the actual results and half below the actual results. |
| | A) | True |
| | B) | False |
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| 6.
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Future demand is forecasted to help operations people plan capacity. |
| | A) | True |
| | B) | False |
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| 7.
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Longer forecast periods result in more accurate forecasts since there is more time for the estimates to materialize. |
| | A) | True |
| | B) | False |
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| 8.
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Forecasts based on Judgment and Opinion include: |
| | A) | Delphi method |
| | B) | Weighted moving average |
| | C) | Linear regression |
| | D) | Single exponential smoothing |
| | E) | Naïve Methods |
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| 9.
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For this set of errors: -1, -4, 0, +2, +3, MAD is: |
| | A) | 1.0 |
| | B) | 1.6 |
| | C) | 2.0 |
| | D) | 2.5 |
| | E) | 10.0 |
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| 10.
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Which probability distribution is used most extensively in dealing with forecasting errors? |
| | A) | Normal |
| | B) | Poisson |
| | C) | Exponential |
| | D) | Beta |
| | E) | Pareto |
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| 11.
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The cumulative forecast error is important for determining the: |
| | A) | Mean squared error. |
| | B) | Bias in forecast error. |
| | C) | Mean absolute deviation. |
| | D) | Control limits |
| | E) | Correlation coefficient |
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| 12.
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When we use exponential smoothing for forecasting, the alpha value (smoothing constant) that would give the greatest weight to the current actual would be: |
| | A) | 0 |
| | B) | .01 |
| | C) | .10 |
| | D) | .20 |
| | E) | .30 |
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| 13.
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Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast? |
| | A) | 0 |
| | B) | .01 |
| | C) | .1 |
| | D) | .5 |
| | E) | 1.0 |
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| 14.
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Simple exponential smoothing is being used to forecast demand. The previous forecast of 66 turned out to be six units less than actual demand. The next forecast is 66.9, implying a smoothing constant, alpha, equal to: |
| | A) | .01 |
| | B) | .10 |
| | C) | .15 |
| | D) | .20 |
| | E) | .60 |
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| 15.
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The temperature on Tuesday was 60 degrees, on Wednesday it was 82 degrees, on Thursday it was 77 degrees. A naive forecast for the temperature on Friday would be: |
| | A) | 73 |
| | B) | 77 |
| | C) | 80 |
| | D) | 82 |
| | E) | Can't tell from the data given |
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