Please copy and paste this embed script to where you want to embed

Chapter 03 Forecasting

True / False Questions

1. Forecasting techniques generally assume an existing causal system that will continue to exist in the future. True

False

2. For new products in a strong growth mode, a low alpha will minimize forecast errors when using exponential smoothing techniques. True

False

3. Once accepted by managers, forecasts should be held firm regardless of new input since many plans have been made using the original forecast. True

False

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. True

False

5. Forecasts help managers both to plan the system itself and to provide valuable information for using the system. True

False

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

False

7. When new products or services are introduced, focus forecasting models are an attractive option. True

False

3-1 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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

False

9. Forecasts based on time-series (historical) data are referred to as associative forecasts. True

False

10. Time-series techniques involve the identification of explanatory variables that can be used to predict future demand. True

False

11. A consumer survey is an easy and sure way to obtain accurate input from future customers since most people enjoy participating in surveys. True

False

12. The Delphi approach involves the use of a series of questionnaires to achieve a consensus forecast. True

False

13. Exponential smoothing adds a percentage (called alpha) of the last period's forecast to estimate the next period's demand. True

False

14. The shorter the forecast period, the more accurately the forecasts tend to track what actually happens. True

False

15. Forecasting techniques that are based on time-series data assume that future values of the series will duplicate past values. True

False

16. Trend-adjusted exponential smoothing uses double smoothing to add twice the forecast error to last period's actual demand. True

False

17. Forecasts based on an average tend to exhibit less variability than the original data. True

False

3-2 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

18. The naive approach to forecasting requires a linear trend line. True

False

19. The naive forecast is limited in its application to series that reflect no trend or seasonality. True

False

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

False

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. True

False

22. In order to update a moving average forecast, the values of each data point in the average must be known. True

False

23. Forecasts of future demand are used by operations people to plan capacity. True

False

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

False

25. Exponential smoothing is a form of weighted averaging. True

False

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. True

False

27. The T in the model TAF = S + T represents the time dimension (which is usually expressed in weeks or months). True

False

28. Trend-adjusted exponential smoothing requires selection of two smoothing constants. True

False

3-3 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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

False

30. A seasonal relative (or seasonal indexes) is expressed as a percentage of average or trend. True

False

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 cannot be used. True

False

32. Removing the seasonal component from a data series (deseasonalizing) can be accomplished by dividing each data point by its appropriate seasonal relative. True

False

33. If a pattern appears when a dependent variable is plotted against time, one should use time series analysis instead of regression analysis. True

False

34. Curvilinear and multiple regression procedures permit us to extend associative models to relationships that are nonlinear or involve more than one predictor variable. True

False

35. The sample standard deviation of forecast error is equal to the square root of MSE. True

False

36. Correlation measures the strength and direction of a relationship between variables. True

False

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. True

False

38. In exponential smoothing, an alpha of 1.0 will generate the same forecast that a naive forecast would yield. True

False

3-4 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

39. A forecast method is generally deemed to perform adequately when the errors exhibit an identifiable pattern. True

False

40. A control chart involves setting action limits for cumulative forecast error. True

False

41. A tracking signal focuses on the ratio of cumulative forecast error to the corresponding value of MAD. True

False

42. The use of a control chart assumes that errors are normally distributed about a mean of zero. True

False

43. Bias exists when forecasts tend to be greater or less than the actual values of time series. True

False

44. Bias is measured by the cumulative sum of forecast errors. True

False

45. Seasonal relatives can be used to deseasonalize data or incorporate seasonality in a forecast. True

False

46. The best forecast is not necessarily the most accurate. True

False

Multiple Choice Questions

3-5 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

47. 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. 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: A t-4 = 380, At-3 = 410, At-2 = 390, At-1 = 400. What will be the demand forecast for period t?

A. B. C. D. E.

402 397 399 393 403

49. 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. B. C. D. E.

2,000 2,095 1,980 2,050 1,875

50. When choosing a forecasting technique, a critical trade-off that must be considered is that between:

A. B. C. D. E.

time series and associative. seasonality and cyclicality. length and duration. simplicity and complexity. cost and accuracy.

3-6 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

51. The more novel a new product or service design is, the more forecasters have to rely on:

A. B. C. D. E.

subjective estimates. seasonality. cyclicality. historical data. smoothed variation.

52. Forecasts based on judgment and opinion do not include:

A. B. C. D. E.

executive opinion. salesperson opinion. second opinions. customer surveys. Delphi methods.

53. Which of the following is/are a primary input into capacity, sales, and production planning?

A. B. C. D. E.

product design market share ethics globalization demand forecasts

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. 55. Which of the following is not a step in the forecasting process?

A. B. C. D. E.

Determine the purpose and level of detail required. Eliminate all assumptions. Establish a time horizon. Select a forecasting model. Monitor the forecast.

3-7 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

56. Minimizing the sum of the squared deviations around the line is called:

A. B. C. D. E.

mean squared error technique. mean absolute deviation. double smoothing. least squares estimation. predictor regression.

57. The two general approaches to forecasting are:

A. B. C. D. E.

mathematical and statistical. qualitative and quantitative. judgmental and qualitative. historical and associative. precise and approximation.

58. Which of the following is not a type of judgmental forecasting?

A. B. C. D. E.

executive opinions sales force opinions consumer surveys the Delphi method time series analysis

59. Accuracy in forecasting can be measured by:

A. B. C. D. E.

MSE. MRP. MPS. MTM. MTE.

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.

3-8 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

61. Which phrase most closely describes the Delphi technique?

A. B. C. D. E.

associative forecast consumer survey series of questionnaires developed in India historical data

62. The forecasting method which uses anonymous questionnaires to achieve a consensus forecast is:

A. B. C. D. E.

sales force opinions. consumer surveys. the Delphi method. time series analysis. executive opinions.

63. One reason for using the Delphi method in forecasting is to:

A. B. C. D. E.

avoid premature consensus (bandwagon effect). achieve a high degree of accuracy. maintain accountability and responsibility. be able to replicate results. prevent hurt feelings.

64. Detecting nonrandomness in errors can be done using:

A. B. C. D. E.

MSEs. MAPs. control charts. correlation coefficients. strategies.

65. Gradual, long-term movement in time series data is called:

A. B. C. D. E.

seasonal variation. cycles. irregular variation. trend. random variation.

3-9 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

66. The primary difference between seasonality and cycles is:

A. B. C. D. E.

the duration of the repeating patterns. the magnitude of the variation. the ability to attribute the pattern to a cause. the direction of the movement. there are only four seasons but 30 cycles.

67. 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. 68. Putting forecast errors into perspective is best done using

A. B. C. D. E.

exponential smoothing. MAPE. linear decision rules. MAD. hindsight.

69. Using the latest observation in a sequence of data to forecast the next period is:

A. B. C. D. E.

a moving average forecast. a naive forecast. an exponentially smoothed forecast. an associative forecast. regression analysis.

3-10 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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

A. B. C. D. E.

58 62 59.5 61 cannot tell from the data given

71. Moving average forecasting techniques do the following:

A. B. C. D. E.

Immediately reflect changing patterns in the data. Lead changes in the data. Smooth variations in the data. Operate independently of recent data. Assist when organizations are relocating.

72. Which is not a characteristic of simple moving averages applied to time series data?

A. B. C. D. E.

smoothes random variations in the data weights each historical value equally lags changes in the data requires only last period's forecast and actual data smoothes real variations in the 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. B. C. D. E.

decreased. increased. multiplied by a larger alpha. multiplied by a smaller alpha. eliminated if the MAD is greater than the MSE.

3-11 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

74. A forecast based on the previous forecast plus a percentage of the forecast error is:

A. B. C. D. E.

a naive forecast. a simple moving average forecast. a centered moving average forecast. an exponentially smoothed forecast. an associative forecast.

75. Which is not a characteristic of exponential smoothing?

A. B. C. D. E.

smoothes random variations in the data weights each historical value equally has an easily altered weighting scheme has minimal data storage requirements smoothes real variations in the data

76. Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast?

A. B. C. D. E.

0 .01 .1 .5 1.0

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. B. C. D. E.

.01. .10. .15. .20. .60.

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. B. C. D. E.

36.9 57.5 60.5 62.5 65.5

3-12 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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. B. C. D. E.

80.8. 93.8. 100.2. 101.8. 108.2.

80. Which of the following possible values of alpha would cause exponential smoothing to respond the most quickly to forecast errors?

A. B. C. D. E.

0 .01 .05 .10 .15

81. A manager uses the following equation to predict monthly receipts: Y t = 40,000 + 150t. What is the forecast for July if t = 0 in April of this year?

A. B. C. D. E.

40,450 40,600 42,100 42,250 42,400

82. In trend-adjusted exponential smoothing, the trend-adjusted forecast consists of:

A. an exponentially smoothed forecast and a smoothed trend factor. B. an exponentially smoothed forecast and an estimated trend value. C. the old forecast adjusted by a trend factor. D. the old forecast and a smoothed trend factor. E. a moving average and a trend factor.

3-13 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

83. In the additive model for seasonality, seasonality is expressed as a ______________ adjustment to the average; in the multiplicative model, seasonality is expressed as a __________ adjustment to the average.

A. B. C. D. E.

quantity; percentage percentage; quantity quantity; quantity percentage; percentage qualitative; quantitative

84. Which technique is used in computing seasonal relatives?

A. B. C. D. E.

double smoothing Delphi mean squared error centered moving average exponential smoothing

85. A persistent tendency for forecasts to be greater than or less than the actual values is called:

A. B. C. D. E.

bias. tracking. control charting. positive correlation. linear regression.

86. Which of the following might be used to indicate the cyclical component of a forecast?

A. B. C. D. E.

leading variable mean squared error Delphi technique exponential smoothing mean absolute deviation

87. The primary method for associative forecasting is:

A. B. C. D. E.

sensitivity analysis. regression analysis. simple moving averages. centered moving averages. exponential smoothing.

3-14 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

88. Which term most closely relates to associative forecasting techniques?

A. B. C. D. E.

time series data expert opinions Delphi technique consumer survey predictor variables

89. Which of the following corresponds to the predictor variable in simple linear regression?

A. B. C. D. E.

regression coefficient dependent variable independent variable predicted variable demand coefficient

90. The mean absolute deviation is used to:

A. B. C. D. E.

estimate the trend line. eliminate forecast errors. measure forecast accuracy. seasonally adjust the forecast. compute periodic forecast errors.

91. Given forecast errors of 4, 8, and -3, what is the mean absolute deviation?

A. B. C. D. E.

4 3 5 6 12

92. Given forecast errors of 5, 0, -4, and 3, what is the mean absolute deviation?

A. B. C. D. E.

4 3 2.5 2 1

3-15 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

93. Given forecast errors of 5, 0, -4, and 3, what is the bias?

A. B. C. D. E.

-4 4 5 12 6

94. Which of the following is used for constructing a control chart?

A. B. C. D.

mean absolute deviation mean squared error tracking signal bias

95. The two most important factors in choosing a forecasting technique are:

A. B. C. D. E.

cost and time horizon. accuracy and time horizon. cost and accuracy. quantity and quality. objective and subjective components.

96. The degree of management involvement in short-range forecasts is:

A. B. C. D. E.

none. low. moderate. high. total.

97. Which of the following is not necessarily an element of a good forecast?

A. B. C. D. E.

estimate of accuracy timeliness meaningful units low cost written

3-16 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

98. Forecasting techniques generally assume:

A. the absence of randomness. B. continuity of some underlying causal system. C. a linear relationship between time and demand. D. accuracy that increases the farther out in time the forecast projects. E. accuracy that is better when individual items, rather than groups of items, are being considered. 99. A managerial approach toward forecasting which seeks to actively influence demand is:

A. B. C. D. E.

reactive. proactive. influential. protracted. retroactive.

100 Customer service levels can be improved by better: . A. B. C. D. E.

mission statements. control charting. short-term forecast accuracy. exponential smoothing. customer selection.

101 Given the following historical data, what is the simple three-period moving average . forecast for period 6?

A. B. C. D. E.

67 115 69 68 68.67

3-17 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

102 Given the following historical data and weights of .5, .3, and .2, what is the three. period moving average forecast for period 5?

A. B. C. D. E.

144.20 144.80 144.67 143.00 144.00

103 Use of simple linear regression analysis assumes that: . A. variations around the line are nonrandom. B. deviations around the line are normally distributed. C. predictions can easily be made beyond the range of observed values of the predictor variable. D. all possible predictor variables are included in the model. E. the variance of error terms (deviations) varies directly with the predictor variable. 104 Given forecast errors of -5, -10, and +15, the MAD is: . A. B. C. D. E.

0. 10. 30. 175. 225.

3-18 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

105 The president of State University wants to forecast student enrollments for this . academic year based on the following historical data:

What is the forecast for this year using the naive approach?

A. B. C. D. E.

18,750 19,500 21,000 22,000 22,800

106 The president of State University wants to forecast student enrollments for this . academic year based on the following historical data:

What is the forecast for this year using a four-year simple moving average?

A. B. C. D. E.

18,750 19,500 21,000 22,650 22,800

3-19 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

107 The president of State University wants to forecast student enrollments for this . academic year based on the following historical data:

What is the forecast for this year using exponential smoothing with alpha = .5, if the forecast for two years ago was 16,000?

A. B. C. D. E.

18,750 19,500 21,000 22,650 22,800

108 The president of State University wants to forecast student enrollments for this . academic year based on the following historical data:

What is the forecast for this year using the least squares trend line for these data?

A. B. C. D. E.

18,750 19,500 21,000 22,650 22,800

3-20 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

109 The president of State University wants to forecast student enrollments for this . academic year based on the following historical data:

What is the forecast for this year using trend-adjusted (double) smoothing with alpha = .05 and beta = .3, if the forecast for last year was 21,000, the forecast for two years ago was 19,000, and the trend estimate for last year's forecast was 1,500?

A. B. C. D. E.

18,750 19,500 21,000 22,650 22,800

110 The business analyst for Video Sales, Inc. wants to forecast this year's demand for . DVD decoders based on the following historical data:

What is the forecast for this year using the naive approach?

A. B. C. D. E.

163 180 300 420 510

3-21 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

111 The business analyst for Video Sales, Inc. wants to forecast this year's demand for . DVD decoders based on the following historical data:

What is the forecast for this year using a three-year weighted moving average with weights of .5, .3, and .2?

A. B. C. D. E.

163 180 300 420 510

112 The business analyst for Video Sales, Inc. wants to forecast this year's demand for . DVD decoders based on the following historical data:

What is the forecast for this year using exponential smoothing with alpha = .4, if the forecast for two years ago was 750?

A. B. C. D. E.

163 180 300 420 510

3-22 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

113 The business analyst for Video Sales, Inc. wants to forecast this year's demand for . DVD decoders based on the following historical data:

What is the forecast for this year using the least squares trend line for these data?

A. B. C. D. E.

163 180 300 420 510

114 The business analyst for Video Sales, Inc. wants to forecast this year's demand for . DVD decoders based on the following historical data:

What is the forecast for this year using trend-adjusted (double) smoothing with alpha = .3 and beta = .2, if the forecast for last year was 310, the forecast for two years ago was 430, and the trend estimate for last year's forecast was -150?

A. B. C. D. E.

162.4 180.3 301.4 403.2 510.0

3-23 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

115 Professor Very Busy needs to allocate time next week to include time for office hours. . He needs to forecast the number of students who will seek appointments. He has gathered the following data:

What is this week's forecast using the naive approach?

A. B. C. D. E.

45 50 52 65 78

116 Professor Very Busy needs to allocate time next week to include time for office hours. . He needs to forecast the number of students who will seek appointments. He has gathered the following data:

What is this week's forecast using a three-week simple moving average?

A. B. C. D. E.

49 50 52 65 78

3-24 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

117 Professor Very Busy needs to allocate time next week to include time for office hours. . He needs to forecast the number of students who will seek appointments. He has gathered the following data:

What is this week's forecast using exponential smoothing with alpha = .2, if the forecast for two weeks ago was 90?

A. B. C. D. E.

49 50 52 65 77

118 Professor Very Busy needs to allocate time next week to include time for office hours. . He needs to forecast the number of students who will seek appointments. He has gathered the following data:

What is this week's forecast using the least squares trend line for these data?

A. B. C. D. E.

49 50 52 65 78

3-25 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

119 Professor Very Busy needs to allocate time next week to include time for office hours. . He needs to forecast the number of students who will seek appointments. He has gathered the following data:

What is this week's forecast using trend-adjusted (double) smoothing with alpha = .5 and beta = .1, if the forecast for last week was 65, the forecast for two weeks ago was 75, and the trend estimate for last week's forecast was -5?

A. B. C. D. E.

49.3 50.6 51.3 65.4 78.7

120 A concert promoter is forecasting this year's attendance for one of his concerts based . on the following historical data:

What is this year's forecast using the naive approach?

A. B. C. D. E.

22,000 20,000 18,000 15,000 12,000

3-26 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

121 A concert promoter is forecasting this year's attendance for one of his concerts based . on the following historical data:

What is this year's forecast using a two-year weighted moving average with weights of .7 and .3?

A. B. C. D. E.

19,400 18,600 19,000 11,400 10,600

122 A concert promoter is forecasting this year's attendance for one of his concerts based . on the following historical data:

What is this year's forecast using exponential smoothing with alpha = .2, if last year's smoothed forecast was 15,000?

A. B. C. D. E.

20,000 19,000 17,500 16,000 15,000

3-27 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

123 A concert promoter is forecasting this year's attendance for one of his concerts based . on the following historical data:

What is this year's forecast using the least squares trend line for these data?

A. B. C. D. E.

20,000 21,000 22,000 23,000 24,000

124 A concert promoter is forecasting this year's attendance for one of his concerts based . on the following historical data:

The previous trend line had predicted 18,500 for two years ago, and 19,700 for last year. What was the mean absolute deviation for these forecasts?

A. B. C. D. E.

100 200 400 500 800

3-28 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

125 The dean of a school of business is forecasting total student enrollment for this year's . summer session classes based on the following historical data:

What is this year's forecast using the naive approach?

A. B. C. D. E.

2,000 2,200 2,800 3,000 4,300

126 The dean of a school of business is forecasting total student enrollment for this year's . summer session classes based on the following historical data:

What is this year's forecast using a three-year simple moving average?

A. B. C. D. E.

2,667 2,600 2,500 2,400 2,333

3-29 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

127 The dean of a school of business is forecasting total student enrollment for this year's . summer session classes based on the following historical data:

What is this year's forecast using exponential smoothing with alpha = .4, if last year's smoothed forecast was 2,600?

A. B. C. D. E.

2,600 2,760 2,800 3,840 3,000

128 The dean of a school of business is forecasting total student enrollment for this year's . summer session classes based on the following historical data:

What is the annual rate of change (slope) of the least squares trend line for these data?

A. B. C. D. E.

0 200 400 180 360

3-30 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

129 The dean of a school of business is forecasting total student enrollment for this year's . summer session classes based on the following historical data:

What is this year's forecast using the least squares trend line for these data?

A. B. C. D. E.

3,600 3,500 3,400 3,300 3,200

130 The owner of Darkest Tans Unlimited in a local mall is forecasting this month's . (October's) demand for the one new tanning booth based on the following historical data:

What is this month's forecast using the naive approach?

A. B. C. D. E.

100 160 130 140 120

3-31 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

131 The owner of Darkest Tans Unlimited in a local mall is forecasting this month's . (October's) demand for the one new tanning booth based on the following historical data:

What is this month's forecast using a four-month weighted moving average with weights of .4, .3, .2, and .1?

A. B. C. D. E.

120 129 141 135 140

132 The owner of Darkest Tans Unlimited in a local mall is forecasting this month's . (October's) demand for the one new tanning booth based on the following historical data:

What is this month's forecast using exponential smoothing with alpha = .2, if August's forecast was 145?

A. B. C. D. E.

144 140 142 148 163

3-32 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

133 The owner of Darkest Tans Unlimited in a local mall is forecasting this month's . (October's) demand for the one new tanning booth based on the following historical data:

What is the monthly rate of change (slope) of the least squares trend line for these data?

A. B. C. D. E.

320 102 8 -.4 -8

134 The owner of Darkest Tans Unlimited in a local mall is forecasting this month's . (October's) demand for the one new tanning booth based on the following historical data:

What is this month's forecast using the least squares trend line for these data?

A. B. C. D. E.

1,250 128.6 102 158 164

3-33 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

135 Which of the following mechanisms for enhancing profitability is most likely to result . from improving short-term forecast performance?

A. B. C. D. E.

increased inventory reduced flexibility higher-quality products greater customer satisfaction greater seasonality

136 Which of the following changes would tend to shorten the time frame for short-term . forecasting?

A. bringing greater variety into the product mix B. increasing the flexibility of the production system C. ordering fewer weather-sensitive items D. adding more special-purpose equipment E. investing in the production system to make it more task-specific 137 Which of the following helps improve supply chain forecasting performance? . A. B. C. D. E.

contracts that require supply chain members to formulate long-term forecasts penalties for supply chain members that adjust forecasts sharing forecasts or demand data across the supply chain increasing lead times for critical supply chain members increasing the number of suppliers at critical junctures in the supply chain

138 Which of the following would tend to decrease forecast accuracy? . A. a reduction in demand variability B. a shortening of the forecast time horizon C. an attempt to forecast demand for a group of similar items rather than an individual item D. a change in the underlying causal system 139 Which of the following is the most valuable piece of information the sales force can . bring into forecasting situations?

A. what customers are most likely to do in the future B. what customers most want to do in the future C. what customers' future plans are D. whether customers are satisfied or dissatisfied with their performance in the past E. what the salesperson's appropriate sales quota should be

3-34 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

Essay Questions

140 Develop a forecast for the next period, given the data below, using a three-period . moving average.

141 Consider the data below: .

Using exponential smoothing with alpha = .2, and assuming the forecast for period 11 was 80, what would the forecast for period 14 be?

3-35 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

142 A manager is using exponential smoothing to predict merchandise returns at a . suburban branch of a department store chain. Given a previous forecast of 140 items, an actual number of returns of 148 items, and a smoothing constant equal to .15, what is the forecast for the next period?

143 A manager is using the equation below to forecast quarterly demand for a product: . Yt = 6,000 + 80t where t = 0 at Q2 of last year Quarter relatives are Q1 = .6, Q2 = .9, Q3 = 1.3, and Q4 = 1.2. What forecasts are appropriate for the last quarter of this year and the first quarter of next year?

144 Over the past five years, a firm's sales have averaged 250 units in the first quarter of . each year, 100 units in the second quarter, 150 units in the third quarter, and 300 units in the fourth quarter. What are appropriate quarter relatives for this firm's sales? Hint: Only minimal computations are necessary.

3-36 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

145 A manager has been using a certain technique to forecast demand for gallons of ice . cream for the past six periods. Actual and predicted amounts are shown below. Would a naive forecast have produced better results?

146 A new car dealer has been using exponential smoothing with an alpha of .2 to . forecast weekly new car sales. Given the data below, would a naive forecast have provided greater accuracy? Explain. Assume an initial exponential forecast of 60 units in period 2 (i.e., no forecast for period 1).

3-37 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

147 A CPA firm has been using the following equation to predict annual demand for tax . audits: Yt = 55 + 4t. Demand for the past few years is shown below. Is the forecast performing as well as it might? Explain.

148 Given the data below, develop a forecast for period 6 using a four-period weighted . moving average and weights of .4, .3, .2 and .1.

3-38 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

149 Use linear regression to develop a predictive model for demand for burial vaults . based on sales of caskets.

150 Given the following data, develop a linear regression model for y as a function of x. .

3-39 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

151 Given the following data, develop a linear regression model for y as a function of x. .

152 Develop a linear trend equation for the data on bread deliveries shown below. . Forecast deliveries for period 11 through 14.

3-40 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

153 Demand for the last four months was: .

A) Predict demand for July using each of these methods: 1) a three-period moving average 2) exponential smoothing with alpha equal to .20 (use a naive forecast for April for your first forecast) B) If the naive approach had been used to predict demand for April through June, what would MAD have been for those months?

3-41 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

154 A manager wants to choose one of two forecasting alternatives. Each alternative was . tested using historical data. The resulting forecast errors for the two are shown in the table. Analyze the data and recommend a course of action to the manager.

155 A manager uses this equation to predict demand: Y t = 20 + 4t. Over the past eight . periods, demand has been as follows. Are the results acceptable? Explain.

3-42 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

156 Data on demand over the last few years are available as follows: .

What would this year's forecast be if we were using the naive approach?

3-43 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

157 Data on demand over the last few years are available as follows: .

What is this year's forecast using a four-year simple moving average?

3-44 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

158 Data on demand over the last few years are available as follows: .

What is this year's forecast using exponential smoothing with alpha = .25, if last year's smoothed forecast was 45?

3-45 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

159 Data on demand over the last few years are available as follows: .

What are this and next year's forecasts using the least squares trend line for these data?

3-46 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

160 Data on demand over the last few years are available as follows: .

What is this year's forecast using trend-adjusted (double) smoothing with alpha = .2 and beta = .1, if the forecast for last year was 56, the forecast for two years ago was 46, and the trend estimate for last year's forecast was 7?

161 Data on the last three years of demand are available as follows: .

What is the centered moving average for spring two years ago?

3-47 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

162 Data on the last three years of demand are available as follows: .

What is the spring's seasonal relative?

163 Data on the last three years of demand are available as follows: .

What is the linear regression trend line for these data (t = 0 for spring, three years ago)?

3-48 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

164 Data on the last three years of demand are available as follows: .

What is this year's seasonally adjusted forecast for each season?

3-49 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

Chapter 03 Forecasting Answer Key

True / False Questions

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Level of Difficulty: 2 Medium Topic: Features Common to All Forecasts

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation 3-50 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

Blooms: Understand Learning Objective: 03-04 Outline the steps in the forecasting process. Level of Difficulty: 1 Easy Topic: Steps in the Forecasting Process

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-01 List features common to all forecasts. Level of Difficulty: 2 Medium Topic: Features Common to All Forecasts

5.

Forecasts help managers both to plan the system itself and to provide valuable information for using the system. TRUE Both planning and use are shaped by forecasts.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-01 List features common to all forecasts. Level of Difficulty: 1 Easy Topic: Forecasting

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 more quickly, its forecasts need not be so long term.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-03 List the elements of a good forecast. Level of Difficulty: 2 Medium Topic: Elements of a Good Forecast

3-51 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Level of Difficulty: 2 Medium Topic: Steps in the Forecasting Process

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 1 Easy Topic: Associative Forecasting Techniques

3-52 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

10.

Time-series techniques involve the identification of explanatory variables that can be used to predict future demand. FALSE Associative forecasts involve identifying explanatory variables.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-06 Describe four qualitative forecasting techniques. Level of Difficulty: 1 Easy Topic: Qualitative Forecasts

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-06 Describe four qualitative forecasting techniques. Level of Difficulty: 1 Easy Topic: Qualitative Forecasts

3-53 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

13.

Exponential smoothing adds a percentage (called alpha) of the last period's forecast to estimate the next period's demand. FALSE Exponential smoothing adds a percentage to the last period's forecast error.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-01 List features common to all forecasts. Level of Difficulty: 1 Easy Topic: Monitoring the Forecast Error

15.

Forecasting techniques that are based on time-series data assume that future values of the series will duplicate past values. FALSE Time-series forecasts assume that future patterns in the series will mimic past patterns in the series.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-54 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-01 List features common to all forecasts. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

18.

The naive approach to forecasting requires a linear trend line. FALSE The naive approach is useful in a wider variety of settings.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-55 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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 naive forecast is more limited in its application.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

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 naive forecast performs reasonably well when compared to more complex techniques.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-56 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-04 Outline the steps in the forecasting process. Level of Difficulty: 1 Easy Topic: Forecasting

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking 3-57 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

28.

Trend-adjusted exponential smoothing requires selection of two smoothing constants. TRUE One is for the trend and one is for the random error.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-58 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

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 cannot be used. TRUE Computing seasonal relatives depends on past data being available.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-59 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

32.

Removing the seasonal component from a data series (deseasonalizing) can be accomplished by dividing each data point by its appropriate seasonal relative. TRUE Deseasonalized data points have been adjusted for seasonal influences.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 3 Hard Topic: Associative Forecasting Techniques

34.

Curvilinear and multiple regression procedures permit us to extend associative models to relationships that are nonlinear or involve more than one predictor variable. TRUE Regression analysis can be used in a variety of settings.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 2 Medium Topic: Associative Forecasting Techniques

3-60 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 1 Easy Topic: Forecast Accuracy

36.

Correlation measures the strength and direction of a relationship between variables. TRUE The association between two variations is summarized in the correlation coefficient.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 2 Medium Topic: Associative Forecasting Techniques

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 2 Medium Topic: Forecast Accuracy

38.

In exponential smoothing, an alpha of 1.0 will generate the same forecast that a naive forecast would yield. TRUE With alpha equal to 1 we are using a naive forecasting method.

AACSB: Reflective Thinking 3-61 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 2 Medium Topic: Forecast Accuracy

40.

A control chart involves setting action limits for cumulative forecast error. FALSE Control charts set action limits for the tracking signal.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Level of Difficulty: 2 Medium Topic: Monitoring the Forecast Error

41.

A tracking signal focuses on the ratio of cumulative forecast error to the corresponding value of MAD. TRUE Large absolute values of the tracking signal suggest a fundamental change in the forecast model's performance.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Level of Difficulty: 2 Medium Topic: Monitoring the Forecast Error

3-62 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

42.

The use of a control chart assumes that errors are normally distributed about a mean of zero. TRUE Over time, a forecast model's tracking signal should fluctuate randomly about a mean of zero.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Level of Difficulty: 3 Hard Topic: Monitoring the Forecast Error

43.

Bias exists when forecasts tend to be greater or less than the actual values of time series. TRUE A tendency in one direction is defined as bias.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 1 Easy Topic: Monitoring the Forecast Error

44.

Bias is measured by the cumulative sum of forecast errors. TRUE Bias would result in the cumulative sum of forecast errors being large in absolute value.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 2 Medium Topic: Monitoring the Forecast Error

3-63 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

45.

Seasonal relatives can be used to deseasonalize data or incorporate seasonality in a forecast. TRUE Seasonal relatives are used to deseasonalize data to forecast future values of the underlying trend, and they are also used to reseasonalize deseasonalized forecasts.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

46.

The best forecast is not necessarily the most accurate. TRUE More accuracy often comes at too high a cost to be worthwhile.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-01 List features common to all forecasts. Level of Difficulty: 2 Medium Topic: Elements of a Good Forecast

Multiple Choice Questions

3-64 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

47.

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. Customers themselves may be unclear regarding what they'd like to do versus what they'll actually do.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-06 Describe four qualitative forecasting techniques. Level of Difficulty: 1 Easy Topic: Qualitative Forecasts

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: A t-4 = 380, At-3 = 410, At-2 = 390, At-1 = 400. What will be the demand forecast for period t?

A. B. C. D. E.

402 397 399 393 403

The forecast will be (.1 * 380) + (.2 * 410) + (.3 * 390) + (.4 * 400) = 397.

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Learning Objective: 03-09 Prepare a weighted-average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-65 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

49.

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: A t-3 = 2,200, At-2 = 1,950, At-1 = 2,050. What will be the demand forecast for period t?

A. B. C. D. E.

2,000 2,095 1,980 2,050 1,875

The forecast for will be (.2 * 2,200) + (.3 * 1,950) + (.5 * 2,050) = 2,050.

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Learning Objective: 03-09 Prepare a weighted-average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

50.

When choosing a forecasting technique, a critical trade-off that must be considered is that between:

A. B. C. D. E.

time series and associative. seasonality and cyclicality. length and duration. simplicity and complexity. cost and accuracy.

The trade-off between cost and accuracy is the critical consideration when choosing a forecasting technique.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Level of Difficulty: 1 Easy Topic: Choosing a Forecasting Technique

3-66 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

51.

The more novel a new product or service design is, the more forecasters have to rely on:

A. B. C. D. E.

subjective estimates. seasonality. cyclicality. historical data. smoothed variation.

New products and services lack historical data, so forecasts for them must be based on subjective estimates.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Level of Difficulty: 1 Easy Topic: Choosing a Forecasting Technique

52.

Forecasts based on judgment and opinion do not include:

A. B. C. D. E.

executive opinion. salesperson opinion. second opinions. customer surveys. Delphi methods.

Second opinions generally refer to medical diagnoses, not demand forecasting.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-06 Describe four qualitative forecasting techniques. Level of Difficulty: 2 Medium Topic: Qualitative Forecasts

3-67 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

53.

Which of the following is/are a primary input into capacity, sales, and production planning?

A. B. C. D. E.

product design market share ethics globalization demand forecasts

Demand forecasts are direct inputs into capacity, sales, and production plans.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-01 List features common to all forecasts. Level of Difficulty: 1 Easy Topic: Features Common to All Forecasts

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-01 List features common to all forecasts. Level of Difficulty: 3 Hard Topic: Features Common to All Forecasts

3-68 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

55.

Which of the following is not a step in the forecasting process?

A. B. C. D. E.

Determine the purpose and level of detail required. Eliminate all assumptions. Establish a time horizon. Select a forecasting model. Monitor the forecast.

We cannot eliminate all assumptions.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-04 Outline the steps in the forecasting process. Level of Difficulty: 2 Medium Topic: Features Common to All Forecasts

56.

Minimizing the sum of the squared deviations around the line is called:

A. B. C. D. E.

mean squared error technique. mean absolute deviation. double smoothing. least squares estimation. predictor regression.

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

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 2 Medium Topic: Associative Forecasting Techniques

57.

The two general approaches to forecasting are:

A. B. C. D. E.

mathematical and statistical. qualitative and quantitative. judgmental and qualitative. historical and associative. precise and approximation.

Forecast approaches are either quantitative or qualitative.

AACSB: Reflective Thinking 3-69 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Level of Difficulty: 1 Easy Topic: Approaches to Forecasting

58.

Which of the following is not a type of judgmental forecasting?

A. B. C. D. E.

executive opinions sales force opinions consumer surveys the Delphi method time series analysis

Time series analysis is a quantitative approach.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-06 Describe four qualitative forecasting techniques. Level of Difficulty: 1 Easy Topic: Qualitative Forecasts

59.

Accuracy in forecasting can be measured by:

A. B. C. D. E.

MSE. MRP. MPS. MTM. MTE.

MSE is mean squared error.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 3 Hard Topic: Forecast Accuracy

3-70 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-06 Describe four qualitative forecasting techniques. Level of Difficulty: 3 Hard Topic: Qualitative Forecasts

61.

Which phrase most closely describes the Delphi technique?

A. B. C. D. E.

associative forecast consumer survey series of questionnaires developed in India historical data

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

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-06 Describe four qualitative forecasting techniques. Level of Difficulty: 1 Easy Topic: Qualitative Forecasts

3-71 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

62.

The forecasting method which uses anonymous questionnaires to achieve a consensus forecast is:

A. B. C. D. E.

sales force opinions. consumer surveys. the Delphi method. time series analysis. executive opinions.

Anonymity is important in Delphi efforts.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-06 Describe four qualitative forecasting techniques. Level of Difficulty: 1 Easy Topic: Qualitative Forecasts

63.

One reason for using the Delphi method in forecasting is to:

A. B. C. D. E.

avoid premature consensus (bandwagon effect). achieve a high degree of accuracy. maintain accountability and responsibility. be able to replicate results. prevent hurt feelings.

A bandwagon can lead to popular but potentially inaccurate viewpoints to drown out other important considerations.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-06 Describe four qualitative forecasting techniques. Level of Difficulty: 2 Medium Topic: Qualitative Forecasts

3-72 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

64.

Detecting nonrandomness in errors can be done using:

A. B. C. D. E.

MSEs. MAPs. control charts. correlation coefficients. strategies.

Control charts graphically depict the statistical behavior of forecast errors.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Level of Difficulty: 2 Medium Topic: Approaches to Forecasting

65.

Gradual, long-term movement in time series data is called:

A. B. C. D. E.

seasonal variation. cycles. irregular variation. trend. random variation.

Trends move the time series in a long-term direction.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

66.

The primary difference between seasonality and cycles is:

A. B. C. D. E.

the duration of the repeating patterns. the magnitude of the variation. the ability to attribute the pattern to a cause. the direction of the movement. there are only four seasons but 30 cycles.

Seasons happen within time periods; cycles happen across multiple time periods.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation 3-73 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

Blooms: Remember Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

67.

Averaging techniques are useful for:

A. B. C. D. E.

distinguishing between random and nonrandom variations. smoothing out fluctuations in time series. eliminating historical data. providing accuracy in forecasts. average people.

Smoothing helps forecasters see past random error.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

68.

Putting forecast errors into perspective is best done using

A. B. C. D. E.

exponential smoothing. MAPE. linear decision rules. MAD. hindsight.

MAPE depicts the forecast error relative to what was being forecast.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 2 Medium Topic: Monitoring the Forecast Error

3-74 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

69.

Using the latest observation in a sequence of data to forecast the next period is:

A. B. C. D. E.

a moving average forecast. a naive forecast. an exponentially smoothed forecast. an associative forecast. regression analysis.

Only one piece of information is needed for a naive forecast.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

70.

For the data given below, what would the naive forecast be for period 5?

A. B. C. D. E.

58 62 59.5 61 cannot tell from the data given

Period 5's forecast would be period 4's demand.

AACSB: Analytic Blooms: Apply Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-75 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

71.

Moving average forecasting techniques do the following:

A. B. C. D. E.

Immediately reflect changing patterns in the data. Lead changes in the data. Smooth variations in the data. Operate independently of recent data. Assist when organizations are relocating.

Variation is smoothed out in moving average forecasts.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

72.

Which is not a characteristic of simple moving averages applied to time series data?

A. B. C. D. E.

smoothes random variations in the data weights each historical value equally lags changes in the data requires only last period's forecast and actual data smoothes real variations in the data

Simple moving averages can require several periods of data.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-76 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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. B. C. D. E.

decreased. increased. multiplied by a larger alpha. multiplied by a smaller alpha. eliminated if the MAD is greater than the MSE.

Fewer data points result in more responsive moving averages.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

74.

A forecast based on the previous forecast plus a percentage of the forecast error is:

A. B. C. D. E.

a naive forecast. a simple moving average forecast. a centered moving average forecast. an exponentially smoothed forecast. an associative forecast.

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

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-77 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

75.

Which is not a characteristic of exponential smoothing?

A. B. C. D. E.

smoothes random variations in the data weights each historical value equally has an easily altered weighting scheme has minimal data storage requirements smoothes real variations in the data

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

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

76.

Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast?

A. B. C. D. E.

0 .01 .1 .5 1.0

An alpha of 1.0 leads to a naive forecast.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-78 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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. B. C. D. E.

.01. .10. .15. .20. .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.

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

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. B. C. D. E.

36.9 57.5 60.5 62.5 65.5

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

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-79 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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. B. C. D. E.

80.8. 93.8. 100.2. 101.8. 108.2.

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

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

80.

Which of the following possible values of alpha would cause exponential smoothing to respond the most quickly to forecast errors?

A. B. C. D. E.

0 .01 .05 .10 .15

Larger values for alpha correspond with greater responsiveness.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-80 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

81.

A manager uses the following equation to predict monthly receipts: Y t = 40,000 + 150t. What is the forecast for July if t = 0 in April of this year?

A. B. C. D. E.

40,450 40,600 42,100 42,250 42,400

July would be period 3, so the forecast would be 40,000 + 150(3).

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

82.

In trend-adjusted exponential smoothing, the trend-adjusted forecast consists of:

A. an exponentially smoothed forecast and a smoothed trend factor. B. an exponentially smoothed forecast and an estimated trend value. C. the old forecast adjusted by a trend factor. D. the old forecast and a smoothed trend factor. E. a moving average and a trend factor. Both random variation and the trend are smoothed in TAF models.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-81 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

83.

In the additive model for seasonality, seasonality is expressed as a ______________ adjustment to the average; in the multiplicative model, seasonality is expressed as a __________ adjustment to the average.

A. B. C. D. E.

quantity; percentage percentage; quantity quantity; quantity percentage; percentage qualitative; quantitative

The additive model simply adds a seasonal adjustment to the deseasonalized forecast. The multiplicative model adjusts the deseasonalized forecast by multiplying it by a season relative or index.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

84.

Which technique is used in computing seasonal relatives?

A. B. C. D. E.

double smoothing Delphi mean squared error centered moving average exponential smoothing

The centered moving average serves as the basis point for computing seasonal relatives.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-82 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

85.

A persistent tendency for forecasts to be greater than or less than the actual values is called:

A. B. C. D. E.

bias. tracking. control charting. positive correlation. linear regression.

Bias is a tendency for a forecast to be above (or below) the actual value.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 2 Medium Topic: Forecast Accuracy

86.

Which of the following might be used to indicate the cyclical component of a forecast?

A. B. C. D. E.

leading variable mean squared error Delphi technique exponential smoothing mean absolute deviation

Leading variables, such as births in a given year, can correlate strongly with longterm phenomena such as cycles.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-83 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

87.

The primary method for associative forecasting is:

A. B. C. D. E.

sensitivity analysis. regression analysis. simple moving averages. centered moving averages. exponential smoothing.

Regression analysis is an associative forecasting technique.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 2 Medium Topic: Associative Forecasting Techniques

88.

Which term most closely relates to associative forecasting techniques?

A. B. C. D. E.

time series data expert opinions Delphi technique consumer survey predictor variables

Associative techniques use predictor variables.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 2 Medium Topic: Associative Forecasting Techniques

3-84 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

89.

Which of the following corresponds to the predictor variable in simple linear regression?

A. B. C. D. E.

regression coefficient dependent variable independent variable predicted variable demand coefficient

Demand is the typical dependent variable when forecasting with simple linear regression.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 2 Medium Topic: Associative Forecasting Techniques

90.

The mean absolute deviation is used to:

A. B. C. D. E.

estimate the trend line. eliminate forecast errors. measure forecast accuracy. seasonally adjust the forecast. compute periodic forecast errors.

MAD is one way of evaluating forecast performance.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 2 Medium Topic: Forecast Accuracy

3-85 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

91.

Given forecast errors of 4, 8, and -3, what is the mean absolute deviation?

A. B. C. D. E.

4 3 5 6 12

Convert each error into an absolute value and then average.

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 1 Easy Topic: Forecast Accuracy

92.

Given forecast errors of 5, 0, -4, and 3, what is the mean absolute deviation?

A. B. C. D. E.

4 3 2.5 2 1

Convert each error into an absolute value and then average.

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 1 Easy Topic: Forecast Accuracy

93.

Given forecast errors of 5, 0, -4, and 3, what is the bias?

A. B. C. D. E.

-4 4 5 12 6

Sum the forecast errors.

AACSB: Analytic Accessibility: Keyboard Navigation 3-86 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

Blooms: Apply Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 2 Medium Topic: Forecast Accuracy

94.

Which of the following is used for constructing a control chart?

A. B. C. D.

mean absolute deviation mean squared error tracking signal bias

The mean squared error leads to an estimate for the sample forecast standard deviation.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Level of Difficulty: 2 Medium Topic: Approaches to Forecasting

95.

The two most important factors in choosing a forecasting technique are:

A. B. C. D. E.

cost and time horizon. accuracy and time horizon. cost and accuracy. quantity and quality. objective and subjective components.

More accurate forecasts cost more but may not be worth the additional cost.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Level of Difficulty: 2 Medium Topic: Choosing a Forecasting Technique

3-87 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

96.

The degree of management involvement in short-range forecasts is:

A. B. C. D. E.

none. low. moderate. high. total.

Short-range forecasting tends to be fairly routine.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-01 List features common to all forecasts. Level of Difficulty: 1 Easy Topic: Choosing a Forecasting Technique

97.

Which of the following is not necessarily an element of a good forecast?

A. B. C. D. E.

estimate of accuracy timeliness meaningful units low cost written

A good forecast can be quite costly if necessary.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-03 List the elements of a good forecast. Level of Difficulty: 2 Medium Topic: Elements of a Good Forecast

3-88 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

98.

Forecasting techniques generally assume:

A. the absence of randomness. B. continuity of some underlying causal system. C. a linear relationship between time and demand. D. accuracy that increases the farther out in time the forecast projects. E. accuracy that is better when individual items, rather than groups of items, are being considered. Forecasting techniques generally assume that the same underlying causal system that existed in the past will continue to exist in the future.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-01 List features common to all forecasts. Level of Difficulty: 1 Easy Topic: Forecast Accuracy

99.

A managerial approach toward forecasting which seeks to actively influence demand is:

A. B. C. D. E.

reactive. proactive. influential. protracted. retroactive.

Simply responding to demand is a reactive approach.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Level of Difficulty: 1 Easy Topic: Using Forecast Information

3-89 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

100. Customer service levels can be improved by better:

A. B. C. D. E.

mission statements. control charting. short-term forecast accuracy. exponential smoothing. customer selection.

More accurate short-term forecasts enable organizations to better accommodate customer requests.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-03 List the elements of a good forecast. Level of Difficulty: 3 Hard Topic: Operations Strategy

101. Given the following historical data, what is the simple three-period moving average forecast for period 6?

A. B. C. D. E.

67 115 69 68 68.67

Average demand from periods 3 through 5.

AACSB: Analytic Blooms: Apply Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-90 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

102. Given the following historical data and weights of .5, .3, and .2, what is the threeperiod moving average forecast for period 5?

A. B. C. D. E.

144.20 144.80 144.67 143.00 144.00

Multiply period 4 (144) by .5, period 3 (148) by .3, and period 2 (142) by .2, then sum these products.

AACSB: Analytic Blooms: Apply Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

103. Use of simple linear regression analysis assumes that:

A. variations around the line are nonrandom. B. deviations around the line are normally distributed. C. predictions can easily be made beyond the range of observed values of the predictor variable. D. all possible predictor variables are included in the model. E. the variance of error terms (deviations) varies directly with the predictor variable. That deviations conform to the normal distribution is a very important assumption underpinning simple linear regression.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 3 Hard Topic: Associative Forecasting Techniques

3-91 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

104. Given forecast errors of -5, -10, and +15, the MAD is:

A. B. C. D. E.

0. 10. 30. 175. 225.

Convert these errors into absolute value, then average.

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 1 Easy Topic: Forecast Accuracy

105. The president of State University wants to forecast student enrollments for this academic year based on the following historical data:

What is the forecast for this year using the naive approach?

A. B. C. D. E.

18,750 19,500 21,000 22,000 22,800

This year's forecast would be last year's enrollment.

AACSB: Analytic Blooms: Apply Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-92 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

106. The president of State University wants to forecast student enrollments for this academic year based on the following historical data:

What is the forecast for this year using a four-year simple moving average?

A. B. C. D. E.

18,750 19,500 21,000 22,650 22,800

Average enrollment from the last four years.

AACSB: Analytic Blooms: Apply Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-93 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

107. The president of State University wants to forecast student enrollments for this academic year based on the following historical data:

What is the forecast for this year using exponential smoothing with alpha = .5, if the forecast for two years ago was 16,000?

A. B. C. D. E.

18,750 19,500 21,000 22,650 22,800

Multiply last year's forecast error by the smoothing constant, then add that adjusted error to last year's forecast to get this year's forecast.

AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-94 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

108. The president of State University wants to forecast student enrollments for this academic year based on the following historical data:

What is the forecast for this year using the least squares trend line for these data?

A. B. C. D. E.

18,750 19,500 21,000 22,650 22,800

Treat 5 years ago as period 0.

AACSB: Analytic Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-95 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

109. The president of State University wants to forecast student enrollments for this academic year based on the following historical data:

What is the forecast for this year using trend-adjusted (double) smoothing with alpha = .05 and beta = .3, if the forecast for last year was 21,000, the forecast for two years ago was 19,000, and the trend estimate for last year's forecast was 1,500?

A. B. C. D. E.

18,750 19,500 21,000 22,650 22,800

Smooth both the trend and the forecast to get this year's forecast.

AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-96 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

110. The business analyst for Video Sales, Inc. wants to forecast this year's demand for DVD decoders based on the following historical data:

What is the forecast for this year using the naive approach?

A. B. C. D. E.

163 180 300 420 510

This year's forecast is last year's demand.

AACSB: Analytic Blooms: Apply Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-97 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

111. The business analyst for Video Sales, Inc. wants to forecast this year's demand for DVD decoders based on the following historical data:

What is the forecast for this year using a three-year weighted moving average with weights of .5, .3, and .2?

A. B. C. D. E.

163 180 300 420 510

Multiply the last three periods of demand by the appropriate weights, then sum the resulting products.

AACSB: Analytic Blooms: Apply Learning Objective: 03-09 Prepare a weighted-average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-98 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

112. The business analyst for Video Sales, Inc. wants to forecast this year's demand for DVD decoders based on the following historical data:

What is the forecast for this year using exponential smoothing with alpha = .4, if the forecast for two years ago was 750?

A. B. C. D. E.

163 180 300 420 510

First formulate last year's exponentially smoothed forecast, then proceed.

AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-99 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

113. The business analyst for Video Sales, Inc. wants to forecast this year's demand for DVD decoders based on the following historical data:

What is the forecast for this year using the least squares trend line for these data?

A. B. C. D. E.

163 180 300 420 510

Treat the earliest period of demand as period 0, then formulate least squares estimates and proceed.

AACSB: Analytic Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-100 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

114. The business analyst for Video Sales, Inc. wants to forecast this year's demand for DVD decoders based on the following historical data:

What is the forecast for this year using trend-adjusted (double) smoothing with alpha = .3 and beta = .2, if the forecast for last year was 310, the forecast for two years ago was 430, and the trend estimate for last year's forecast was -150?

A. B. C. D. E.

162.4 180.3 301.4 403.2 510.0

Smooth both the trend and the forecast to get this year's forecast.

AACSB: Analytic Blooms: Apply Learning Objective: 03-12 Prepare a trend-adjusted exponential smoothing forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-101 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

115. Professor Very Busy needs to allocate time next week to include time for office hours. He needs to forecast the number of students who will seek appointments. He has gathered the following data:

What is this week's forecast using the naive approach?

A. B. C. D. E.

45 50 52 65 78

This week's forecast is last week's demand.

AACSB: Analytic Blooms: Apply Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-102 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

116. Professor Very Busy needs to allocate time next week to include time for office hours. He needs to forecast the number of students who will seek appointments. He has gathered the following data:

What is this week's forecast using a three-week simple moving average?

A. B. C. D. E.

49 50 52 65 78

Average the three most recent weeks of demand.

AACSB: Analytic Blooms: Apply Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-103 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

117. Professor Very Busy needs to allocate time next week to include time for office hours. He needs to forecast the number of students who will seek appointments. He has gathered the following data:

What is this week's forecast using exponential smoothing with alpha = .2, if the forecast for two weeks ago was 90?

A. B. C. D. E.

49 50 52 65 77

Formulate the forecast for last week, then use that to get this week's forecast.

AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-104 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

118. Professor Very Busy needs to allocate time next week to include time for office hours. He needs to forecast the number of students who will seek appointments. He has gathered the following data:

What is this week's forecast using the least squares trend line for these data?

A. B. C. D. E.

49 50 52 65 78

Treat the earliest period as period 0, then formulate least squares coefficients and proceed.

AACSB: Analytic Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-105 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

119. Professor Very Busy needs to allocate time next week to include time for office hours. He needs to forecast the number of students who will seek appointments. He has gathered the following data:

What is this week's forecast using trend-adjusted (double) smoothing with alpha = . 5 and beta = .1, if the forecast for last week was 65, the forecast for two weeks ago was 75, and the trend estimate for last week's forecast was -5?

A. B. C. D. E.

49.3 50.6 51.3 65.4 78.7

Smooth both the trend and the forecast to get this year's forecast.

AACSB: Analytic Blooms: Apply Learning Objective: 03-12 Prepare a trend-adjusted exponential smoothing forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-106 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

120. A concert promoter is forecasting this year's attendance for one of his concerts based on the following historical data:

What is this year's forecast using the naive approach?

A. B. C. D. E.

22,000 20,000 18,000 15,000 12,000

This year's forecast is last year's attendance.

AACSB: Analytic Blooms: Apply Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-107 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

121. A concert promoter is forecasting this year's attendance for one of his concerts based on the following historical data:

What is this year's forecast using a two-year weighted moving average with weights of .7 and .3?

A. B. C. D. E.

19,400 18,600 19,000 11,400 10,600

Multiply the two most recent periods by the appropriate weights, then sum the resulting products.

AACSB: Analytic Blooms: Apply Learning Objective: 03-09 Prepare a weighted-average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-108 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

122. A concert promoter is forecasting this year's attendance for one of his concerts based on the following historical data:

What is this year's forecast using exponential smoothing with alpha = .2, if last year's smoothed forecast was 15,000?

A. B. C. D. E.

20,000 19,000 17,500 16,000 15,000

Multiply last year's forecast error by the smoothing constant, then add that product to last year's forecast to get this year's forecast.

AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-109 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

123. A concert promoter is forecasting this year's attendance for one of his concerts based on the following historical data:

What is this year's forecast using the least squares trend line for these data?

A. B. C. D. E.

20,000 21,000 22,000 23,000 24,000

Treat the earliest year as period zero in formulating least squares coefficients.

AACSB: Analytic Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-110 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

124. A concert promoter is forecasting this year's attendance for one of his concerts based on the following historical data:

The previous trend line had predicted 18,500 for two years ago, and 19,700 for last year. What was the mean absolute deviation for these forecasts?

A. B. C. D. E.

100 200 400 500 800

Convert each period's forecast error into absolute value, then average.

AACSB: Analytic Blooms: Apply Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 2 Medium Topic: Forecast Accuracy

3-111 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

125. The dean of a school of business is forecasting total student enrollment for this year's summer session classes based on the following historical data:

What is this year's forecast using the naive approach?

A. B. C. D. E.

2,000 2,200 2,800 3,000 4,300

This year's forecast would be last year's enrollment.

AACSB: Analytic Blooms: Apply Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-112 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

126. The dean of a school of business is forecasting total student enrollment for this year's summer session classes based on the following historical data:

What is this year's forecast using a three-year simple moving average?

A. B. C. D. E.

2,667 2,600 2,500 2,400 2,333

Average the most recent periods of enrollment.

AACSB: Analytic Blooms: Apply Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-113 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

127. The dean of a school of business is forecasting total student enrollment for this year's summer session classes based on the following historical data:

What is this year's forecast using exponential smoothing with alpha = .4, if last year's smoothed forecast was 2,600?

A. B. C. D. E.

2,600 2,760 2,800 3,840 3,000

Multiply last year's forecast error by the smoothing constant. Add the product to last year's forecast to get this year's forecast.

AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-114 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

128. The dean of a school of business is forecasting total student enrollment for this year's summer session classes based on the following historical data:

What is the annual rate of change (slope) of the least squares trend line for these data?

A. B. C. D. E.

0 200 400 180 360

Treat the earliest period as period 0, then formulate the least squares slope.

AACSB: Analytic Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-115 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

129. The dean of a school of business is forecasting total student enrollment for this year's summer session classes based on the following historical data:

What is this year's forecast using the least squares trend line for these data?

A. B. C. D. E.

3,600 3,500 3,400 3,300 3,200

Treat the earliest period as period 0, then formulate the least squares coefficients and proceed.

AACSB: Analytic Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-116 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

130. The owner of Darkest Tans Unlimited in a local mall is forecasting this month's (October's) demand for the one new tanning booth based on the following historical data:

What is this month's forecast using the naive approach?

A. B. C. D. E.

100 160 130 140 120

This month's forecast is last month's demand.

AACSB: Analytic Blooms: Apply Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-117 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

131. The owner of Darkest Tans Unlimited in a local mall is forecasting this month's (October's) demand for the one new tanning booth based on the following historical data:

What is this month's forecast using a four-month weighted moving average with weights of .4, .3, .2, and .1?

A. B. C. D. E.

120 129 141 135 140

Multiply the four most recent periods of demand by the appropriate weights, then sum the resulting products.

AACSB: Analytic Blooms: Apply Learning Objective: 03-09 Prepare a weighted-average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-118 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

132. The owner of Darkest Tans Unlimited in a local mall is forecasting this month's (October's) demand for the one new tanning booth based on the following historical data:

What is this month's forecast using exponential smoothing with alpha = .2, if August's forecast was 145?

A. B. C. D. E.

144 140 142 148 163

First calculate September's forecast, then use that to calculate this month's forecast.

AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-119 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

133. The owner of Darkest Tans Unlimited in a local mall is forecasting this month's (October's) demand for the one new tanning booth based on the following historical data:

What is the monthly rate of change (slope) of the least squares trend line for these data?

A. B. C. D. E.

320 102 8 -.4 -8

Treat the earliest period as period 0, then formulate the least squares slope.

AACSB: Analytic Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-120 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

134. The owner of Darkest Tans Unlimited in a local mall is forecasting this month's (October's) demand for the one new tanning booth based on the following historical data:

What is this month's forecast using the least squares trend line for these data?

A. B. C. D. E.

1,250 128.6 102 158 164

Treat the earliest period as period 0, then formulate the least squares coefficients and proceed.

AACSB: Analytic Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

135. Which of the following mechanisms for enhancing profitability is most likely to result from improving short-term forecast performance?

A. B. C. D. E.

increased inventory reduced flexibility higher-quality products greater customer satisfaction greater seasonality

Short-term forecast performance won't necessarily improve product quality, but it does allow firms to better satisfy their customers.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting 3-121 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

technique. Level of Difficulty: 2 Medium Topic: Operations Strategy

136. Which of the following changes would tend to shorten the time frame for short-term forecasting?

A. B. C. D. E.

bringing greater variety into the product mix increasing the flexibility of the production system ordering fewer weather-sensitive items adding more special-purpose equipment investing in the production system to make it more task-specific

An increasingly flexible system permits more rapid responses to changing conditions, which allows firms to reduce their forecast time horizon.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Level of Difficulty: 2 Medium Topic: Operations Strategy

137. Which of the following helps improve supply chain forecasting performance?

A. contracts that require supply chain members to formulate long-term forecasts B. penalties for supply chain members that adjust forecasts C. sharing forecasts or demand data across the supply chain D. increasing lead times for critical supply chain members E. increasing the number of suppliers at critical junctures in the supply chain Sharing forecasts and/or demand data is a means of ensuring that the supply chain's overall forecast is as accurate as it can be.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-03 List the elements of a good forecast. Level of Difficulty: 1 Easy Topic: Forecasting and the Supply Chain

3-122 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

138. Which of the following would tend to decrease forecast accuracy?

A. a reduction in demand variability B. a shortening of the forecast time horizon C. an attempt to forecast demand for a group of similar items rather than an individual item D. a change in the underlying causal system Forecasting techniques generally assume that the same underlying causal system that existed in the past will continue to exist in the future.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-02 Explain why forecasts are generally wrong. Level of Difficulty: 2 Medium Topic: Forecasting and the Supply Chain

139. Which of the following is the most valuable piece of information the sales force can bring into forecasting situations?

A. what customers are most likely to do in the future B. what customers most want to do in the future C. what customers' future plans are D. whether customers are satisfied or dissatisfied with their performance in the past E. what the salesperson's appropriate sales quota should be Knowledge about what customers are likely to do is much more valuable than information regarding what customers plan or want to do.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-06 Describe four qualitative forecasting techniques. Level of Difficulty: 2 Medium Topic: Qualitative Forecasts

Essay Questions

3-123 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

140. Develop a forecast for the next period, given the data below, using a three-period moving average.

Feedback: Average demand from periods 3 through 5.

AACSB: Analytic Blooms: Apply Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-124 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

141. Consider the data below:

Using exponential smoothing with alpha = .2, and assuming the forecast for period 11 was 80, what would the forecast for period 14 be?

Feedback: The forecast error in period 13 (2.84) is multiplied by the smoothing constant. This is then added to the period 13 forecast to get the period 14 forecast.

AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

142. A manager is using exponential smoothing to predict merchandise returns at a suburban branch of a department store chain. Given a previous forecast of 140 items, an actual number of returns of 148 items, and a smoothing constant equal to .15, what is the forecast for the next period?

Feedback: The forecast error in the previous period is multiplied by the smoothing constant. This is then added to the previous period's forecast to get the upcoming period's forecast.

AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data 3-125 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

143. A manager is using the equation below to forecast quarterly demand for a product: Yt = 6,000 + 80t where t = 0 at Q2 of last year Quarter relatives are Q1 = .6, Q2 = .9, Q3 = 1.3, and Q4 = 1.2. What forecasts are appropriate for the last quarter of this year and the first quarter of next year?

For Q4 of this year t = 6 For Q1 of next year t = 7

Feedback: Adjust deseasonalized forecasts by the quarterly seasonal relatives.

AACSB: Analytic Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

144. Over the past five years, a firm's sales have averaged 250 units in the first quarter of each year, 100 units in the second quarter, 150 units in the third quarter, and 300 units in the fourth quarter. What are appropriate quarter relatives for this firm's sales? Hint: Only minimal computations are necessary.

Feedback: Since a trend is not present, quarter relatives are simply a percentage of average, which is 200 units.

AACSB: Analytic Blooms: Apply Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-126 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

145. A manager has been using a certain technique to forecast demand for gallons of ice cream for the past six periods. Actual and predicted amounts are shown below. Would a naive forecast have produced better results?

Current method: MAD = 3.67; MSE = 16.8; 2s control limits ± 8.2 (OK) Naive method: MAD = 4.40; MSE = 30.0; 2s control limits ± 11.0 (OK) Feedback: Either MSE or MAD should be computed for both forecasts and compared. The demand data are stable. Therefore, the most recent value of the series is a reasonable forecast for the next period of time, justifying the naive approach. The current method is slightly superior both in terms of MAD and MSE. Either method would be considered in control.

AACSB: Analytic Blooms: Apply Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 3 Hard Topic: Forecast Accuracy

3-127 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

146. A new car dealer has been using exponential smoothing with an alpha of .2 to forecast weekly new car sales. Given the data below, would a naive forecast have provided greater accuracy? Explain. Assume an initial exponential forecast of 60 units in period 2 (i.e., no forecast for period 1).

Exponential method: MAD = 1.70; MSE = 6.34 Naive method: MAD = 3.00; MSE = 15.25 Feedback: The exponential forecast method appears to be superior because both MAD and MSE are lower when it is used.

AACSB: Analytic Blooms: Apply Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 3 Hard Topic: Forecast Accuracy

3-128 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

147. A CPA firm has been using the following equation to predict annual demand for tax audits: Yt = 55 + 4t. Demand for the past few years is shown below. Is the forecast performing as well as it might? Explain.

MSE = 11/6 and s = = 3.41. Even with ± 2s limits (6.82), all values are within the limits. It seems, then, that only random variation is present, so one might say that the forecast is working. One might also observe that the first three errors are negative and the last three are positive. Although six observations constitute a relatively small sample, it may be that the errors are cycling, and this would be a matter to investigate with additional data. Feedback: Either a tracking signal or a control chart is called for. To conduct these assessments, it is necessary to generate the forecasts so that errors can be determined.

AACSB: Analytic Blooms: Apply Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-129 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

148. Given the data below, develop a forecast for period 6 using a four-period weighted moving average and weights of .4, .3, .2 and .1.

.4(17) + .3(19) + .2(18) + .1(20) = 18.1 Feedback: Multiply demand observed in periods 2 through 5 by the appropriate weight, then sum these products.

AACSB: Analytic Blooms: Apply Learning Objective: 03-09 Prepare a weighted-average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-130 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

149. Use linear regression to develop a predictive model for demand for burial vaults based on sales of caskets.

Feedback: Least-squares estimation leads to this regression equation.

AACSB: Analytic Blooms: Apply Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 3 Hard Topic: Associative Forecasting Techniques

3-131 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

150. Given the following data, develop a linear regression model for y as a function of x.

Feedback: Least squares estimation leads to this regression equation.

AACSB: Analytic Blooms: Apply Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 2 Medium Topic: Associative Forecasting Techniques

3-132 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

151. Given the following data, develop a linear regression model for y as a function of x.

Feedback: Least squares estimation leads to this regression equation.

AACSB: Analytic Blooms: Apply Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 3 Hard Topic: Associative Forecasting Techniques

3-133 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

152. Develop a linear trend equation for the data on bread deliveries shown below. Forecast deliveries for period 11 through 14.

Yt = 518.2 + 52.164t r = +.935

Feedback: Formulate the regression equation using least squares estimation, then apply the result to periods 11 through 14.

AACSB: Analytic Blooms: Apply Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-134 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

153. Demand for the last four months was:

A) Predict demand for July using each of these methods: 1) a three-period moving average 2) exponential smoothing with alpha equal to .20 (use a naive forecast for April for your first forecast) B) If the naive approach had been used to predict demand for April through June, what would MAD have been for those months?

Feedback: The naive approach leads to absolute forecast errors of two units in each period.

AACSB: Analytic Blooms: Apply Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-135 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

154. A manager wants to choose one of two forecasting alternatives. Each alternative was tested using historical data. The resulting forecast errors for the two are shown in the table. Analyze the data and recommend a course of action to the manager.

Although Alternative 1 has the smaller MSE, it appears to be cycling and steady; Alternative 2 errors after the first three periods are small or zero. For the last six periods, Alternative 2 was much better, suggesting that approach would be better. Feedback: Although Alternative 1 has the smaller MSE, it appears to be cycling and steady; Alternative 2 errors after the first three periods are small or zero. For the last six periods, Alternative 2 was much better, suggesting that approach would be better.

AACSB: Analytic Blooms: Apply Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 3 Hard Topic: Forecast Accuracy

3-136 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

155. A manager uses this equation to predict demand: Yt = 20 + 4t. Over the past eight periods, demand has been as follows. Are the results acceptable? Explain.

s = 2.10; 2s control limits are ± 4.20. Although all values are within control limits, the errors may be exhibiting cyclical patterns, which would suggest nonrandomness. Feedback: s = 2.10; 2s control limits are ± 4.20. Although all values are within control limits, the errors may be exhibiting cyclical patterns, which would suggest nonrandomness.

AACSB: Analytic Blooms: Apply Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Level of Difficulty: 2 Medium Topic: Approaches to Forecasting

3-137 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

156. Data on demand over the last few years are available as follows:

What would this year's forecast be if we were using the naive approach?

49 Feedback: This year's forecast would be last year's demand.

AACSB: Analytic Blooms: Apply Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-138 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

157. Data on demand over the last few years are available as follows:

What is this year's forecast using a four-year simple moving average?

45.5 Feedback: Average the four most recent periods of demand.

AACSB: Analytic Blooms: Apply Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-139 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

158. Data on demand over the last few years are available as follows:

What is this year's forecast using exponential smoothing with alpha = .25, if last year's smoothed forecast was 45?

45.8 Feedback: Multiply last year's forecast error by the smoothing constant. Add the resulting product to last year's forecast to get this year's forecast.

AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-140 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

159. Data on demand over the last few years are available as follows:

What are this and next year's forecasts using the least squares trend line for these data?

62; 69 Feedback: Treat the earliest period as period 0 in formulating least squares coefficients, then proceed.

AACSB: Analytic Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-141 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

160. Data on demand over the last few years are available as follows:

What is this year's forecast using trend-adjusted (double) smoothing with alpha = . 2 and beta = .1, if the forecast for last year was 56, the forecast for two years ago was 46, and the trend estimate for last year's forecast was 7?

61.76 Feedback: Smooth both the trend and the forecasts using the appropriate smoothing coefficients.

AACSB: Analytic Blooms: Apply Learning Objective: 03-12 Prepare a trend-adjusted exponential smoothing forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-142 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

161. Data on the last three years of demand are available as follows:

What is the centered moving average for spring two years ago?

29 Feedback: First average the four periods beginning fall three years ago. Then average the four periods beginning spring two years ago. Then average these two averages.

AACSB: Analytic Blooms: Apply Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

162. Data on the last three years of demand are available as follows:

What is the spring's seasonal relative?

Spring = 0.91 Feedback: Divide data points by centered moving averages where moving averages are available. Average the resulting values across the seasons to get the seasonal relatives.

AACSB: Analytic Blooms: Apply Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data 3-143 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

163. Data on the last three years of demand are available as follows:

What is the linear regression trend line for these data (t = 0 for spring, three years ago)?

y = 17 + 2.33t Feedback: Used deseasonalized data points to formulate least squares coefficients.

AACSB: Analytic Blooms: Apply Learning Objective: 03-12 Prepare a trend-adjusted exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

164. Data on the last three years of demand are available as follows:

What is this year's seasonally adjusted forecast for each season?

Spring = 40.93; Summer = 29.81; Fall = 51.14; Winter = 74.37 Feedback: First forecast each period's deseasonalized value (e.g., Spring is period 12). Then multiply the deseasonalized forecast by the appropriate seasonal relative.

AACSB: Analytic Blooms: Apply Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data 3-144 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

3-145 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

View more...
True / False Questions

1. Forecasting techniques generally assume an existing causal system that will continue to exist in the future. True

False

2. For new products in a strong growth mode, a low alpha will minimize forecast errors when using exponential smoothing techniques. True

False

3. Once accepted by managers, forecasts should be held firm regardless of new input since many plans have been made using the original forecast. True

False

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. True

False

5. Forecasts help managers both to plan the system itself and to provide valuable information for using the system. True

False

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

False

7. When new products or services are introduced, focus forecasting models are an attractive option. True

False

3-1 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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

False

9. Forecasts based on time-series (historical) data are referred to as associative forecasts. True

False

10. Time-series techniques involve the identification of explanatory variables that can be used to predict future demand. True

False

11. A consumer survey is an easy and sure way to obtain accurate input from future customers since most people enjoy participating in surveys. True

False

12. The Delphi approach involves the use of a series of questionnaires to achieve a consensus forecast. True

False

13. Exponential smoothing adds a percentage (called alpha) of the last period's forecast to estimate the next period's demand. True

False

14. The shorter the forecast period, the more accurately the forecasts tend to track what actually happens. True

False

15. Forecasting techniques that are based on time-series data assume that future values of the series will duplicate past values. True

False

16. Trend-adjusted exponential smoothing uses double smoothing to add twice the forecast error to last period's actual demand. True

False

17. Forecasts based on an average tend to exhibit less variability than the original data. True

False

3-2 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

18. The naive approach to forecasting requires a linear trend line. True

False

19. The naive forecast is limited in its application to series that reflect no trend or seasonality. True

False

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

False

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. True

False

22. In order to update a moving average forecast, the values of each data point in the average must be known. True

False

23. Forecasts of future demand are used by operations people to plan capacity. True

False

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

False

25. Exponential smoothing is a form of weighted averaging. True

False

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. True

False

27. The T in the model TAF = S + T represents the time dimension (which is usually expressed in weeks or months). True

False

28. Trend-adjusted exponential smoothing requires selection of two smoothing constants. True

False

3-3 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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

False

30. A seasonal relative (or seasonal indexes) is expressed as a percentage of average or trend. True

False

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 cannot be used. True

False

32. Removing the seasonal component from a data series (deseasonalizing) can be accomplished by dividing each data point by its appropriate seasonal relative. True

False

33. If a pattern appears when a dependent variable is plotted against time, one should use time series analysis instead of regression analysis. True

False

34. Curvilinear and multiple regression procedures permit us to extend associative models to relationships that are nonlinear or involve more than one predictor variable. True

False

35. The sample standard deviation of forecast error is equal to the square root of MSE. True

False

36. Correlation measures the strength and direction of a relationship between variables. True

False

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. True

False

38. In exponential smoothing, an alpha of 1.0 will generate the same forecast that a naive forecast would yield. True

False

3-4 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

39. A forecast method is generally deemed to perform adequately when the errors exhibit an identifiable pattern. True

False

40. A control chart involves setting action limits for cumulative forecast error. True

False

41. A tracking signal focuses on the ratio of cumulative forecast error to the corresponding value of MAD. True

False

42. The use of a control chart assumes that errors are normally distributed about a mean of zero. True

False

43. Bias exists when forecasts tend to be greater or less than the actual values of time series. True

False

44. Bias is measured by the cumulative sum of forecast errors. True

False

45. Seasonal relatives can be used to deseasonalize data or incorporate seasonality in a forecast. True

False

46. The best forecast is not necessarily the most accurate. True

False

Multiple Choice Questions

3-5 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

47. 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. 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: A t-4 = 380, At-3 = 410, At-2 = 390, At-1 = 400. What will be the demand forecast for period t?

A. B. C. D. E.

402 397 399 393 403

49. 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. B. C. D. E.

2,000 2,095 1,980 2,050 1,875

50. When choosing a forecasting technique, a critical trade-off that must be considered is that between:

A. B. C. D. E.

time series and associative. seasonality and cyclicality. length and duration. simplicity and complexity. cost and accuracy.

3-6 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

51. The more novel a new product or service design is, the more forecasters have to rely on:

A. B. C. D. E.

subjective estimates. seasonality. cyclicality. historical data. smoothed variation.

52. Forecasts based on judgment and opinion do not include:

A. B. C. D. E.

executive opinion. salesperson opinion. second opinions. customer surveys. Delphi methods.

53. Which of the following is/are a primary input into capacity, sales, and production planning?

A. B. C. D. E.

product design market share ethics globalization demand forecasts

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. 55. Which of the following is not a step in the forecasting process?

A. B. C. D. E.

Determine the purpose and level of detail required. Eliminate all assumptions. Establish a time horizon. Select a forecasting model. Monitor the forecast.

3-7 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

56. Minimizing the sum of the squared deviations around the line is called:

A. B. C. D. E.

mean squared error technique. mean absolute deviation. double smoothing. least squares estimation. predictor regression.

57. The two general approaches to forecasting are:

A. B. C. D. E.

mathematical and statistical. qualitative and quantitative. judgmental and qualitative. historical and associative. precise and approximation.

58. Which of the following is not a type of judgmental forecasting?

A. B. C. D. E.

executive opinions sales force opinions consumer surveys the Delphi method time series analysis

59. Accuracy in forecasting can be measured by:

A. B. C. D. E.

MSE. MRP. MPS. MTM. MTE.

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.

3-8 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

61. Which phrase most closely describes the Delphi technique?

A. B. C. D. E.

associative forecast consumer survey series of questionnaires developed in India historical data

62. The forecasting method which uses anonymous questionnaires to achieve a consensus forecast is:

A. B. C. D. E.

sales force opinions. consumer surveys. the Delphi method. time series analysis. executive opinions.

63. One reason for using the Delphi method in forecasting is to:

A. B. C. D. E.

avoid premature consensus (bandwagon effect). achieve a high degree of accuracy. maintain accountability and responsibility. be able to replicate results. prevent hurt feelings.

64. Detecting nonrandomness in errors can be done using:

A. B. C. D. E.

MSEs. MAPs. control charts. correlation coefficients. strategies.

65. Gradual, long-term movement in time series data is called:

A. B. C. D. E.

seasonal variation. cycles. irregular variation. trend. random variation.

3-9 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

66. The primary difference between seasonality and cycles is:

A. B. C. D. E.

the duration of the repeating patterns. the magnitude of the variation. the ability to attribute the pattern to a cause. the direction of the movement. there are only four seasons but 30 cycles.

67. 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. 68. Putting forecast errors into perspective is best done using

A. B. C. D. E.

exponential smoothing. MAPE. linear decision rules. MAD. hindsight.

69. Using the latest observation in a sequence of data to forecast the next period is:

A. B. C. D. E.

a moving average forecast. a naive forecast. an exponentially smoothed forecast. an associative forecast. regression analysis.

3-10 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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

A. B. C. D. E.

58 62 59.5 61 cannot tell from the data given

71. Moving average forecasting techniques do the following:

A. B. C. D. E.

Immediately reflect changing patterns in the data. Lead changes in the data. Smooth variations in the data. Operate independently of recent data. Assist when organizations are relocating.

72. Which is not a characteristic of simple moving averages applied to time series data?

A. B. C. D. E.

smoothes random variations in the data weights each historical value equally lags changes in the data requires only last period's forecast and actual data smoothes real variations in the 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. B. C. D. E.

decreased. increased. multiplied by a larger alpha. multiplied by a smaller alpha. eliminated if the MAD is greater than the MSE.

3-11 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

74. A forecast based on the previous forecast plus a percentage of the forecast error is:

A. B. C. D. E.

a naive forecast. a simple moving average forecast. a centered moving average forecast. an exponentially smoothed forecast. an associative forecast.

75. Which is not a characteristic of exponential smoothing?

A. B. C. D. E.

smoothes random variations in the data weights each historical value equally has an easily altered weighting scheme has minimal data storage requirements smoothes real variations in the data

76. Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast?

A. B. C. D. E.

0 .01 .1 .5 1.0

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. B. C. D. E.

.01. .10. .15. .20. .60.

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. B. C. D. E.

36.9 57.5 60.5 62.5 65.5

3-12 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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. B. C. D. E.

80.8. 93.8. 100.2. 101.8. 108.2.

80. Which of the following possible values of alpha would cause exponential smoothing to respond the most quickly to forecast errors?

A. B. C. D. E.

0 .01 .05 .10 .15

81. A manager uses the following equation to predict monthly receipts: Y t = 40,000 + 150t. What is the forecast for July if t = 0 in April of this year?

A. B. C. D. E.

40,450 40,600 42,100 42,250 42,400

82. In trend-adjusted exponential smoothing, the trend-adjusted forecast consists of:

A. an exponentially smoothed forecast and a smoothed trend factor. B. an exponentially smoothed forecast and an estimated trend value. C. the old forecast adjusted by a trend factor. D. the old forecast and a smoothed trend factor. E. a moving average and a trend factor.

3-13 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

83. In the additive model for seasonality, seasonality is expressed as a ______________ adjustment to the average; in the multiplicative model, seasonality is expressed as a __________ adjustment to the average.

A. B. C. D. E.

quantity; percentage percentage; quantity quantity; quantity percentage; percentage qualitative; quantitative

84. Which technique is used in computing seasonal relatives?

A. B. C. D. E.

double smoothing Delphi mean squared error centered moving average exponential smoothing

85. A persistent tendency for forecasts to be greater than or less than the actual values is called:

A. B. C. D. E.

bias. tracking. control charting. positive correlation. linear regression.

86. Which of the following might be used to indicate the cyclical component of a forecast?

A. B. C. D. E.

leading variable mean squared error Delphi technique exponential smoothing mean absolute deviation

87. The primary method for associative forecasting is:

A. B. C. D. E.

sensitivity analysis. regression analysis. simple moving averages. centered moving averages. exponential smoothing.

3-14 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

88. Which term most closely relates to associative forecasting techniques?

A. B. C. D. E.

time series data expert opinions Delphi technique consumer survey predictor variables

89. Which of the following corresponds to the predictor variable in simple linear regression?

A. B. C. D. E.

regression coefficient dependent variable independent variable predicted variable demand coefficient

90. The mean absolute deviation is used to:

A. B. C. D. E.

estimate the trend line. eliminate forecast errors. measure forecast accuracy. seasonally adjust the forecast. compute periodic forecast errors.

91. Given forecast errors of 4, 8, and -3, what is the mean absolute deviation?

A. B. C. D. E.

4 3 5 6 12

92. Given forecast errors of 5, 0, -4, and 3, what is the mean absolute deviation?

A. B. C. D. E.

4 3 2.5 2 1

3-15 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

93. Given forecast errors of 5, 0, -4, and 3, what is the bias?

A. B. C. D. E.

-4 4 5 12 6

94. Which of the following is used for constructing a control chart?

A. B. C. D.

mean absolute deviation mean squared error tracking signal bias

95. The two most important factors in choosing a forecasting technique are:

A. B. C. D. E.

cost and time horizon. accuracy and time horizon. cost and accuracy. quantity and quality. objective and subjective components.

96. The degree of management involvement in short-range forecasts is:

A. B. C. D. E.

none. low. moderate. high. total.

97. Which of the following is not necessarily an element of a good forecast?

A. B. C. D. E.

estimate of accuracy timeliness meaningful units low cost written

3-16 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

98. Forecasting techniques generally assume:

A. the absence of randomness. B. continuity of some underlying causal system. C. a linear relationship between time and demand. D. accuracy that increases the farther out in time the forecast projects. E. accuracy that is better when individual items, rather than groups of items, are being considered. 99. A managerial approach toward forecasting which seeks to actively influence demand is:

A. B. C. D. E.

reactive. proactive. influential. protracted. retroactive.

100 Customer service levels can be improved by better: . A. B. C. D. E.

mission statements. control charting. short-term forecast accuracy. exponential smoothing. customer selection.

101 Given the following historical data, what is the simple three-period moving average . forecast for period 6?

A. B. C. D. E.

67 115 69 68 68.67

3-17 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

102 Given the following historical data and weights of .5, .3, and .2, what is the three. period moving average forecast for period 5?

A. B. C. D. E.

144.20 144.80 144.67 143.00 144.00

103 Use of simple linear regression analysis assumes that: . A. variations around the line are nonrandom. B. deviations around the line are normally distributed. C. predictions can easily be made beyond the range of observed values of the predictor variable. D. all possible predictor variables are included in the model. E. the variance of error terms (deviations) varies directly with the predictor variable. 104 Given forecast errors of -5, -10, and +15, the MAD is: . A. B. C. D. E.

0. 10. 30. 175. 225.

3-18 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

105 The president of State University wants to forecast student enrollments for this . academic year based on the following historical data:

What is the forecast for this year using the naive approach?

A. B. C. D. E.

18,750 19,500 21,000 22,000 22,800

106 The president of State University wants to forecast student enrollments for this . academic year based on the following historical data:

What is the forecast for this year using a four-year simple moving average?

A. B. C. D. E.

18,750 19,500 21,000 22,650 22,800

3-19 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

107 The president of State University wants to forecast student enrollments for this . academic year based on the following historical data:

What is the forecast for this year using exponential smoothing with alpha = .5, if the forecast for two years ago was 16,000?

A. B. C. D. E.

18,750 19,500 21,000 22,650 22,800

108 The president of State University wants to forecast student enrollments for this . academic year based on the following historical data:

What is the forecast for this year using the least squares trend line for these data?

A. B. C. D. E.

18,750 19,500 21,000 22,650 22,800

3-20 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

109 The president of State University wants to forecast student enrollments for this . academic year based on the following historical data:

What is the forecast for this year using trend-adjusted (double) smoothing with alpha = .05 and beta = .3, if the forecast for last year was 21,000, the forecast for two years ago was 19,000, and the trend estimate for last year's forecast was 1,500?

A. B. C. D. E.

18,750 19,500 21,000 22,650 22,800

110 The business analyst for Video Sales, Inc. wants to forecast this year's demand for . DVD decoders based on the following historical data:

What is the forecast for this year using the naive approach?

A. B. C. D. E.

163 180 300 420 510

3-21 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

111 The business analyst for Video Sales, Inc. wants to forecast this year's demand for . DVD decoders based on the following historical data:

What is the forecast for this year using a three-year weighted moving average with weights of .5, .3, and .2?

A. B. C. D. E.

163 180 300 420 510

112 The business analyst for Video Sales, Inc. wants to forecast this year's demand for . DVD decoders based on the following historical data:

What is the forecast for this year using exponential smoothing with alpha = .4, if the forecast for two years ago was 750?

A. B. C. D. E.

163 180 300 420 510

3-22 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

113 The business analyst for Video Sales, Inc. wants to forecast this year's demand for . DVD decoders based on the following historical data:

What is the forecast for this year using the least squares trend line for these data?

A. B. C. D. E.

163 180 300 420 510

114 The business analyst for Video Sales, Inc. wants to forecast this year's demand for . DVD decoders based on the following historical data:

What is the forecast for this year using trend-adjusted (double) smoothing with alpha = .3 and beta = .2, if the forecast for last year was 310, the forecast for two years ago was 430, and the trend estimate for last year's forecast was -150?

A. B. C. D. E.

162.4 180.3 301.4 403.2 510.0

3-23 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

115 Professor Very Busy needs to allocate time next week to include time for office hours. . He needs to forecast the number of students who will seek appointments. He has gathered the following data:

What is this week's forecast using the naive approach?

A. B. C. D. E.

45 50 52 65 78

116 Professor Very Busy needs to allocate time next week to include time for office hours. . He needs to forecast the number of students who will seek appointments. He has gathered the following data:

What is this week's forecast using a three-week simple moving average?

A. B. C. D. E.

49 50 52 65 78

3-24 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

117 Professor Very Busy needs to allocate time next week to include time for office hours. . He needs to forecast the number of students who will seek appointments. He has gathered the following data:

What is this week's forecast using exponential smoothing with alpha = .2, if the forecast for two weeks ago was 90?

A. B. C. D. E.

49 50 52 65 77

118 Professor Very Busy needs to allocate time next week to include time for office hours. . He needs to forecast the number of students who will seek appointments. He has gathered the following data:

What is this week's forecast using the least squares trend line for these data?

A. B. C. D. E.

49 50 52 65 78

3-25 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

119 Professor Very Busy needs to allocate time next week to include time for office hours. . He needs to forecast the number of students who will seek appointments. He has gathered the following data:

What is this week's forecast using trend-adjusted (double) smoothing with alpha = .5 and beta = .1, if the forecast for last week was 65, the forecast for two weeks ago was 75, and the trend estimate for last week's forecast was -5?

A. B. C. D. E.

49.3 50.6 51.3 65.4 78.7

120 A concert promoter is forecasting this year's attendance for one of his concerts based . on the following historical data:

What is this year's forecast using the naive approach?

A. B. C. D. E.

22,000 20,000 18,000 15,000 12,000

3-26 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

121 A concert promoter is forecasting this year's attendance for one of his concerts based . on the following historical data:

What is this year's forecast using a two-year weighted moving average with weights of .7 and .3?

A. B. C. D. E.

19,400 18,600 19,000 11,400 10,600

122 A concert promoter is forecasting this year's attendance for one of his concerts based . on the following historical data:

What is this year's forecast using exponential smoothing with alpha = .2, if last year's smoothed forecast was 15,000?

A. B. C. D. E.

20,000 19,000 17,500 16,000 15,000

3-27 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

123 A concert promoter is forecasting this year's attendance for one of his concerts based . on the following historical data:

What is this year's forecast using the least squares trend line for these data?

A. B. C. D. E.

20,000 21,000 22,000 23,000 24,000

124 A concert promoter is forecasting this year's attendance for one of his concerts based . on the following historical data:

The previous trend line had predicted 18,500 for two years ago, and 19,700 for last year. What was the mean absolute deviation for these forecasts?

A. B. C. D. E.

100 200 400 500 800

3-28 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

125 The dean of a school of business is forecasting total student enrollment for this year's . summer session classes based on the following historical data:

What is this year's forecast using the naive approach?

A. B. C. D. E.

2,000 2,200 2,800 3,000 4,300

126 The dean of a school of business is forecasting total student enrollment for this year's . summer session classes based on the following historical data:

What is this year's forecast using a three-year simple moving average?

A. B. C. D. E.

2,667 2,600 2,500 2,400 2,333

3-29 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

127 The dean of a school of business is forecasting total student enrollment for this year's . summer session classes based on the following historical data:

What is this year's forecast using exponential smoothing with alpha = .4, if last year's smoothed forecast was 2,600?

A. B. C. D. E.

2,600 2,760 2,800 3,840 3,000

128 The dean of a school of business is forecasting total student enrollment for this year's . summer session classes based on the following historical data:

What is the annual rate of change (slope) of the least squares trend line for these data?

A. B. C. D. E.

0 200 400 180 360

3-30 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

129 The dean of a school of business is forecasting total student enrollment for this year's . summer session classes based on the following historical data:

What is this year's forecast using the least squares trend line for these data?

A. B. C. D. E.

3,600 3,500 3,400 3,300 3,200

130 The owner of Darkest Tans Unlimited in a local mall is forecasting this month's . (October's) demand for the one new tanning booth based on the following historical data:

What is this month's forecast using the naive approach?

A. B. C. D. E.

100 160 130 140 120

3-31 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

131 The owner of Darkest Tans Unlimited in a local mall is forecasting this month's . (October's) demand for the one new tanning booth based on the following historical data:

What is this month's forecast using a four-month weighted moving average with weights of .4, .3, .2, and .1?

A. B. C. D. E.

120 129 141 135 140

132 The owner of Darkest Tans Unlimited in a local mall is forecasting this month's . (October's) demand for the one new tanning booth based on the following historical data:

What is this month's forecast using exponential smoothing with alpha = .2, if August's forecast was 145?

A. B. C. D. E.

144 140 142 148 163

3-32 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

133 The owner of Darkest Tans Unlimited in a local mall is forecasting this month's . (October's) demand for the one new tanning booth based on the following historical data:

What is the monthly rate of change (slope) of the least squares trend line for these data?

A. B. C. D. E.

320 102 8 -.4 -8

134 The owner of Darkest Tans Unlimited in a local mall is forecasting this month's . (October's) demand for the one new tanning booth based on the following historical data:

What is this month's forecast using the least squares trend line for these data?

A. B. C. D. E.

1,250 128.6 102 158 164

3-33 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

135 Which of the following mechanisms for enhancing profitability is most likely to result . from improving short-term forecast performance?

A. B. C. D. E.

increased inventory reduced flexibility higher-quality products greater customer satisfaction greater seasonality

136 Which of the following changes would tend to shorten the time frame for short-term . forecasting?

A. bringing greater variety into the product mix B. increasing the flexibility of the production system C. ordering fewer weather-sensitive items D. adding more special-purpose equipment E. investing in the production system to make it more task-specific 137 Which of the following helps improve supply chain forecasting performance? . A. B. C. D. E.

contracts that require supply chain members to formulate long-term forecasts penalties for supply chain members that adjust forecasts sharing forecasts or demand data across the supply chain increasing lead times for critical supply chain members increasing the number of suppliers at critical junctures in the supply chain

138 Which of the following would tend to decrease forecast accuracy? . A. a reduction in demand variability B. a shortening of the forecast time horizon C. an attempt to forecast demand for a group of similar items rather than an individual item D. a change in the underlying causal system 139 Which of the following is the most valuable piece of information the sales force can . bring into forecasting situations?

A. what customers are most likely to do in the future B. what customers most want to do in the future C. what customers' future plans are D. whether customers are satisfied or dissatisfied with their performance in the past E. what the salesperson's appropriate sales quota should be

3-34 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

Essay Questions

140 Develop a forecast for the next period, given the data below, using a three-period . moving average.

141 Consider the data below: .

Using exponential smoothing with alpha = .2, and assuming the forecast for period 11 was 80, what would the forecast for period 14 be?

3-35 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

142 A manager is using exponential smoothing to predict merchandise returns at a . suburban branch of a department store chain. Given a previous forecast of 140 items, an actual number of returns of 148 items, and a smoothing constant equal to .15, what is the forecast for the next period?

143 A manager is using the equation below to forecast quarterly demand for a product: . Yt = 6,000 + 80t where t = 0 at Q2 of last year Quarter relatives are Q1 = .6, Q2 = .9, Q3 = 1.3, and Q4 = 1.2. What forecasts are appropriate for the last quarter of this year and the first quarter of next year?

144 Over the past five years, a firm's sales have averaged 250 units in the first quarter of . each year, 100 units in the second quarter, 150 units in the third quarter, and 300 units in the fourth quarter. What are appropriate quarter relatives for this firm's sales? Hint: Only minimal computations are necessary.

3-36 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

145 A manager has been using a certain technique to forecast demand for gallons of ice . cream for the past six periods. Actual and predicted amounts are shown below. Would a naive forecast have produced better results?

146 A new car dealer has been using exponential smoothing with an alpha of .2 to . forecast weekly new car sales. Given the data below, would a naive forecast have provided greater accuracy? Explain. Assume an initial exponential forecast of 60 units in period 2 (i.e., no forecast for period 1).

3-37 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

147 A CPA firm has been using the following equation to predict annual demand for tax . audits: Yt = 55 + 4t. Demand for the past few years is shown below. Is the forecast performing as well as it might? Explain.

148 Given the data below, develop a forecast for period 6 using a four-period weighted . moving average and weights of .4, .3, .2 and .1.

3-38 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

149 Use linear regression to develop a predictive model for demand for burial vaults . based on sales of caskets.

150 Given the following data, develop a linear regression model for y as a function of x. .

3-39 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

151 Given the following data, develop a linear regression model for y as a function of x. .

152 Develop a linear trend equation for the data on bread deliveries shown below. . Forecast deliveries for period 11 through 14.

3-40 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

153 Demand for the last four months was: .

A) Predict demand for July using each of these methods: 1) a three-period moving average 2) exponential smoothing with alpha equal to .20 (use a naive forecast for April for your first forecast) B) If the naive approach had been used to predict demand for April through June, what would MAD have been for those months?

3-41 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

154 A manager wants to choose one of two forecasting alternatives. Each alternative was . tested using historical data. The resulting forecast errors for the two are shown in the table. Analyze the data and recommend a course of action to the manager.

155 A manager uses this equation to predict demand: Y t = 20 + 4t. Over the past eight . periods, demand has been as follows. Are the results acceptable? Explain.

3-42 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

156 Data on demand over the last few years are available as follows: .

What would this year's forecast be if we were using the naive approach?

3-43 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

157 Data on demand over the last few years are available as follows: .

What is this year's forecast using a four-year simple moving average?

3-44 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

158 Data on demand over the last few years are available as follows: .

What is this year's forecast using exponential smoothing with alpha = .25, if last year's smoothed forecast was 45?

3-45 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

159 Data on demand over the last few years are available as follows: .

What are this and next year's forecasts using the least squares trend line for these data?

3-46 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

160 Data on demand over the last few years are available as follows: .

What is this year's forecast using trend-adjusted (double) smoothing with alpha = .2 and beta = .1, if the forecast for last year was 56, the forecast for two years ago was 46, and the trend estimate for last year's forecast was 7?

161 Data on the last three years of demand are available as follows: .

What is the centered moving average for spring two years ago?

3-47 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

162 Data on the last three years of demand are available as follows: .

What is the spring's seasonal relative?

163 Data on the last three years of demand are available as follows: .

What is the linear regression trend line for these data (t = 0 for spring, three years ago)?

3-48 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

164 Data on the last three years of demand are available as follows: .

What is this year's seasonally adjusted forecast for each season?

3-49 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

Chapter 03 Forecasting Answer Key

True / False Questions

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Level of Difficulty: 2 Medium Topic: Features Common to All Forecasts

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation 3-50 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

Blooms: Understand Learning Objective: 03-04 Outline the steps in the forecasting process. Level of Difficulty: 1 Easy Topic: Steps in the Forecasting Process

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-01 List features common to all forecasts. Level of Difficulty: 2 Medium Topic: Features Common to All Forecasts

5.

Forecasts help managers both to plan the system itself and to provide valuable information for using the system. TRUE Both planning and use are shaped by forecasts.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-01 List features common to all forecasts. Level of Difficulty: 1 Easy Topic: Forecasting

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 more quickly, its forecasts need not be so long term.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-03 List the elements of a good forecast. Level of Difficulty: 2 Medium Topic: Elements of a Good Forecast

3-51 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Level of Difficulty: 2 Medium Topic: Steps in the Forecasting Process

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 1 Easy Topic: Associative Forecasting Techniques

3-52 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

10.

Time-series techniques involve the identification of explanatory variables that can be used to predict future demand. FALSE Associative forecasts involve identifying explanatory variables.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-06 Describe four qualitative forecasting techniques. Level of Difficulty: 1 Easy Topic: Qualitative Forecasts

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-06 Describe four qualitative forecasting techniques. Level of Difficulty: 1 Easy Topic: Qualitative Forecasts

3-53 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

13.

Exponential smoothing adds a percentage (called alpha) of the last period's forecast to estimate the next period's demand. FALSE Exponential smoothing adds a percentage to the last period's forecast error.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-01 List features common to all forecasts. Level of Difficulty: 1 Easy Topic: Monitoring the Forecast Error

15.

Forecasting techniques that are based on time-series data assume that future values of the series will duplicate past values. FALSE Time-series forecasts assume that future patterns in the series will mimic past patterns in the series.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-54 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-01 List features common to all forecasts. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

18.

The naive approach to forecasting requires a linear trend line. FALSE The naive approach is useful in a wider variety of settings.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-55 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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 naive forecast is more limited in its application.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

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 naive forecast performs reasonably well when compared to more complex techniques.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-56 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-04 Outline the steps in the forecasting process. Level of Difficulty: 1 Easy Topic: Forecasting

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking 3-57 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

28.

Trend-adjusted exponential smoothing requires selection of two smoothing constants. TRUE One is for the trend and one is for the random error.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-58 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

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 cannot be used. TRUE Computing seasonal relatives depends on past data being available.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-59 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

32.

Removing the seasonal component from a data series (deseasonalizing) can be accomplished by dividing each data point by its appropriate seasonal relative. TRUE Deseasonalized data points have been adjusted for seasonal influences.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 3 Hard Topic: Associative Forecasting Techniques

34.

Curvilinear and multiple regression procedures permit us to extend associative models to relationships that are nonlinear or involve more than one predictor variable. TRUE Regression analysis can be used in a variety of settings.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 2 Medium Topic: Associative Forecasting Techniques

3-60 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 1 Easy Topic: Forecast Accuracy

36.

Correlation measures the strength and direction of a relationship between variables. TRUE The association between two variations is summarized in the correlation coefficient.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 2 Medium Topic: Associative Forecasting Techniques

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 2 Medium Topic: Forecast Accuracy

38.

In exponential smoothing, an alpha of 1.0 will generate the same forecast that a naive forecast would yield. TRUE With alpha equal to 1 we are using a naive forecasting method.

AACSB: Reflective Thinking 3-61 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 2 Medium Topic: Forecast Accuracy

40.

A control chart involves setting action limits for cumulative forecast error. FALSE Control charts set action limits for the tracking signal.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Level of Difficulty: 2 Medium Topic: Monitoring the Forecast Error

41.

A tracking signal focuses on the ratio of cumulative forecast error to the corresponding value of MAD. TRUE Large absolute values of the tracking signal suggest a fundamental change in the forecast model's performance.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Level of Difficulty: 2 Medium Topic: Monitoring the Forecast Error

3-62 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

42.

The use of a control chart assumes that errors are normally distributed about a mean of zero. TRUE Over time, a forecast model's tracking signal should fluctuate randomly about a mean of zero.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Level of Difficulty: 3 Hard Topic: Monitoring the Forecast Error

43.

Bias exists when forecasts tend to be greater or less than the actual values of time series. TRUE A tendency in one direction is defined as bias.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 1 Easy Topic: Monitoring the Forecast Error

44.

Bias is measured by the cumulative sum of forecast errors. TRUE Bias would result in the cumulative sum of forecast errors being large in absolute value.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 2 Medium Topic: Monitoring the Forecast Error

3-63 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

45.

Seasonal relatives can be used to deseasonalize data or incorporate seasonality in a forecast. TRUE Seasonal relatives are used to deseasonalize data to forecast future values of the underlying trend, and they are also used to reseasonalize deseasonalized forecasts.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

46.

The best forecast is not necessarily the most accurate. TRUE More accuracy often comes at too high a cost to be worthwhile.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-01 List features common to all forecasts. Level of Difficulty: 2 Medium Topic: Elements of a Good Forecast

Multiple Choice Questions

3-64 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

47.

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. Customers themselves may be unclear regarding what they'd like to do versus what they'll actually do.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-06 Describe four qualitative forecasting techniques. Level of Difficulty: 1 Easy Topic: Qualitative Forecasts

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: A t-4 = 380, At-3 = 410, At-2 = 390, At-1 = 400. What will be the demand forecast for period t?

A. B. C. D. E.

402 397 399 393 403

The forecast will be (.1 * 380) + (.2 * 410) + (.3 * 390) + (.4 * 400) = 397.

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Learning Objective: 03-09 Prepare a weighted-average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-65 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

49.

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: A t-3 = 2,200, At-2 = 1,950, At-1 = 2,050. What will be the demand forecast for period t?

A. B. C. D. E.

2,000 2,095 1,980 2,050 1,875

The forecast for will be (.2 * 2,200) + (.3 * 1,950) + (.5 * 2,050) = 2,050.

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Learning Objective: 03-09 Prepare a weighted-average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

50.

When choosing a forecasting technique, a critical trade-off that must be considered is that between:

A. B. C. D. E.

time series and associative. seasonality and cyclicality. length and duration. simplicity and complexity. cost and accuracy.

The trade-off between cost and accuracy is the critical consideration when choosing a forecasting technique.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Level of Difficulty: 1 Easy Topic: Choosing a Forecasting Technique

3-66 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

51.

The more novel a new product or service design is, the more forecasters have to rely on:

A. B. C. D. E.

subjective estimates. seasonality. cyclicality. historical data. smoothed variation.

New products and services lack historical data, so forecasts for them must be based on subjective estimates.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Level of Difficulty: 1 Easy Topic: Choosing a Forecasting Technique

52.

Forecasts based on judgment and opinion do not include:

A. B. C. D. E.

executive opinion. salesperson opinion. second opinions. customer surveys. Delphi methods.

Second opinions generally refer to medical diagnoses, not demand forecasting.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-06 Describe four qualitative forecasting techniques. Level of Difficulty: 2 Medium Topic: Qualitative Forecasts

3-67 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

53.

Which of the following is/are a primary input into capacity, sales, and production planning?

A. B. C. D. E.

product design market share ethics globalization demand forecasts

Demand forecasts are direct inputs into capacity, sales, and production plans.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-01 List features common to all forecasts. Level of Difficulty: 1 Easy Topic: Features Common to All Forecasts

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-01 List features common to all forecasts. Level of Difficulty: 3 Hard Topic: Features Common to All Forecasts

3-68 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

55.

Which of the following is not a step in the forecasting process?

A. B. C. D. E.

Determine the purpose and level of detail required. Eliminate all assumptions. Establish a time horizon. Select a forecasting model. Monitor the forecast.

We cannot eliminate all assumptions.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-04 Outline the steps in the forecasting process. Level of Difficulty: 2 Medium Topic: Features Common to All Forecasts

56.

Minimizing the sum of the squared deviations around the line is called:

A. B. C. D. E.

mean squared error technique. mean absolute deviation. double smoothing. least squares estimation. predictor regression.

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

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 2 Medium Topic: Associative Forecasting Techniques

57.

The two general approaches to forecasting are:

A. B. C. D. E.

mathematical and statistical. qualitative and quantitative. judgmental and qualitative. historical and associative. precise and approximation.

Forecast approaches are either quantitative or qualitative.

AACSB: Reflective Thinking 3-69 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Level of Difficulty: 1 Easy Topic: Approaches to Forecasting

58.

Which of the following is not a type of judgmental forecasting?

A. B. C. D. E.

executive opinions sales force opinions consumer surveys the Delphi method time series analysis

Time series analysis is a quantitative approach.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-06 Describe four qualitative forecasting techniques. Level of Difficulty: 1 Easy Topic: Qualitative Forecasts

59.

Accuracy in forecasting can be measured by:

A. B. C. D. E.

MSE. MRP. MPS. MTM. MTE.

MSE is mean squared error.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 3 Hard Topic: Forecast Accuracy

3-70 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-06 Describe four qualitative forecasting techniques. Level of Difficulty: 3 Hard Topic: Qualitative Forecasts

61.

Which phrase most closely describes the Delphi technique?

A. B. C. D. E.

associative forecast consumer survey series of questionnaires developed in India historical data

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

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-06 Describe four qualitative forecasting techniques. Level of Difficulty: 1 Easy Topic: Qualitative Forecasts

3-71 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

62.

The forecasting method which uses anonymous questionnaires to achieve a consensus forecast is:

A. B. C. D. E.

sales force opinions. consumer surveys. the Delphi method. time series analysis. executive opinions.

Anonymity is important in Delphi efforts.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-06 Describe four qualitative forecasting techniques. Level of Difficulty: 1 Easy Topic: Qualitative Forecasts

63.

One reason for using the Delphi method in forecasting is to:

A. B. C. D. E.

avoid premature consensus (bandwagon effect). achieve a high degree of accuracy. maintain accountability and responsibility. be able to replicate results. prevent hurt feelings.

A bandwagon can lead to popular but potentially inaccurate viewpoints to drown out other important considerations.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-06 Describe four qualitative forecasting techniques. Level of Difficulty: 2 Medium Topic: Qualitative Forecasts

3-72 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

64.

Detecting nonrandomness in errors can be done using:

A. B. C. D. E.

MSEs. MAPs. control charts. correlation coefficients. strategies.

Control charts graphically depict the statistical behavior of forecast errors.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Level of Difficulty: 2 Medium Topic: Approaches to Forecasting

65.

Gradual, long-term movement in time series data is called:

A. B. C. D. E.

seasonal variation. cycles. irregular variation. trend. random variation.

Trends move the time series in a long-term direction.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

66.

The primary difference between seasonality and cycles is:

A. B. C. D. E.

the duration of the repeating patterns. the magnitude of the variation. the ability to attribute the pattern to a cause. the direction of the movement. there are only four seasons but 30 cycles.

Seasons happen within time periods; cycles happen across multiple time periods.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation 3-73 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

Blooms: Remember Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

67.

Averaging techniques are useful for:

A. B. C. D. E.

distinguishing between random and nonrandom variations. smoothing out fluctuations in time series. eliminating historical data. providing accuracy in forecasts. average people.

Smoothing helps forecasters see past random error.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

68.

Putting forecast errors into perspective is best done using

A. B. C. D. E.

exponential smoothing. MAPE. linear decision rules. MAD. hindsight.

MAPE depicts the forecast error relative to what was being forecast.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 2 Medium Topic: Monitoring the Forecast Error

3-74 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

69.

Using the latest observation in a sequence of data to forecast the next period is:

A. B. C. D. E.

a moving average forecast. a naive forecast. an exponentially smoothed forecast. an associative forecast. regression analysis.

Only one piece of information is needed for a naive forecast.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

70.

For the data given below, what would the naive forecast be for period 5?

A. B. C. D. E.

58 62 59.5 61 cannot tell from the data given

Period 5's forecast would be period 4's demand.

AACSB: Analytic Blooms: Apply Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-75 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

71.

Moving average forecasting techniques do the following:

A. B. C. D. E.

Immediately reflect changing patterns in the data. Lead changes in the data. Smooth variations in the data. Operate independently of recent data. Assist when organizations are relocating.

Variation is smoothed out in moving average forecasts.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

72.

Which is not a characteristic of simple moving averages applied to time series data?

A. B. C. D. E.

smoothes random variations in the data weights each historical value equally lags changes in the data requires only last period's forecast and actual data smoothes real variations in the data

Simple moving averages can require several periods of data.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-76 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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. B. C. D. E.

decreased. increased. multiplied by a larger alpha. multiplied by a smaller alpha. eliminated if the MAD is greater than the MSE.

Fewer data points result in more responsive moving averages.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

74.

A forecast based on the previous forecast plus a percentage of the forecast error is:

A. B. C. D. E.

a naive forecast. a simple moving average forecast. a centered moving average forecast. an exponentially smoothed forecast. an associative forecast.

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

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-77 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

75.

Which is not a characteristic of exponential smoothing?

A. B. C. D. E.

smoothes random variations in the data weights each historical value equally has an easily altered weighting scheme has minimal data storage requirements smoothes real variations in the data

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

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

76.

Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast?

A. B. C. D. E.

0 .01 .1 .5 1.0

An alpha of 1.0 leads to a naive forecast.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-78 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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. B. C. D. E.

.01. .10. .15. .20. .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.

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

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. B. C. D. E.

36.9 57.5 60.5 62.5 65.5

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

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-79 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

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. B. C. D. E.

80.8. 93.8. 100.2. 101.8. 108.2.

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

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

80.

Which of the following possible values of alpha would cause exponential smoothing to respond the most quickly to forecast errors?

A. B. C. D. E.

0 .01 .05 .10 .15

Larger values for alpha correspond with greater responsiveness.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-80 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

81.

A manager uses the following equation to predict monthly receipts: Y t = 40,000 + 150t. What is the forecast for July if t = 0 in April of this year?

A. B. C. D. E.

40,450 40,600 42,100 42,250 42,400

July would be period 3, so the forecast would be 40,000 + 150(3).

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

82.

In trend-adjusted exponential smoothing, the trend-adjusted forecast consists of:

A. an exponentially smoothed forecast and a smoothed trend factor. B. an exponentially smoothed forecast and an estimated trend value. C. the old forecast adjusted by a trend factor. D. the old forecast and a smoothed trend factor. E. a moving average and a trend factor. Both random variation and the trend are smoothed in TAF models.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-81 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

83.

In the additive model for seasonality, seasonality is expressed as a ______________ adjustment to the average; in the multiplicative model, seasonality is expressed as a __________ adjustment to the average.

A. B. C. D. E.

quantity; percentage percentage; quantity quantity; quantity percentage; percentage qualitative; quantitative

The additive model simply adds a seasonal adjustment to the deseasonalized forecast. The multiplicative model adjusts the deseasonalized forecast by multiplying it by a season relative or index.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

84.

Which technique is used in computing seasonal relatives?

A. B. C. D. E.

double smoothing Delphi mean squared error centered moving average exponential smoothing

The centered moving average serves as the basis point for computing seasonal relatives.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-82 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

85.

A persistent tendency for forecasts to be greater than or less than the actual values is called:

A. B. C. D. E.

bias. tracking. control charting. positive correlation. linear regression.

Bias is a tendency for a forecast to be above (or below) the actual value.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 2 Medium Topic: Forecast Accuracy

86.

Which of the following might be used to indicate the cyclical component of a forecast?

A. B. C. D. E.

leading variable mean squared error Delphi technique exponential smoothing mean absolute deviation

Leading variables, such as births in a given year, can correlate strongly with longterm phenomena such as cycles.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-83 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

87.

The primary method for associative forecasting is:

A. B. C. D. E.

sensitivity analysis. regression analysis. simple moving averages. centered moving averages. exponential smoothing.

Regression analysis is an associative forecasting technique.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 2 Medium Topic: Associative Forecasting Techniques

88.

Which term most closely relates to associative forecasting techniques?

A. B. C. D. E.

time series data expert opinions Delphi technique consumer survey predictor variables

Associative techniques use predictor variables.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 2 Medium Topic: Associative Forecasting Techniques

3-84 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

89.

Which of the following corresponds to the predictor variable in simple linear regression?

A. B. C. D. E.

regression coefficient dependent variable independent variable predicted variable demand coefficient

Demand is the typical dependent variable when forecasting with simple linear regression.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 2 Medium Topic: Associative Forecasting Techniques

90.

The mean absolute deviation is used to:

A. B. C. D. E.

estimate the trend line. eliminate forecast errors. measure forecast accuracy. seasonally adjust the forecast. compute periodic forecast errors.

MAD is one way of evaluating forecast performance.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 2 Medium Topic: Forecast Accuracy

3-85 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

91.

Given forecast errors of 4, 8, and -3, what is the mean absolute deviation?

A. B. C. D. E.

4 3 5 6 12

Convert each error into an absolute value and then average.

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 1 Easy Topic: Forecast Accuracy

92.

Given forecast errors of 5, 0, -4, and 3, what is the mean absolute deviation?

A. B. C. D. E.

4 3 2.5 2 1

Convert each error into an absolute value and then average.

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 1 Easy Topic: Forecast Accuracy

93.

Given forecast errors of 5, 0, -4, and 3, what is the bias?

A. B. C. D. E.

-4 4 5 12 6

Sum the forecast errors.

AACSB: Analytic Accessibility: Keyboard Navigation 3-86 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

Blooms: Apply Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 2 Medium Topic: Forecast Accuracy

94.

Which of the following is used for constructing a control chart?

A. B. C. D.

mean absolute deviation mean squared error tracking signal bias

The mean squared error leads to an estimate for the sample forecast standard deviation.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Level of Difficulty: 2 Medium Topic: Approaches to Forecasting

95.

The two most important factors in choosing a forecasting technique are:

A. B. C. D. E.

cost and time horizon. accuracy and time horizon. cost and accuracy. quantity and quality. objective and subjective components.

More accurate forecasts cost more but may not be worth the additional cost.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Level of Difficulty: 2 Medium Topic: Choosing a Forecasting Technique

3-87 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

96.

The degree of management involvement in short-range forecasts is:

A. B. C. D. E.

none. low. moderate. high. total.

Short-range forecasting tends to be fairly routine.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-01 List features common to all forecasts. Level of Difficulty: 1 Easy Topic: Choosing a Forecasting Technique

97.

Which of the following is not necessarily an element of a good forecast?

A. B. C. D. E.

estimate of accuracy timeliness meaningful units low cost written

A good forecast can be quite costly if necessary.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-03 List the elements of a good forecast. Level of Difficulty: 2 Medium Topic: Elements of a Good Forecast

3-88 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

98.

Forecasting techniques generally assume:

A. the absence of randomness. B. continuity of some underlying causal system. C. a linear relationship between time and demand. D. accuracy that increases the farther out in time the forecast projects. E. accuracy that is better when individual items, rather than groups of items, are being considered. Forecasting techniques generally assume that the same underlying causal system that existed in the past will continue to exist in the future.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-01 List features common to all forecasts. Level of Difficulty: 1 Easy Topic: Forecast Accuracy

99.

A managerial approach toward forecasting which seeks to actively influence demand is:

A. B. C. D. E.

reactive. proactive. influential. protracted. retroactive.

Simply responding to demand is a reactive approach.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Level of Difficulty: 1 Easy Topic: Using Forecast Information

3-89 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

100. Customer service levels can be improved by better:

A. B. C. D. E.

mission statements. control charting. short-term forecast accuracy. exponential smoothing. customer selection.

More accurate short-term forecasts enable organizations to better accommodate customer requests.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-03 List the elements of a good forecast. Level of Difficulty: 3 Hard Topic: Operations Strategy

101. Given the following historical data, what is the simple three-period moving average forecast for period 6?

A. B. C. D. E.

67 115 69 68 68.67

Average demand from periods 3 through 5.

AACSB: Analytic Blooms: Apply Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-90 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

102. Given the following historical data and weights of .5, .3, and .2, what is the threeperiod moving average forecast for period 5?

A. B. C. D. E.

144.20 144.80 144.67 143.00 144.00

Multiply period 4 (144) by .5, period 3 (148) by .3, and period 2 (142) by .2, then sum these products.

AACSB: Analytic Blooms: Apply Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

103. Use of simple linear regression analysis assumes that:

A. variations around the line are nonrandom. B. deviations around the line are normally distributed. C. predictions can easily be made beyond the range of observed values of the predictor variable. D. all possible predictor variables are included in the model. E. the variance of error terms (deviations) varies directly with the predictor variable. That deviations conform to the normal distribution is a very important assumption underpinning simple linear regression.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Remember Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 3 Hard Topic: Associative Forecasting Techniques

3-91 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

104. Given forecast errors of -5, -10, and +15, the MAD is:

A. B. C. D. E.

0. 10. 30. 175. 225.

Convert these errors into absolute value, then average.

AACSB: Analytic Accessibility: Keyboard Navigation Blooms: Apply Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 1 Easy Topic: Forecast Accuracy

105. The president of State University wants to forecast student enrollments for this academic year based on the following historical data:

What is the forecast for this year using the naive approach?

A. B. C. D. E.

18,750 19,500 21,000 22,000 22,800

This year's forecast would be last year's enrollment.

AACSB: Analytic Blooms: Apply Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-92 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

106. The president of State University wants to forecast student enrollments for this academic year based on the following historical data:

What is the forecast for this year using a four-year simple moving average?

A. B. C. D. E.

18,750 19,500 21,000 22,650 22,800

Average enrollment from the last four years.

AACSB: Analytic Blooms: Apply Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-93 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

107. The president of State University wants to forecast student enrollments for this academic year based on the following historical data:

What is the forecast for this year using exponential smoothing with alpha = .5, if the forecast for two years ago was 16,000?

A. B. C. D. E.

18,750 19,500 21,000 22,650 22,800

Multiply last year's forecast error by the smoothing constant, then add that adjusted error to last year's forecast to get this year's forecast.

AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-94 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

108. The president of State University wants to forecast student enrollments for this academic year based on the following historical data:

What is the forecast for this year using the least squares trend line for these data?

A. B. C. D. E.

18,750 19,500 21,000 22,650 22,800

Treat 5 years ago as period 0.

AACSB: Analytic Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-95 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

109. The president of State University wants to forecast student enrollments for this academic year based on the following historical data:

What is the forecast for this year using trend-adjusted (double) smoothing with alpha = .05 and beta = .3, if the forecast for last year was 21,000, the forecast for two years ago was 19,000, and the trend estimate for last year's forecast was 1,500?

A. B. C. D. E.

18,750 19,500 21,000 22,650 22,800

Smooth both the trend and the forecast to get this year's forecast.

AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-96 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

110. The business analyst for Video Sales, Inc. wants to forecast this year's demand for DVD decoders based on the following historical data:

What is the forecast for this year using the naive approach?

A. B. C. D. E.

163 180 300 420 510

This year's forecast is last year's demand.

AACSB: Analytic Blooms: Apply Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-97 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

111. The business analyst for Video Sales, Inc. wants to forecast this year's demand for DVD decoders based on the following historical data:

What is the forecast for this year using a three-year weighted moving average with weights of .5, .3, and .2?

A. B. C. D. E.

163 180 300 420 510

Multiply the last three periods of demand by the appropriate weights, then sum the resulting products.

AACSB: Analytic Blooms: Apply Learning Objective: 03-09 Prepare a weighted-average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-98 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

112. The business analyst for Video Sales, Inc. wants to forecast this year's demand for DVD decoders based on the following historical data:

What is the forecast for this year using exponential smoothing with alpha = .4, if the forecast for two years ago was 750?

A. B. C. D. E.

163 180 300 420 510

First formulate last year's exponentially smoothed forecast, then proceed.

AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-99 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

113. The business analyst for Video Sales, Inc. wants to forecast this year's demand for DVD decoders based on the following historical data:

What is the forecast for this year using the least squares trend line for these data?

A. B. C. D. E.

163 180 300 420 510

Treat the earliest period of demand as period 0, then formulate least squares estimates and proceed.

AACSB: Analytic Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-100 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

114. The business analyst for Video Sales, Inc. wants to forecast this year's demand for DVD decoders based on the following historical data:

What is the forecast for this year using trend-adjusted (double) smoothing with alpha = .3 and beta = .2, if the forecast for last year was 310, the forecast for two years ago was 430, and the trend estimate for last year's forecast was -150?

A. B. C. D. E.

162.4 180.3 301.4 403.2 510.0

Smooth both the trend and the forecast to get this year's forecast.

AACSB: Analytic Blooms: Apply Learning Objective: 03-12 Prepare a trend-adjusted exponential smoothing forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-101 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

115. Professor Very Busy needs to allocate time next week to include time for office hours. He needs to forecast the number of students who will seek appointments. He has gathered the following data:

What is this week's forecast using the naive approach?

A. B. C. D. E.

45 50 52 65 78

This week's forecast is last week's demand.

AACSB: Analytic Blooms: Apply Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-102 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

116. Professor Very Busy needs to allocate time next week to include time for office hours. He needs to forecast the number of students who will seek appointments. He has gathered the following data:

What is this week's forecast using a three-week simple moving average?

A. B. C. D. E.

49 50 52 65 78

Average the three most recent weeks of demand.

AACSB: Analytic Blooms: Apply Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-103 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

117. Professor Very Busy needs to allocate time next week to include time for office hours. He needs to forecast the number of students who will seek appointments. He has gathered the following data:

What is this week's forecast using exponential smoothing with alpha = .2, if the forecast for two weeks ago was 90?

A. B. C. D. E.

49 50 52 65 77

Formulate the forecast for last week, then use that to get this week's forecast.

AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-104 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

118. Professor Very Busy needs to allocate time next week to include time for office hours. He needs to forecast the number of students who will seek appointments. He has gathered the following data:

What is this week's forecast using the least squares trend line for these data?

A. B. C. D. E.

49 50 52 65 78

Treat the earliest period as period 0, then formulate least squares coefficients and proceed.

AACSB: Analytic Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-105 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

119. Professor Very Busy needs to allocate time next week to include time for office hours. He needs to forecast the number of students who will seek appointments. He has gathered the following data:

What is this week's forecast using trend-adjusted (double) smoothing with alpha = . 5 and beta = .1, if the forecast for last week was 65, the forecast for two weeks ago was 75, and the trend estimate for last week's forecast was -5?

A. B. C. D. E.

49.3 50.6 51.3 65.4 78.7

Smooth both the trend and the forecast to get this year's forecast.

AACSB: Analytic Blooms: Apply Learning Objective: 03-12 Prepare a trend-adjusted exponential smoothing forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-106 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

120. A concert promoter is forecasting this year's attendance for one of his concerts based on the following historical data:

What is this year's forecast using the naive approach?

A. B. C. D. E.

22,000 20,000 18,000 15,000 12,000

This year's forecast is last year's attendance.

AACSB: Analytic Blooms: Apply Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-107 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

121. A concert promoter is forecasting this year's attendance for one of his concerts based on the following historical data:

What is this year's forecast using a two-year weighted moving average with weights of .7 and .3?

A. B. C. D. E.

19,400 18,600 19,000 11,400 10,600

Multiply the two most recent periods by the appropriate weights, then sum the resulting products.

AACSB: Analytic Blooms: Apply Learning Objective: 03-09 Prepare a weighted-average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-108 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

122. A concert promoter is forecasting this year's attendance for one of his concerts based on the following historical data:

What is this year's forecast using exponential smoothing with alpha = .2, if last year's smoothed forecast was 15,000?

A. B. C. D. E.

20,000 19,000 17,500 16,000 15,000

Multiply last year's forecast error by the smoothing constant, then add that product to last year's forecast to get this year's forecast.

AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-109 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

123. A concert promoter is forecasting this year's attendance for one of his concerts based on the following historical data:

What is this year's forecast using the least squares trend line for these data?

A. B. C. D. E.

20,000 21,000 22,000 23,000 24,000

Treat the earliest year as period zero in formulating least squares coefficients.

AACSB: Analytic Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-110 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

124. A concert promoter is forecasting this year's attendance for one of his concerts based on the following historical data:

The previous trend line had predicted 18,500 for two years ago, and 19,700 for last year. What was the mean absolute deviation for these forecasts?

A. B. C. D. E.

100 200 400 500 800

Convert each period's forecast error into absolute value, then average.

AACSB: Analytic Blooms: Apply Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 2 Medium Topic: Forecast Accuracy

3-111 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

125. The dean of a school of business is forecasting total student enrollment for this year's summer session classes based on the following historical data:

What is this year's forecast using the naive approach?

A. B. C. D. E.

2,000 2,200 2,800 3,000 4,300

This year's forecast would be last year's enrollment.

AACSB: Analytic Blooms: Apply Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-112 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

126. The dean of a school of business is forecasting total student enrollment for this year's summer session classes based on the following historical data:

What is this year's forecast using a three-year simple moving average?

A. B. C. D. E.

2,667 2,600 2,500 2,400 2,333

Average the most recent periods of enrollment.

AACSB: Analytic Blooms: Apply Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-113 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

127. The dean of a school of business is forecasting total student enrollment for this year's summer session classes based on the following historical data:

What is this year's forecast using exponential smoothing with alpha = .4, if last year's smoothed forecast was 2,600?

A. B. C. D. E.

2,600 2,760 2,800 3,840 3,000

Multiply last year's forecast error by the smoothing constant. Add the product to last year's forecast to get this year's forecast.

AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-114 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

128. The dean of a school of business is forecasting total student enrollment for this year's summer session classes based on the following historical data:

What is the annual rate of change (slope) of the least squares trend line for these data?

A. B. C. D. E.

0 200 400 180 360

Treat the earliest period as period 0, then formulate the least squares slope.

AACSB: Analytic Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-115 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

129. The dean of a school of business is forecasting total student enrollment for this year's summer session classes based on the following historical data:

What is this year's forecast using the least squares trend line for these data?

A. B. C. D. E.

3,600 3,500 3,400 3,300 3,200

Treat the earliest period as period 0, then formulate the least squares coefficients and proceed.

AACSB: Analytic Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-116 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

130. The owner of Darkest Tans Unlimited in a local mall is forecasting this month's (October's) demand for the one new tanning booth based on the following historical data:

What is this month's forecast using the naive approach?

A. B. C. D. E.

100 160 130 140 120

This month's forecast is last month's demand.

AACSB: Analytic Blooms: Apply Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-117 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

131. The owner of Darkest Tans Unlimited in a local mall is forecasting this month's (October's) demand for the one new tanning booth based on the following historical data:

What is this month's forecast using a four-month weighted moving average with weights of .4, .3, .2, and .1?

A. B. C. D. E.

120 129 141 135 140

Multiply the four most recent periods of demand by the appropriate weights, then sum the resulting products.

AACSB: Analytic Blooms: Apply Learning Objective: 03-09 Prepare a weighted-average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-118 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

132. The owner of Darkest Tans Unlimited in a local mall is forecasting this month's (October's) demand for the one new tanning booth based on the following historical data:

What is this month's forecast using exponential smoothing with alpha = .2, if August's forecast was 145?

A. B. C. D. E.

144 140 142 148 163

First calculate September's forecast, then use that to calculate this month's forecast.

AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-119 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

133. The owner of Darkest Tans Unlimited in a local mall is forecasting this month's (October's) demand for the one new tanning booth based on the following historical data:

What is the monthly rate of change (slope) of the least squares trend line for these data?

A. B. C. D. E.

320 102 8 -.4 -8

Treat the earliest period as period 0, then formulate the least squares slope.

AACSB: Analytic Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-120 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

134. The owner of Darkest Tans Unlimited in a local mall is forecasting this month's (October's) demand for the one new tanning booth based on the following historical data:

What is this month's forecast using the least squares trend line for these data?

A. B. C. D. E.

1,250 128.6 102 158 164

Treat the earliest period as period 0, then formulate the least squares coefficients and proceed.

AACSB: Analytic Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

135. Which of the following mechanisms for enhancing profitability is most likely to result from improving short-term forecast performance?

A. B. C. D. E.

increased inventory reduced flexibility higher-quality products greater customer satisfaction greater seasonality

Short-term forecast performance won't necessarily improve product quality, but it does allow firms to better satisfy their customers.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting 3-121 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

technique. Level of Difficulty: 2 Medium Topic: Operations Strategy

136. Which of the following changes would tend to shorten the time frame for short-term forecasting?

A. B. C. D. E.

bringing greater variety into the product mix increasing the flexibility of the production system ordering fewer weather-sensitive items adding more special-purpose equipment investing in the production system to make it more task-specific

An increasingly flexible system permits more rapid responses to changing conditions, which allows firms to reduce their forecast time horizon.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-16 Describe the key factors and trade-offs to consider when choosing a forecasting technique. Level of Difficulty: 2 Medium Topic: Operations Strategy

137. Which of the following helps improve supply chain forecasting performance?

A. contracts that require supply chain members to formulate long-term forecasts B. penalties for supply chain members that adjust forecasts C. sharing forecasts or demand data across the supply chain D. increasing lead times for critical supply chain members E. increasing the number of suppliers at critical junctures in the supply chain Sharing forecasts and/or demand data is a means of ensuring that the supply chain's overall forecast is as accurate as it can be.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-03 List the elements of a good forecast. Level of Difficulty: 1 Easy Topic: Forecasting and the Supply Chain

3-122 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

138. Which of the following would tend to decrease forecast accuracy?

A. a reduction in demand variability B. a shortening of the forecast time horizon C. an attempt to forecast demand for a group of similar items rather than an individual item D. a change in the underlying causal system Forecasting techniques generally assume that the same underlying causal system that existed in the past will continue to exist in the future.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-02 Explain why forecasts are generally wrong. Level of Difficulty: 2 Medium Topic: Forecasting and the Supply Chain

139. Which of the following is the most valuable piece of information the sales force can bring into forecasting situations?

A. what customers are most likely to do in the future B. what customers most want to do in the future C. what customers' future plans are D. whether customers are satisfied or dissatisfied with their performance in the past E. what the salesperson's appropriate sales quota should be Knowledge about what customers are likely to do is much more valuable than information regarding what customers plan or want to do.

AACSB: Reflective Thinking Accessibility: Keyboard Navigation Blooms: Understand Learning Objective: 03-06 Describe four qualitative forecasting techniques. Level of Difficulty: 2 Medium Topic: Qualitative Forecasts

Essay Questions

3-123 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

140. Develop a forecast for the next period, given the data below, using a three-period moving average.

Feedback: Average demand from periods 3 through 5.

AACSB: Analytic Blooms: Apply Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-124 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

141. Consider the data below:

Using exponential smoothing with alpha = .2, and assuming the forecast for period 11 was 80, what would the forecast for period 14 be?

Feedback: The forecast error in period 13 (2.84) is multiplied by the smoothing constant. This is then added to the period 13 forecast to get the period 14 forecast.

AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

142. A manager is using exponential smoothing to predict merchandise returns at a suburban branch of a department store chain. Given a previous forecast of 140 items, an actual number of returns of 148 items, and a smoothing constant equal to .15, what is the forecast for the next period?

Feedback: The forecast error in the previous period is multiplied by the smoothing constant. This is then added to the previous period's forecast to get the upcoming period's forecast.

AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data 3-125 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

143. A manager is using the equation below to forecast quarterly demand for a product: Yt = 6,000 + 80t where t = 0 at Q2 of last year Quarter relatives are Q1 = .6, Q2 = .9, Q3 = 1.3, and Q4 = 1.2. What forecasts are appropriate for the last quarter of this year and the first quarter of next year?

For Q4 of this year t = 6 For Q1 of next year t = 7

Feedback: Adjust deseasonalized forecasts by the quarterly seasonal relatives.

AACSB: Analytic Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

144. Over the past five years, a firm's sales have averaged 250 units in the first quarter of each year, 100 units in the second quarter, 150 units in the third quarter, and 300 units in the fourth quarter. What are appropriate quarter relatives for this firm's sales? Hint: Only minimal computations are necessary.

Feedback: Since a trend is not present, quarter relatives are simply a percentage of average, which is 200 units.

AACSB: Analytic Blooms: Apply Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-126 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

145. A manager has been using a certain technique to forecast demand for gallons of ice cream for the past six periods. Actual and predicted amounts are shown below. Would a naive forecast have produced better results?

Current method: MAD = 3.67; MSE = 16.8; 2s control limits ± 8.2 (OK) Naive method: MAD = 4.40; MSE = 30.0; 2s control limits ± 11.0 (OK) Feedback: Either MSE or MAD should be computed for both forecasts and compared. The demand data are stable. Therefore, the most recent value of the series is a reasonable forecast for the next period of time, justifying the naive approach. The current method is slightly superior both in terms of MAD and MSE. Either method would be considered in control.

AACSB: Analytic Blooms: Apply Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 3 Hard Topic: Forecast Accuracy

3-127 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

146. A new car dealer has been using exponential smoothing with an alpha of .2 to forecast weekly new car sales. Given the data below, would a naive forecast have provided greater accuracy? Explain. Assume an initial exponential forecast of 60 units in period 2 (i.e., no forecast for period 1).

Exponential method: MAD = 1.70; MSE = 6.34 Naive method: MAD = 3.00; MSE = 15.25 Feedback: The exponential forecast method appears to be superior because both MAD and MSE are lower when it is used.

AACSB: Analytic Blooms: Apply Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 3 Hard Topic: Forecast Accuracy

3-128 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

147. A CPA firm has been using the following equation to predict annual demand for tax audits: Yt = 55 + 4t. Demand for the past few years is shown below. Is the forecast performing as well as it might? Explain.

MSE = 11/6 and s = = 3.41. Even with ± 2s limits (6.82), all values are within the limits. It seems, then, that only random variation is present, so one might say that the forecast is working. One might also observe that the first three errors are negative and the last three are positive. Although six observations constitute a relatively small sample, it may be that the errors are cycling, and this would be a matter to investigate with additional data. Feedback: Either a tracking signal or a control chart is called for. To conduct these assessments, it is necessary to generate the forecasts so that errors can be determined.

AACSB: Analytic Blooms: Apply Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-129 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

148. Given the data below, develop a forecast for period 6 using a four-period weighted moving average and weights of .4, .3, .2 and .1.

.4(17) + .3(19) + .2(18) + .1(20) = 18.1 Feedback: Multiply demand observed in periods 2 through 5 by the appropriate weight, then sum these products.

AACSB: Analytic Blooms: Apply Learning Objective: 03-09 Prepare a weighted-average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-130 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

149. Use linear regression to develop a predictive model for demand for burial vaults based on sales of caskets.

Feedback: Least-squares estimation leads to this regression equation.

AACSB: Analytic Blooms: Apply Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 3 Hard Topic: Associative Forecasting Techniques

3-131 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

150. Given the following data, develop a linear regression model for y as a function of x.

Feedback: Least squares estimation leads to this regression equation.

AACSB: Analytic Blooms: Apply Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 2 Medium Topic: Associative Forecasting Techniques

3-132 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

151. Given the following data, develop a linear regression model for y as a function of x.

Feedback: Least squares estimation leads to this regression equation.

AACSB: Analytic Blooms: Apply Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 3 Hard Topic: Associative Forecasting Techniques

3-133 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

152. Develop a linear trend equation for the data on bread deliveries shown below. Forecast deliveries for period 11 through 14.

Yt = 518.2 + 52.164t r = +.935

Feedback: Formulate the regression equation using least squares estimation, then apply the result to periods 11 through 14.

AACSB: Analytic Blooms: Apply Learning Objective: 03-14 Compute and use regression and correlation coefficients. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-134 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

153. Demand for the last four months was:

A) Predict demand for July using each of these methods: 1) a three-period moving average 2) exponential smoothing with alpha equal to .20 (use a naive forecast for April for your first forecast) B) If the naive approach had been used to predict demand for April through June, what would MAD have been for those months?

Feedback: The naive approach leads to absolute forecast errors of two units in each period.

AACSB: Analytic Blooms: Apply Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-135 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

154. A manager wants to choose one of two forecasting alternatives. Each alternative was tested using historical data. The resulting forecast errors for the two are shown in the table. Analyze the data and recommend a course of action to the manager.

Although Alternative 1 has the smaller MSE, it appears to be cycling and steady; Alternative 2 errors after the first three periods are small or zero. For the last six periods, Alternative 2 was much better, suggesting that approach would be better. Feedback: Although Alternative 1 has the smaller MSE, it appears to be cycling and steady; Alternative 2 errors after the first three periods are small or zero. For the last six periods, Alternative 2 was much better, suggesting that approach would be better.

AACSB: Analytic Blooms: Apply Learning Objective: 03-05 Summarize forecast errors and use summaries to make decisions. Level of Difficulty: 3 Hard Topic: Forecast Accuracy

3-136 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

155. A manager uses this equation to predict demand: Yt = 20 + 4t. Over the past eight periods, demand has been as follows. Are the results acceptable? Explain.

s = 2.10; 2s control limits are ± 4.20. Although all values are within control limits, the errors may be exhibiting cyclical patterns, which would suggest nonrandomness. Feedback: s = 2.10; 2s control limits are ± 4.20. Although all values are within control limits, the errors may be exhibiting cyclical patterns, which would suggest nonrandomness.

AACSB: Analytic Blooms: Apply Learning Objective: 03-15 Construct control charts and use them to monitor forecast errors. Level of Difficulty: 2 Medium Topic: Approaches to Forecasting

3-137 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

156. Data on demand over the last few years are available as follows:

What would this year's forecast be if we were using the naive approach?

49 Feedback: This year's forecast would be last year's demand.

AACSB: Analytic Blooms: Apply Learning Objective: 03-07 Use a naive method to make a forecast. Level of Difficulty: 1 Easy Topic: Forecasts Based on Time-Series Data

3-138 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

157. Data on demand over the last few years are available as follows:

What is this year's forecast using a four-year simple moving average?

45.5 Feedback: Average the four most recent periods of demand.

AACSB: Analytic Blooms: Apply Learning Objective: 03-08 Prepare a moving average forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-139 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

158. Data on demand over the last few years are available as follows:

What is this year's forecast using exponential smoothing with alpha = .25, if last year's smoothed forecast was 45?

45.8 Feedback: Multiply last year's forecast error by the smoothing constant. Add the resulting product to last year's forecast to get this year's forecast.

AACSB: Analytic Blooms: Apply Learning Objective: 03-10 Prepare an exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

3-140 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

159. Data on demand over the last few years are available as follows:

What are this and next year's forecasts using the least squares trend line for these data?

62; 69 Feedback: Treat the earliest period as period 0 in formulating least squares coefficients, then proceed.

AACSB: Analytic Blooms: Apply Learning Objective: 03-11 Prepare a linear trend forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-141 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

160. Data on demand over the last few years are available as follows:

What is this year's forecast using trend-adjusted (double) smoothing with alpha = . 2 and beta = .1, if the forecast for last year was 56, the forecast for two years ago was 46, and the trend estimate for last year's forecast was 7?

61.76 Feedback: Smooth both the trend and the forecasts using the appropriate smoothing coefficients.

AACSB: Analytic Blooms: Apply Learning Objective: 03-12 Prepare a trend-adjusted exponential smoothing forecast. Level of Difficulty: 3 Hard Topic: Forecasts Based on Time-Series Data

3-142 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

161. Data on the last three years of demand are available as follows:

What is the centered moving average for spring two years ago?

29 Feedback: First average the four periods beginning fall three years ago. Then average the four periods beginning spring two years ago. Then average these two averages.

AACSB: Analytic Blooms: Apply Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

162. Data on the last three years of demand are available as follows:

What is the spring's seasonal relative?

Spring = 0.91 Feedback: Divide data points by centered moving averages where moving averages are available. Average the resulting values across the seasons to get the seasonal relatives.

AACSB: Analytic Blooms: Apply Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data 3-143 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

163. Data on the last three years of demand are available as follows:

What is the linear regression trend line for these data (t = 0 for spring, three years ago)?

y = 17 + 2.33t Feedback: Used deseasonalized data points to formulate least squares coefficients.

AACSB: Analytic Blooms: Apply Learning Objective: 03-12 Prepare a trend-adjusted exponential smoothing forecast. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data

164. Data on the last three years of demand are available as follows:

What is this year's seasonally adjusted forecast for each season?

Spring = 40.93; Summer = 29.81; Fall = 51.14; Winter = 74.37 Feedback: First forecast each period's deseasonalized value (e.g., Spring is period 12). Then multiply the deseasonalized forecast by the appropriate seasonal relative.

AACSB: Analytic Blooms: Apply Learning Objective: 03-13 Compute and use seasonal relatives. Level of Difficulty: 2 Medium Topic: Forecasts Based on Time-Series Data 3-144 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

3-145 Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

Thank you for interesting in our services. We are a non-profit group that run this website to share documents. We need your help to maintenance this website.