(Not: Bazı problemlerde bulunan “.” lı sayılarda, noktadan önce sıfır rakamı bulunmalıdır.)

Chapter 3 Forecasting Questions

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

2. 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

3. Forecasts help managers plan both the system itself and provide valuable information for using the system. TRUE

4. Organizations that are capable of responding quickly to changing requirements can use a shorter forecast horizon and therefore benefit from more accurate forecasts. TRUE

5. When new products or services are introduced, focus forecasting models are an attractive option. FALSE

6. Time series techniques involve identification of explanatory variables that can be used to predict future demand. FALSE

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

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

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

10. The naive approach to forecasting requires a linear trend line. FALSE

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

12. 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

13. 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

14. Forecasts of future demand are used by operations people to plan capacity. TRUE

15. An advantage of a weighted moving average is that recent actual results can be given more importance than what occurred a while ago. TRUE

16. Exponential smoothing is a form of weighted averaging. TRUE

17. MAD is equal to the square root of MSEFALSE

18. In exponential smoothing, an alpha of 1.0 will generate the same forecast that a naïve forecast would yield. TRUE

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

20. In business, forecasts are the basis for: A. capacity planningB. budgetingC. sales planningD. production planningE. all of the above

21. Which of the following features would not generally be considered common to all forecasts? A. Assumption of a stable underlying causal systemB. 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.

22. Which of the following is not a step in the forecasting process? A. determine the purpose and level of detail requiredB. eliminate all assumptionsC. establish a time horizonD. select a forecasting modelE. monitor the forecast

23. The two general approaches to forecasting are: A. mathematical and statisticalB. qualitative and quantitativeC. judgmental and qualitativeD. historical and associativeE. precise and approximation

24. Which of the following is not a type of judgmental forecasting? A. executive opinionsB. sales force opinionsC. consumer surveysD. the Delphi methodE. time series analysis

25. 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.

26. Gradual, long-term movement in time series data is called: A. seasonal variationB. cyclesC. irregular variationD. trendE. random variation

27. The primary difference between seasonality and cycles is: A. the duration of the repeating patternsB. the magnitude of the variationC. the ability to attribute the pattern to a causeD. the direction of the movementE. there are only 4 seasons but 30 cycles

28. Averaging techniques are useful for: A. distinguishing between random and non-random variationsB. smoothing out fluctuations in time seriesC. eliminating historical dataD. providing accuracy in forecastsE. average people

29. Using the latest observation in a sequence of data to forecast the next period is: A. a moving average forecastB. a naive forecastC. an exponentially smoothed forecastD. an associative forecastE. regression analysis

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

 A. 58B. 62C. 59.5D. 61E. cannot tell from the data given

31. Moving average forecasting techniques do the following: A. immediately reflect changing patterns in the dataB. lead changes in the dataC. smooth variations in the dataD. operate independently of recent dataE. assist when organizations are relocating

32. Which is not a characteristic of simple moving averages applied to time series data? A. smoothes random variations in the dataB. weights each historical value equallyC. lags changes in the dataD. requires only last period's forecast and actual dataE. smoothes real variations in the data

33. 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. decreasedB. increasedC. multiplied by a larger alphaD. multiplied by a smaller alphaE. eliminated if the MAD is greater than the MSE

34. A forecast based on the previous forecast plus a percentage of the forecast error is: A. a naive forecastB. a simple moving average forecastC. a centered moving average forecastD. an exponentially smoothed forecastE. an associative forecast

35. Which is not a characteristic of exponential smoothing? A. smoothes random variations in the dataB. weights each historical value equallyC. has an easily altered weighting schemeD. has minimal data storage requirementsE. smoothes real variations in the data

36. Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast? A. 0B. .01C. .1D. .5E. 1.0

37. Given an actual demand of 59, a previous forecast of 64, and an alpha of .3, what would the forecast for the next period be using simple exponential smoothing? A. 36.9B. 57.5C. 60.5D. 62.5E. 65.5

38. A manager uses the following equation to predict monthly receipts: Yt= 40,000 + 150t. What is the forecast for July if t = 0 in April of this year? A. 40,450B. 40,600C. 42,100D. 42,250E. 42,400

39. A persistent tendency for forecasts to be greater than or less than the actual values is called: A. biasB. trackingC. control chartingD. positive correlationE. linear regression

40. The mean absolute deviation (MAD) is used to: A. estimate the trend lineB. eliminate forecast errorsC. measure forecast accuracyD. seasonally adjust the forecastE. all of the above

41. Given forecast errors of 4, 8, and – 3, what is the mean absolute deviation? A. 4B. 3C. 5D. 6E. 12

42. Given forecast errors of 5, 0, – 4, and 3, what is the mean absolute deviation? A. 4B. 3C. 2.5D. 2E. 1

43. The two most important factors in choosing a forecasting technique are: A. cost and time horizonB. accuracy and time horizonC. cost and accuracyD. quantity and qualityE. objective and subjective components

44. Which of the following is not necessarily an element of a good forecast? A. estimate of accuracyB. timelinessC. meaningful unitsD. low costE. written

45. Current information on _________ can have a significant impact on forecast accuracy: A. pricesB. promotionC. inventoryD. competitionE. all of the above

46. Customer service levels can be improved by better: A. mission statementsB. control chartingC. short term forecast accuracyD. exponential smoothingE. customer selection

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

 A. 67B. 115C. 69D. 68E. 68.67

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

 A. 144.20B. 144.80C. 144.67D. 143.00E. 144.00

49. Given forecast errors of – 5, – 10, and +15, the MAD is: A. 0B. 10C. 30D. 175E. none of these

50. Develop a forecast for the next period, given the data below, using a 3-period moving average.

 

51. 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? 

52. 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? 

53. 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?

 

Essentially, the student must recognize that 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 naïve approach. Some students may also elect to compute control limits to see if the forecasts are in control.

Summary:

Current method: MAD = 3.67; MSE =16.8; 2s Control limits 8.2 (OK)Naïve method: MAD = 4.40; MSE = 30.0; 2s Control limits 11.0 (OK)The current method is slightly superior both in terms of MAD and MSE. Either method would be considered in control.

54. 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).

 

Summary:Exponential method: MAD = 1.70; MSE = 6.34Naïve method: MAD = 3.00; MSE = 15.25The exponential forecast method appears to be superior

55. 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

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

 

Yt = 518.2 + 52.164tr = +.935

57. Demand for the last four months was:

A) Predict demand for July using each of these methods:1) a 3-period moving average2) exponential smoothing with alpha equal to .20 (use a naïve 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? 

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

58. What is the forecast for this year using a four-year simple moving average? A. 18,750B. 19,500C. 21,000D. 22,650E. 22,800

59. What is the forecast for this year using exponential smoothing with alpha = 0.5, if the forecast for two years ago was 16,000? A. 18,750B. 19,500C. 21,000D. 22,650E. 22,800

60. What is the forecast for this year using the least squares trend line for these data? A. 18,750B. 19,500C. 21,000D. 22,650E. 22,800

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

 61. What is the forecast for this year using the naive approach? A. 163B. 180C. 300D. 420E. 510

62. What is the forecast for this year using a three-year weighted moving average with weights of .5, .3, and .2? A. 163B. 180C. 300D. 420E. 510

63. What is the forecast for this year using exponential smoothing with alpha = .4, if the forecast for two years ago was 750? A. 163B. 180C. 300D. 420E. 510

64. What is the forecast for this year using the least squares trend line for these data? A. 163B. 180C. 300D. 420E. 510

 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:

 

65. What is this week's forecast using the naive approach? A. 45B. 50C. 52D. 65E. 78

66. What is this week's forecast using a three-week simple moving average? A. 49B. 50C. 52D. 65E. 78

67. What is this week's forecast using exponential smoothing with alpha = .2, if the forecast for two weeks ago was 90? A. 49B. 50C. 52D. 65E. 77

68. What is this week's forecast using the least squares trend line for these data? A. 49B. 50C. 52D. 65E. 78

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

 

69. What is this year's forecast using the naive approach? A. 22,000B. 20,000C. 18,000D. 15,000E. 12,000

70. What is this year's forecast using a two-year weighted moving average with weights of .7 and .3? A. 19,400B. 18,600C. 19,000D. 11,400E. 10,600

71. What is this year's forecast using the least squares trend line for these data? A. 20,000B. 21,000C. 22,000D. 23,000E. 24,000

 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:

72. What is this year's forecast using the naive approach? A. 2,000B. 2,200C. 2,800D. 3,000E. none of the above

73. What is this year's forecast using a three-year simple moving average? A. 2,667B. 2,600C. 2,500D. 2,400E. 2,333

74. What is this year's forecast using exponential smoothing with alpha = .4, if last year's smoothed forecast was 2600? A. 2,600B. 2,760C. 2,800D. 3,840E. 3,000

75. What is this year's forecast using the least squares trend line for these data? A. 3,600B. 3,500C. 3,400D. 3,300E. 3,200

 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:

 

76. What is this month's forecast using the naive approach? A. 100B. 160C. 130D. 140E. 120

77. What is this month's forecast using a four-month weighted moving average with weights of .4, .3, .2, and .1? A. 120B. 129C. 141D. 135E. 140

78. What is this month's forecast using exponential smoothing with alpha = .2, if August's forecast was 145? A. 144B. 140C. 142D. 148E. 163

79. What is this month's forecast using the least squares trend line for these data? A. 1,250B. 128.6C. 102D. 158E. 164

 

 

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

49

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

(45.5)

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

(45.8)

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

(62; 69)