MGT613 Operation Management Solved MCQS Chapter 3b by William Stevenson

MGT613 Operation Management Solved MCQS Chapter 3b by William Stevenson

Forecasting 
52. Forecasts based on judgment and opinion don't include
A. executive opinion
B. salesperson opinion
C. second opinions
D. customer surveys
E. Delphi methods
Second opinions generally refer to medical diagnoses, not demand forecasting.

53. In business, forecasts are the basis for:
A. capacity planning
B. budgeting
C. sales planning
D. production planning
E. all of the above
A wide variety of areas depend on forecasting.

54. 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.
In some forecasting situations historical data are not available.

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
We cannot eliminate all assumptions.

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
Least squares estimations minimizes the sum of squared deviations around the estimated regression function.

57. 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
Forecast approaches are either quantitative or qualitative.

58. 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
Time series analysis is a quantitative approach.

59. Accuracy in forecasting can be measured by:
A. MSE
B. MRP
C. MAPE
D. MTM
E. A & C
MSE is mean squared error; MAPE is mean absolute percent error.

60. 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.
Members of the sales force should be the organization's tightest link with its customers.

61. 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
The questionnaires are a way of fostering a consensus among divergent perspectives.

62. 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
Anonymity is important in Delphi efforts.

63. One reason for using the Delphi method in forecasting is to:
A. avoid premature consensus (bandwagon effect)
B. achieve a high degree of accuracy
C. maintain accountability and responsibility
D. be able to replicate results
E. prevent hurt feelings
A bandwagon can lead to popular but potentially inaccurate viewpoints to drown up other important considerations.

64. Detecting non-randomness in errors can be done using:
A. MSEs
B. MAPs
C. Control Charts
D. Correlation Coefficients
E. Strategies
Control charts graphically depict the statistical behavior of forecast errors.

65. Gradual, long-term movement in time series data is called:
A. seasonal variation
B. cycles
C. irregular variation
D. trend
E. random variation
Trends move the time series in a long-term direction.

66. 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 4 seasons but 30 cycles
Seasons happen within time periods; cycles happen across multiple time periods.

67. Averaging techniques are useful for:
A. distinguishing between random and non-random variations
B. smoothing out fluctuations in time series
C. eliminating historical data
D. providing accuracy in forecasts
E. average people
Smoothing helps forecasters see past random error.

68. Putting forecast errors into perspective is best done using
A. Exponential smoothing
B. MAPE
C. Linear decision rules
D. MAD
E. Hindsight
MAPE depicts the forecast error relative to what was being forecast.

69. 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
Only one piece of information is needed for a naïve forecast.

70. For the data given below, what would the naive forecast be for the next period (period #5)?

A. 58
B. 62
C. 59.5
D. 61
E. cannot tell from the data given
Period 5's forecast would be period 4's demand.

71. 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
Variation is smoothed out in moving average forecasts.

72. 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
Simple moving averages can require several periods of data.

73. 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
Fewer data points result in more responsive moving averages.

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
Exponential smoothing uses the previous forecast error to shape the next forecast.

75. 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
The most recent period of demand is given the most weight in exponential smoothing.

76. 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
An alpha of 1.0 leads to a naïve forecast.

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 previous period's forecast error of 4 units would lead to a change in the forecast of 0.6 if alpha equals 0.15.

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
Multiply the previous period's forecast error (-5) by alpha and then add to the previous period's forecast.

79. 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
Multiply the previous period's forecast error (8) by alpha and then add to the previous period's forecast.

80. Which of the following possible values of alpha would cause exponential smoothing to respond the most quickly to forecast errors?
A. 0
B. .01
C. .05
D. .10
E. .15
Larger values for alpha correspond with greater responsiveness.