Deck 3: Forecasting
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Deck 3: Forecasting
1
Forecasts based on an average tend to exhibit less variability than the original data.
True
2
The naive forecast is limited in its application to series that reflect no trend or seasonality.
False
3
Forecasts based on time-series (historical) data are referred to as associative forecasts.
False
4
The naive forecast can serve as a quick and easy standard of comparison against which to judge the cost and accuracy of other techniques.
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5
Forecasting techniques generally assume an existing causal system that will continue to exist in the future.
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6
The shorter the forecast period, the more accurately the forecasts tend to track what actually happens.
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7
The naive approach to forecasting requires a linear trend line.
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8
Forecasts help managers both to plan the system itself and to provide valuable information for using the system.
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9
When new products or services are introduced, focus forecasting models are an attractive option.
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10
Trend-adjusted exponential smoothing uses double smoothing to add twice the forecast error to last period's actual demand.
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11
Organizations that are capable of responding quickly to changing requirements can use a shorter forecast horizon and therefore benefit from more accurate forecasts.
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12
Forecasting techniques that are based on time-series data assume that future values of the series will duplicate past values.
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13
The purpose of the forecast should be established first so that the level of detail, amount of resources, and accuracy level can be understood.
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14
For new products in a strong growth mode, a low alpha will minimize forecast errors when using exponential smoothing techniques.
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15
A consumer survey is an easy and sure way to obtain accurate input from future customers since most people enjoy participating in surveys.
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16
Exponential smoothing adds a percentage (called alpha) of the last period's forecast to estimate the next period's demand.
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17
Once accepted by managers, forecasts should be held firm regardless of new input since many plans have been made using the original forecast.
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18
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.
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19
Time-series techniques involve the identification of explanatory variables that can be used to predict future demand.
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20
The Delphi approach involves the use of a series of questionnaires to achieve a consensus forecast.
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21
Removing the seasonal component from a data series (deseasonalizing) can be accomplished by dividing each data point by its appropriate seasonal relative.
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22
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 cannot be used.
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23
In order to update a moving average forecast, the values of each data point in the average must be known.
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24
In exponential smoothing, an alpha of 1.0 will generate the same forecast that a naive forecast would yield.
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25
If a pattern appears when a dependent variable is plotted against time, one should use time series analysis instead of simple linear regression.
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26
MAD is equal to the square root of MSE, which is why we calculate the easier MSE and then calculate the more difficult MAD.
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27
A forecast method is generally deemed to perform adequately when the errors exhibit an identifiable pattern.
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28
The T in the model TAF = S + T represents the time dimension (which is usually expressed in weeks or months).
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29
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.
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30
A control chart involves setting action limits for cumulative forecast error.
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31
An advantage of a weighted moving average is that recent actual results can be given more importance than what occurred a while ago.
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32
Correlation measures the strength and direction of a relationship between variables.
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33
A seasonal relative (or seasonal indexes) is expressed as a percentage of average or trend.
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34
Nonlinear and multiple regression procedures permit us to extend associative models to relationships that are nonlinear or involve more than one predictor variable.
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35
The sample standard deviation of forecast error is estimated by the square root of MSE.
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36
Exponential smoothing is a form of weighted averaging.
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37
An advantage of trend-adjusted exponential smoothing over the linear trend equation is its ability to adjust over time to changes in the trend.
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38
Forecasts of future demand are used by operations people to plan capacity.
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39
Trend-adjusted exponential smoothing requires selection of two smoothing constants.
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40
A moving average forecast tends to be more responsive to changes in the data series when more data points are included in the average.
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41
The best forecast is not necessarily the most accurate.
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42
Which of the following is not a type of judgmental forecasting?
A) executive opinions
B) sales force opinions
C) consumer surveys
D) the Delphi method
E) time series analysis
A) executive opinions
B) sales force opinions
C) consumer surveys
D) the Delphi method
E) time series analysis
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43
Which of the following would be an advantage of using a sales force composite to develop a demand forecast?
A) The sales staff is least affected by changing customer needs.
B) The sales force can easily distinguish between customer desires and probable actions.
C) The sales staff is often aware of customers' future plans.
D) Salespeople are least likely to be influenced by recent events.
E) Salespeople are least likely to be biased by sales quotas.
A) The sales staff is least affected by changing customer needs.
B) The sales force can easily distinguish between customer desires and probable actions.
C) The sales staff is often aware of customers' future plans.
D) Salespeople are least likely to be influenced by recent events.
E) Salespeople are least likely to be biased by sales quotas.
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44
When choosing a forecasting technique, a critical trade-off that must be considered is that between:
A) time series and associative.
B) seasonality and cyclicality.
C) length and duration.
D) simplicity and complexity.
E) cost and accuracy.
A) time series and associative.
B) seasonality and cyclicality.
C) length and duration.
D) simplicity and complexity.
E) cost and accuracy.
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45
Which of the following features would not generally be considered common to all forecasts?
A) Assumption of a stable underlying causal system.
B) Actual results will differ somewhat from predicted values.
C) Historical data is available on which to base the forecast.
D) Forecasts for groups of items tend to be more accurate than forecasts for individual items.
E) Accuracy decreases as the time horizon increases.
A) Assumption of a stable underlying causal system.
B) Actual results will differ somewhat from predicted values.
C) Historical data is available on which to base the forecast.
D) Forecasts for groups of items tend to be more accurate than forecasts for individual items.
E) Accuracy decreases as the time horizon increases.
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46
The more novel a new product or service design is, the more forecasters have to rely on:
A) subjective estimates.
B) seasonality.
C) cyclicality.
D) historical data.
E) smoothed variation.
A) subjective estimates.
B) seasonality.
C) cyclicality.
D) historical data.
E) smoothed variation.
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47
A tracking signal focuses on the ratio of cumulative forecast error to the corresponding value of MAD.
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48
Suppose a four-period weighted average is being used to forecast demand. Weights for the periods are as follows: wt-4 = 0.1, wt-3 = 0.2, wt-2 = 0.3 and wt-1 = 0.4. Demand observed in the previous four periods was as follows: At-4 = 380, At-3 = 410, At-2 = 390, At-1 = 400. What will be the demand forecast for period t?
A) 402
B) 397
C) 399
D) 393
E) 403
A) 402
B) 397
C) 399
D) 393
E) 403
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49
Bias is measured by the ratio of the cumulative sum of forecast errors to the mean absolute deviation (MAD).
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50
Forecasts based on judgment and opinion do not include:
A) executive opinion.
B) salesperson opinion.
C) second opinions.
D) customer surveys.
E) Delphi methods.
A) executive opinion.
B) salesperson opinion.
C) second opinions.
D) customer surveys.
E) Delphi methods.
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51
Bias exists when forecasts tend to be greater or less than the actual values of time series.
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52
Which of the following is a potential shortcoming of using sales force opinions in demand forecasting?
A) Members of the sales force often have substantial histories of working with and understanding their customers.
B) Members of the sales force often are well aware of customers' future plans.
C) Members of the sales force have direct contact with consumers.
D) Members of the sales force can have difficulty distinguishing between what customers would like to do and what they actually will do.
E) Customers often are quite open with members of the sales force with regard to future plans.
A) Members of the sales force often have substantial histories of working with and understanding their customers.
B) Members of the sales force often are well aware of customers' future plans.
C) Members of the sales force have direct contact with consumers.
D) Members of the sales force can have difficulty distinguishing between what customers would like to do and what they actually will do.
E) Customers often are quite open with members of the sales force with regard to future plans.
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53
Suppose a three-period weighted average is being used to forecast demand. Weights for the periods are as follows: wt-3 = 0.2, wt-2 = 0.3 and wt-1 = 0.5. Demand observed in the previous three periods was as follows: At-3 = 2,200, At-2 = 1,950, At-1 = 2,050. What will be the demand forecast for period t?
A) 2,000
B) 2,095
C) 1,980
D) 2,050
E) 1,875
A) 2,000
B) 2,095
C) 1,980
D) 2,050
E) 1,875
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54
Which of the following is/are a primary input into capacity, sales, and production planning?
A) product design
B) market share
C) ethics
D) globalization
E) demand forecasts
A) product design
B) market share
C) ethics
D) globalization
E) demand forecasts
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55
Which of the following is not a step in the forecasting process?
A) Determine the purpose and level of detail required.
B) Eliminate all assumptions.
C) Establish a time horizon.
D) Select a forecasting model.
E) Monitor the forecast.
A) Determine the purpose and level of detail required.
B) Eliminate all assumptions.
C) Establish a time horizon.
D) Select a forecasting model.
E) Monitor the forecast.
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56
Minimizing the sum of the squared deviations around the line is called:
A) mean squared error technique.
B) mean absolute deviation.
C) double smoothing.
D) least squares estimation.
E) predictor regression.
A) mean squared error technique.
B) mean absolute deviation.
C) double smoothing.
D) least squares estimation.
E) predictor regression.
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57
Accuracy in forecasting can be measured by:
A) MSE.
B) MRP.
C) MPS.
D) MTM.
E) MTE.
A) MSE.
B) MRP.
C) MPS.
D) MTM.
E) MTE.
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58
Seasonal relatives can be used to deseasonalize data or incorporate seasonality in a forecast.
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59
The two general approaches to forecasting are:
A) mathematical and statistical.
B) qualitative and quantitative.
C) judgmental and qualitative.
D) historical and associative.
E) precise and approximation.
A) mathematical and statistical.
B) qualitative and quantitative.
C) judgmental and qualitative.
D) historical and associative.
E) precise and approximation.
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60
The use of a control chart assumes that random errors are normally distributed about a mean of zero.
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61
In order to increase the responsiveness of a forecast made using the moving average technique, the number of data points in the average should be:
A) decreased.
B) increased.
C) multiplied by a larger alpha.
D) multiplied by a smaller alpha.
E) eliminated if the MAD is greater than the MSE.
A) decreased.
B) increased.
C) multiplied by a larger alpha.
D) multiplied by a smaller alpha.
E) eliminated if the MAD is greater than the MSE.
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62
One reason for using the Delphi method in forecasting is to:
A) reduce the risk that one individual's opinion will prevail.
B) achieve a high degree of accuracy.
C) maintain accountability and responsibility.
D) be able to replicate results.
E) prevent hurt feelings.
A) reduce the risk that one individual's opinion will prevail.
B) achieve a high degree of accuracy.
C) maintain accountability and responsibility.
D) be able to replicate results.
E) prevent hurt feelings.
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63
Putting forecast errors into perspective is best done using
A) exponential smoothing.
B) MAPE.
C) linear decision rules.
D) MAD.
E) hindsight.
A) exponential smoothing.
B) MAPE.
C) linear decision rules.
D) MAD.
E) hindsight.
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64
Detecting nonrandomness in errors can be done using:
A) MSEs.
B) MAPs.
C) control charts.
D) correlation coefficients.
E) strategies.
A) MSEs.
B) MAPs.
C) control charts.
D) correlation coefficients.
E) strategies.
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65
Moving average forecasting techniques do the following:
A) Immediately reflect changing patterns in the data.
B) Lead changes in the data.
C) Smooth variations in the data.
D) Operate independently of recent data.
E) Assist when organizations are relocating.
A) Immediately reflect changing patterns in the data.
B) Lead changes in the data.
C) Smooth variations in the data.
D) Operate independently of recent data.
E) Assist when organizations are relocating.
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66
Which is not a characteristic of exponential smoothing?
A) smoothes random variations in the data
B) weights each historical value equally
C) has an easily altered weighting scheme
D) has minimal data storage requirements
E) smoothes real variations in the data
A) smoothes random variations in the data
B) weights each historical value equally
C) has an easily altered weighting scheme
D) has minimal data storage requirements
E) smoothes real variations in the data
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67
Gradual, long-term movement in time series data is called:
A) seasonal variation.
B) cycles.
C) irregular variation.
D) trend.
E) random variation.
A) seasonal variation.
B) cycles.
C) irregular variation.
D) trend.
E) random variation.
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68
Which phrase most closely describes the Delphi technique?
A) associative forecast
B) consumer survey
C) series of questionnaires
D) developed in India
E) historical data
A) associative forecast
B) consumer survey
C) series of questionnaires
D) developed in India
E) historical data
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69
For the data given below, what would the naive forecast be for period 5? 
A) 58
B) 62
C) 59.5
D) 61
E) cannot tell from the data given

A) 58
B) 62
C) 59.5
D) 61
E) cannot tell from the data given
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70
Given an actual demand of 105, a forecasted value of 97, and an alpha of .4, the simple exponential smoothing forecast for the next period would be:
A) 80.8.
B) 93.8.
C) 100.2.
D) 101.8.
E) 108.2.
A) 80.8.
B) 93.8.
C) 100.2.
D) 101.8.
E) 108.2.
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71
Using the latest observation in a sequence of data to forecast the next period is:
A) a moving average forecast.
B) a naive forecast.
C) an exponentially smoothed forecast.
D) an associative forecast.
E) regression analysis.
A) a moving average forecast.
B) a naive forecast.
C) an exponentially smoothed forecast.
D) an associative forecast.
E) regression analysis.
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72
Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast?
A) 0
B) 0.01
C) 0.1
D) 0.5
E) 1
A) 0
B) 0.01
C) 0.1
D) 0.5
E) 1
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73
Which is not a characteristic of simple moving averages applied to time series data?
A) smoothes random variations in the data
B) weights each historical value equally
C) lags changes in the data
D) requires only last period's forecast and actual data
E) smoothes real variations in the data
A) smoothes random variations in the data
B) weights each historical value equally
C) lags changes in the data
D) requires only last period's forecast and actual data
E) smoothes real variations in the data
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74
A forecast based on the previous forecast plus a percentage of the forecast error is:
A) a naive forecast.
B) a simple moving average forecast.
C) a centered moving average forecast.
D) an exponentially smoothed forecast.
E) an associative forecast.
A) a naive forecast.
B) a simple moving average forecast.
C) a centered moving average forecast.
D) an exponentially smoothed forecast.
E) an associative forecast.
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75
The primary difference between seasonality and cycles is:
A) the duration of the repeating patterns.
B) the magnitude of the variation.
C) the ability to attribute the pattern to a cause.
D) the direction of the movement.
E) there are only four seasons but 30 cycles.
A) the duration of the repeating patterns.
B) the magnitude of the variation.
C) the ability to attribute the pattern to a cause.
D) the direction of the movement.
E) there are only four seasons but 30 cycles.
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76
The forecasting method which uses anonymous questionnaires to achieve a consensus forecast is:
A) sales force opinions.
B) consumer surveys.
C) the Delphi method.
D) time series analysis.
E) executive opinions.
A) sales force opinions.
B) consumer surveys.
C) the Delphi method.
D) time series analysis.
E) executive opinions.
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77
Simple exponential smoothing is being used to forecast demand. The previous forecast of 66 turned out to be four units less than actual demand. The next forecast is 66.6, implying a smoothing constant, alpha, equal to:
A) .01.
B) .10.
C) .15.
D) .20.
E) .60.
A) .01.
B) .10.
C) .15.
D) .20.
E) .60.
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78
Given an actual demand of 59, a previous forecast of 64, and an alpha of .3, what would the forecast for the next period be using simple exponential smoothing?
A) 36.9
B) 57.5
C) 60.5
D) 62.5
E) 65.5
A) 36.9
B) 57.5
C) 60.5
D) 62.5
E) 65.5
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79
Averaging techniques are useful for:
A) distinguishing between random and nonrandom variations.
B) smoothing out fluctuations in time series.
C) eliminating historical data.
D) providing accuracy in forecasts.
E) average people.
A) distinguishing between random and nonrandom variations.
B) smoothing out fluctuations in time series.
C) eliminating historical data.
D) providing accuracy in forecasts.
E) average people.
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80
Which of the following possible values of alpha would cause exponential smoothing to respond the most quickly to forecast errors?
A) 0
B) 0.01
C) 0.05
D) 0.1
E) 0.15
A) 0
B) 0.01
C) 0.05
D) 0.1
E) 0.15
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