Chap 011

Chapter 11 - Demand Management and Forecasting

Chapter 11Demand Management and Forecasting

Learning Objectives for Chapter 11:

1. Understand the role of forecasting as a basis for supply chain planning.

2. Compare the differences between independent and dependent demand.

3. Identify the basic components of independent demand: average, trend, seasonal, and random variation.

4. Describe the common qualitative forecasting techniques such as the Delphi method and Collaborative Forecasting.

5. Show how to make a time series forecast using regression, moving averages, and exponential smoothing.

6. Use decomposition to forecast when trend and seasonality is present.

True / False Questions

1. Continual review and updating in light of new data is a forecasting technique called second-guessing. True False

2. Independent demand is the demand for a product or service caused by the demand for other products or services. True False

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3. There is not much that a firm can do to influence independent demand. True False

4. Cyclical influences on demand are often expressed graphically as a linear function that is either upward or downward sloping. True False

5. Cyclical influences on demand may come from occurrences such as political elections, war or economic conditions. True False

6. Trend lines are usually the last things considered when developing a forecast. True False

7. Time series forecasting models make predictions about the future based on analysis of past data. True False

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8. In the weighted moving average forecasting model the weights must add up to one times the number of data points. True False

9. In a forecasting model using simple exponential smoothing the data pattern should remain stationary. True False

10. In a forecasting model using simple moving average the shorter the time span used for calculating the moving average, the closer the average follows volatile trends. True False

11. In the simple exponential smoothing forecasting model you need at least 100 observations to set the weight. True False

12. Experience and trial and error are the simplest ways to choose weights for the weighted moving average forecasting model. True False

13. The weighted moving average forecasting model uses a weighting scheme to modify the effects of individual data points. This is its major advantage over the simple moving average model. True False

14. A central premise of exponential smoothing is that more recent data is less indicative of the future than data from the distant past. True False

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15. The equation for exponential smoothing states that the new forecast is equal to the old forecast plus the error of the old forecast. True False

16. Exponential smoothing is always the most accurate of all forecasting models. True False

17. In exponential smoothing, it is desirable to use a higher smoothing constant when forecasting demand for a product experiencing high growth. True False

18. The exponential smoothing model permits non-linear forecast values. True False

19. The weighted moving average model does not work with non-linear forecast values. True False

20. The simple moving average model permits non-linear forecast values. True False

21. The simple moving average model requires linear forecast values. True False

22. The value of the smoothing constant alpha in an exponential smoothing model is between 0 and 1. True False

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23. Simple exponential smoothing lags changes in demand. True False

24. Exponential smoothing forecasts always lag behind the actual occurrence but can be corrected somewhat with a trend adjustment. True False

25. Because the factors governing demand for products are very complex, all forecasts of demand contain some error. True False

26. Random errors can be defined as those that cannot be explained by the forecast model being used. True False

27. Random errors in forecasting occur when an undetected secular trend is not included in a forecasting model. True False

28. When forecast errors occur in a normally distributed pattern, the ratio of the mean absolute deviation to the standard deviation is 2 to 1, or 2 MAD = 1 standard deviation. True False

29. MAD statistics can be used to generate tracking signals. True False

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30. RSFE in forecasting stands for "reliable safety function error." True False

31. RSFE in forecasting stands for "running sum of forecast errors." True False

32. A tracking signal (TS) can be calculated using the arithmetic sum of forecast deviations divided by the MAD. True False

33. A major limitation of linear regression as a model for forecasting is that past data and future projections are assumed to fall on or near a straight line. True False

34. Regression is a functional relationship between two or more correlated variables, where one variable is used to predict another. True False

35. Linear regression is not useful for aggregate planning. True False

36. The standard error of the estimate of a linear regression is not useful for judging the fit between the data and the regression line when doing forecasts. True False

37. Multiple regression analysis uses several regression models to generate a forecast. True False

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38. For every forecasting problem there is one best forecasting technique. True False

39. A good forecaster is one who develops special skills and experience at one forecasting technique and is capable of applying it to widely diverse situations. True False

40. In causal relationship forecasting leading indicators are used to forecast occurrences. True False

41. Qualitative forecasting techniques generally take advantage of the knowledge of experts and therefore do not require much judgment. True False

42. Market research is a quantitative method of forecasting. True False

43. Decomposition of a time series means identifying and separating the time series data into its components. True False

44. A time series is defined in the text as chronologically ordered data that may contain one or more components of demand variation: trend, seasonal, cyclical, autocorrelation, and random. True False

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45. It is difficult to identify the trend in time series data. True False

46. In decomposition of time series data it is relatively easy identify cycles and autocorrelation components. True False

47. We usually associate the word "seasonal" with recurrent periods of repetitive activity that happen on other than an annual cycle. True False

Multiple Choice Questions

48. In time series data depicting demand which of the following is not considered a component of demand variation? A. TrendB. SeasonalC. CyclicalD. VarianceE. Autocorrelation

49. Which of the following is not one of the basic types of forecasting? A. QualitativeB. Time series analysisC. Causal relationshipsD. SimulationE. Force field analysis

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50. In most cases, demand for products or services can be broken into several components. Which of the following is not considered a component of demand? A. Average demand for a periodB. A trendC. Seasonal elementsD. Past demandE. Autocorrelation

51. In most cases, demand for products or services can be broken into several components. Which of the following is considered a component of demand? A. Cyclical elementsB. Future demandC. Past demandD. Inconsistent demandE. Level demand

52. In most cases, demand for products or services can be broken into several components. Which of the following is considered a component of demand? A. Forecast errorB. AutocorrelationC. Previous demandD. Consistent demandE. Repeat demand

53. Which of the following forecasting methodologies is considered a qualitative forecasting technique? A. Simple moving averageB. Market researchC. Linear regressionD. Exponential smoothingE. Multiple regression

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54. Which of the following forecasting methodologies is considered a qualitative forecasting technique? A. Market researchB. Causal relationship forecastingC. Regression analysisD. Exponential smoothingE. Simple moving average

55. Which of the following forecasting methodologies is considered a time series forecasting technique? A. Simple moving averageB. Market researchC. Leading indicatorsD. Historical analogyE. Simulation

56. Which of the following forecasting methodologies is considered a time series forecasting technique? A. Delphi methodB. Exponential averagingC. Simple movement smoothingD. Weighted moving averageE. Simulation

57. Which of the following forecasting methodologies is considered a causal forecasting technique? A. Exponential smoothingB. Weighted moving averageC. Linear regressionD. Historical analogyE. Market research

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58. Which of the following forecasting methods uses executive judgment as its primary component for forecasting? A. Historical analogyB. Time series analysisC. Panel consensusD. Market researchE. Linear regression

59. Which of the following forecasting methods is very dependent on selection of the right individuals who will judgmentally be used to actually generate the forecast? A. Time series analysisB. Simple moving averageC. Weighted moving averageD. Delphi methodE. Panel consensus

60. In business forecasting, what is usually considered a short-term time period? A. Four weeks or lessB. More than three monthsC. Six months or moreD. Less than three monthsE. One year

61. In business forecasting, what is usually considered a medium-term time period? A. Six weeks to one yearB. Three months to two yearsC. One to five yearsD. One to six monthsE. Six months to six years

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62. In business forecasting, what is usually considered a long-term time period? A. Three months or longerB. Six months or longerC. One year or longerD. Two years or longerE. Ten years or longer

63. In general, which forecasting time frame compensates most effectively for random variation and short term changes? A. Short-term forecastsB. Quick-time forecastsC. Long range forecastsD. Medium term forecastsE. Rapid change forecasts

64. In general, which forecasting time frame best identifies seasonal effects? A. Short-term forecastsB. Quick-time forecastsC. Long range forecastsD. Medium term forecastsE. Rapid change forecasts

65. In general, which forecasting time frame is best to detect general trends? A. Short-term forecastsB. Quick-time forecastsC. Long range forecastsD. Medium term forecastsE. Rapid change forecasts

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66. Which of the following forecasting methods can be used for short-term forecasting? A. Simple exponential smoothingB. Delphi techniqueC. Market researchD. Hoskins-Hamilton smoothingE. Serial regression

67. Which of the following considerations is not usually a factor in deciding which forecasting model a firm should choose? A. Time horizon to forecastB. ProductC. Accuracy requiredD. Data availabilityE. Analyst sophistication

68. A company wants to forecast demand using the simple moving average. If the company uses four prior yearly sales values (i.e., year 2007 = 100, year 2008 = 120, year 2009 = 140, and year 2010 = 210), which of the following is the simple moving average forecast for year 2011? A. 100.5B. 140.0C. 142.5D. 145.5E. 155.0

69. A company wants to forecast demand using the simple moving average. If the company uses three prior yearly sales values (i.e., year 2008 = 130, year 2009 = 110, and year 2010 =160), which of the following is the simple moving average forecast for year 2011? A. 100.5B. 122.5C. 133.3D. 135.6E. 139.3

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70. A company wants to forecast demand using the weighted moving average. If the company uses two prior yearly sales values (i.e., year 2009 = 110 and year 2010 = 130), and we want to weight year 2009 at 10% and year 2010 at 90%, which of the following is the weighted moving average forecast for year 2011? A. 120B. 128C. 133D. 138E. 142

71. A company wants to forecast demand using the weighted moving average. If the company uses three prior yearly sales values (i.e., year 2008 = 160, year 2009 = 140 and year 2010 = 170), and we want to weight year 2008 at 30%, year 2009 at 30% and year 2010 at 40%, which of the following is the weighted moving average forecast for year 2011? A. 170B. 168C. 158D. 152E. 146

72. Which of the following is the major reason that exponential smoothing has become well accepted as a forecasting technique? A. AccuracyB. Sophistication of analysisC. Predicts turning pointsD. Ease of useE. Ability to Forecast lagging data trends

73. The exponential smoothing method requires which of the following data to forecast the future? A. The most recent forecastB. Precise actual demand for the past several yearsC. The value of the smoothing constant deltaD. Overall industry demand dataE. Tracking values

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74. Given a prior forecast demand value of 230, a related actual demand value of 250, and a smoothing constant alpha of 0.1, what is the exponential smoothing forecast value for the following period? A. 230B. 232C. 238D. 248E. 250

75. If a firm produced a standard item with relatively stable demand, the smoothing constant alpha used in an exponential smoothing forecasting model would tend to be in which of the following ranges? A. 5 % to 10 %B. 20 % to 50 %C. 20 % to 80 %D. 60 % to 120 %E. 90 % to 100 %

76. If a firm produced a product that is experiencing growth in demand, the smoothing constant alpha used in an exponential smoothing forecasting model would tend to be which of the following? A. Close to zeroB. A very low percentage, less than 10%C. The more rapid the growth, the higher the percentageD. The more rapid the growth, the lower the percentageE. 50 % or more

77. Given a prior forecast demand value of 1,100, a related actual demand value of 1,000, and a smoothing constant alpha of 0.3, what is the exponential smoothing forecast value? A. 1,000B. 1,030C. 1,070D. 1,130E. 970

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78. A company wants to generate a forecast for unit demand for year 2011 using exponential smoothing. The actual demand in year 2010 was 120. The forecast demand in year 2010 was 110. Using this data and a smoothing constant alpha of 0.1, which of the following is the resulting year 2011 forecast value? A. 100B. 110C. 111D. 114E. 120

79. As a consultant you have been asked to generate a unit demand forecast for a product for year 2011 using exponential smoothing. The actual demand in year 2010 was 750. The forecast demand in year 2010 was 960. Using this data and a smoothing constant alpha of 0.3, which of the following is the resulting year 2008 forecast value? A. 766B. 813C. 897D. 1,023E. 1,120

80. Which of the following is a possible source of bias error in forecasting? A. Failing to include the right variablesB. Using the wrong forecasting methodC. Employing less sophisticated analysts than necessaryD. Using incorrect dataE. Using standard deviation rather than MAD

81. Which of the following is used to describe the degree of error? A. Weighted moving averageB. RegressionC. Moving averageD. Forecast as a percent of actualE. Mean absolute deviation

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82. A company has actual unit demand for three consecutive years of 124, 126, and 135. The respective forecasts for the same three years are 120, 120, and 130. Which of the following is the resulting MAD value that can be computed from this data? A. 1B. 3C. 5D. 15E. 123

83. A company has actual unit demand for four consecutive years of 100, 105, 135, and 150. The respective forecasts were 120 for all four years. Which of the following is the resulting MAD value that can be computed from this data? A. 2.5B. 10C. 20D. 22.5E. 30

84. If you were selecting a forecasting model based on MAD, which of the following MAD values reflects the most accurate model? A. 0.2B. 0.8C. 1.0D. 10.0E. 100.0

85. A company has calculated its running sum of forecast errors to be 500 and its mean absolute deviation is exactly 35. Which of the following is the company's tracking signal? A. Cannot be calculated based on this informationB. About 14.3C. More than 35D. Exactly 35E. About 0.07

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86. A company has a MAD of 10. It wants to have a 99.7 percent control limits on its forecasting system. Its most recent tracking signal value is 31. What can the company conclude from this information? A. The forecasting model is operating acceptablyB. The forecasting model is out of control and needs to be correctedC. The MAD value is incorrectD. The upper control value is less than 20E. It is using an inappropriate forecasting methodology

87. You are hired as a consultant to advise a small firm on forecasting methodology. Based on your research you find the company has a MAD of 3. It wants to have a 99.7 percent control limits on its forecasting system. Its most recent tracking signal value is 15. What should be your report to the company? A. The forecasting model is operating acceptablyB. The forecasting model is out of control and needs to be correctedC. The MAD value is incorrectD. The upper control value is less than 20E. The company is using an inappropriate forecasting methodology

88. Which of the following is the portion of observations you would expect to see lying within a plus or minus 3 MAD range? A. 57.048 percentB. 88.946 percentC. 98.334 percentD. 99.856 percentE. 100 percent

89. Which of the following is the portion of observations you would expect to see lying within a plus or minus 2 MAD range? A. 57.048B. 88.946C. 98.334D. 99.856E. 100

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90. If the intercept value of a linear regression model is 40, the slope value is 40, and the value of X is 40, which of the following is the resulting forecast value using this model? A. 120B. 1,600C. 1,640D. 2,200E. 64,000

91. A company hires you to develop a linear regression forecasting model. Based on the company's historical sales information, you determine the intercept value of the model to be 1,200. You also find the slope value is -50. If after developing the model you are given a value of X = 10, which of the following is the resulting forecast value using this model? A. -3,800B. 700C. 1,700D. 1,040E. 12,000

92. Heavy sales of umbrellas during a rain storm is an example of which of the following? A. A trendB. A causal relationshipC. A statistical correlationD. A coincidenceE. A fad

93. You are using an exponential smoothing model for forecasting. The running sum of the forecast error statistics (RSFE) are calculated each time a forecast is generated. You find the last RSFE to be 34. Originally the forecasting model used was selected because it's relatively low MAD of 0.4. To determine when it is time to re-evaluate the usefulness of the exponential smoothing model you compute tracking signals. Which of the following is the resulting tracking system? A. 85B. 60C. 13.6D. 12.9E. 8

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Fill in the Blank Questions

94. Name the four basic types of forecasting.1. _____________________;2. _____________________;3. _____________________;4. _____________________. ________________________________________

95. A company has calculated its running sum of forecast errors to be 400 and its mean absolute deviation is exactly 25. What is the company's tracking signal? _____________________. ________________________________________

96. A company has calculated its running sum of forecast errors to be 1,000 and its tracking signal is 50. What is the company's mean absolute deviation? ___________ ________________________________________

97. A company wants to forecast demand using the simple moving average. If the company uses three prior yearly sales values (i.e., year 2008 = 185, year 2009 = 215, and year 2010 =230), what is the simple moving average forecast for year 2011? ____________ ________________________________________

98. A company wants to forecast demand using the weighted moving average. If the company uses two prior yearly sales values (i.e., year 2009 = 11,000 and year 2010 = 13,000), and we want to weight year 2009 at 35% and year 2010 at 65%, what is the weighted moving average forecast for Year 2011? ________________________________________

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99. As a consultant you have been asked to generate a unit demand forecast for a product for Year 2011 using exponential smoothing. Actual demand in year 2010 was 950 but the forecast for that year 1,060. Using this data and a smoothing constant alpha of 0.5, which of the following is the resulting year 2011 forecast value? __________ ________________________________________

100. A company has had actual unit demand for four consecutive years of 100, 110, 125, and 150. The respective forecasts using exponential smoothing were 120 for each of those four years. What value of alpha, the smoothing constant, was the firm using? ___________ ________________________________________

101. What are the five steps of CPFR (collaborative planning, forecasting and replenishment?)1. _____________________;2. _____________________;3. _____________________;4. _____________________;5. _____________________. ________________________________________

102. When analyzing time series data, if demand data contains both seasonal and trend effects at the same time, what are the two ways that they relate to each other discussed in the text? 1) ___________________________2) ___________________________ ________________________________________

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Essay Questions

103. What does the text mean when it states that rather than to search for the perfect forecast one should learn to live with inaccurate forecasts?

104. Distinguish between "dependent" and "independent" demand.

105. Distinguish between errors in statistics and errors in forecasting.

106. Describe the collaborative planning, forecasting and replenishment (CPFR) technique.

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Chapter 11 Demand Management and Forecasting Answer Key

True / False Questions

1. Continual review and updating in light of new data is a forecasting technique called second-guessing. FALSE

AACSB: AnalyticDifficulty: EasyLearning Objective: 1Taxonomy: KnowledgeTopic: Wal-Mart's Data Warehouse

2. Independent demand is the demand for a product or service caused by the demand for other products or services. FALSE

AACSB: AnalyticDifficulty: EasyLearning Objective: 2Taxonomy: KnowledgeTopic: Demand Management

3. There is not much that a firm can do to influence independent demand. FALSE


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4. Cyclical influences on demand are often expressed graphically as a linear function that is either upward or downward sloping. FALSE


5. Cyclical influences on demand may come from occurrences such as political elections, war or economic conditions. TRUE


6. Trend lines are usually the last things considered when developing a forecast. FALSE

AACSB: AnalyticDifficulty: EasyLearning Objective: 5Taxonomy: KnowledgeTopic: Time Series Analysis

7. Time series forecasting models make predictions about the future based on analysis of past data. TRUE


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8. In the weighted moving average forecasting model the weights must add up to one times the number of data points. FALSE


9. In a forecasting model using simple exponential smoothing the data pattern should remain stationary. TRUE


10. In a forecasting model using simple moving average the shorter the time span used for calculating the moving average, the closer the average follows volatile trends. TRUE


11. In the simple exponential smoothing forecasting model you need at least 100 observations to set the weight. FALSE


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12. Experience and trial and error are the simplest ways to choose weights for the weighted moving average forecasting model. TRUE


13. The weighted moving average forecasting model uses a weighting scheme to modify the effects of individual data points. This is its major advantage over the simple moving average model. TRUE


14. A central premise of exponential smoothing is that more recent data is less indicative of the future than data from the distant past. FALSE


15. The equation for exponential smoothing states that the new forecast is equal to the old forecast plus the error of the old forecast. FALSE


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16. Exponential smoothing is always the most accurate of all forecasting models. FALSE


17. In exponential smoothing, it is desirable to use a higher smoothing constant when forecasting demand for a product experiencing high growth. TRUE


18. The exponential smoothing model permits non-linear forecast values. TRUE


19. The weighted moving average model does not work with non-linear forecast values. TRUE


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20. The simple moving average model permits non-linear forecast values. TRUE


21. The simple moving average model requires linear forecast values. FALSE


22. The value of the smoothing constant alpha in an exponential smoothing model is between 0 and 1. TRUE


23. Simple exponential smoothing lags changes in demand. TRUE


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24. Exponential smoothing forecasts always lag behind the actual occurrence but can be corrected somewhat with a trend adjustment. TRUE


25. Because the factors governing demand for products are very complex, all forecasts of demand contain some error. TRUE


26. Random errors can be defined as those that cannot be explained by the forecast model being used. TRUE


27. Random errors in forecasting occur when an undetected secular trend is not included in a forecasting model. FALSE


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28. When forecast errors occur in a normally distributed pattern, the ratio of the mean absolute deviation to the standard deviation is 2 to 1, or 2 MAD = 1 standard deviation. FALSE


29. MAD statistics can be used to generate tracking signals. TRUE


30. RSFE in forecasting stands for "reliable safety function error." FALSE


31. RSFE in forecasting stands for "running sum of forecast errors." TRUE


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32. A tracking signal (TS) can be calculated using the arithmetic sum of forecast deviations divided by the MAD. TRUE


33. A major limitation of linear regression as a model for forecasting is that past data and future projections are assumed to fall on or near a straight line. TRUE


34. Regression is a functional relationship between two or more correlated variables, where one variable is used to predict another. TRUE


35. Linear regression is not useful for aggregate planning. FALSE


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36. The standard error of the estimate of a linear regression is not useful for judging the fit between the data and the regression line when doing forecasts. FALSE


37. Multiple regression analysis uses several regression models to generate a forecast. FALSE


38. For every forecasting problem there is one best forecasting technique. FALSE


39. A good forecaster is one who develops special skills and experience at one forecasting technique and is capable of applying it to widely diverse situations. FALSE


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40. In causal relationship forecasting leading indicators are used to forecast occurrences. FALSE


41. Qualitative forecasting techniques generally take advantage of the knowledge of experts and therefore do not require much judgment. FALSE


42. Market research is a quantitative method of forecasting. FALSE


43. Decomposition of a time series means identifying and separating the time series data into its components. TRUE


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44. A time series is defined in the text as chronologically ordered data that may contain one or more components of demand variation: trend, seasonal, cyclical, autocorrelation, and random. TRUE


45. It is difficult to identify the trend in time series data. FALSE


46. In decomposition of time series data it is relatively easy identify cycles and autocorrelation components. FALSE


47. We usually associate the word "seasonal" with recurrent periods of repetitive activity that happen on other than an annual cycle. FALSE


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Multiple Choice Questions

48. In time series data depicting demand which of the following is not considered a component of demand variation? A. TrendB. SeasonalC. CyclicalD. VarianceE. Autocorrelation


49. Which of the following is not one of the basic types of forecasting? A. QualitativeB. Time series analysisC. Causal relationshipsD. SimulationE. Force field analysis


50. In most cases, demand for products or services can be broken into several components. Which of the following is not considered a component of demand? A. Average demand for a periodB. A trendC. Seasonal elementsD. Past demandE. Autocorrelation


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51. In most cases, demand for products or services can be broken into several components. Which of the following is considered a component of demand? A. Cyclical elementsB. Future demandC. Past demandD. Inconsistent demandE. Level demand


52. In most cases, demand for products or services can be broken into several components. Which of the following is considered a component of demand? A. Forecast errorB. AutocorrelationC. Previous demandD. Consistent demandE. Repeat demand


53. Which of the following forecasting methodologies is considered a qualitative forecasting technique? A. Simple moving averageB. Market researchC. Linear regressionD. Exponential smoothingE. Multiple regression

AACSB: AnalyticDifficulty: EasyLearning Objective: 4Taxonomy: KnowledgeTopic: Qualitative Techniques in Forecasting

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54. Which of the following forecasting methodologies is considered a qualitative forecasting technique? A. Market researchB. Causal relationship forecastingC. Regression analysisD. Exponential smoothingE. Simple moving average

AACSB: AnalyticDifficulty: MediumLearning Objective: 4Taxonomy: KnowledgeTopic: Qualitative Techniques in Forecasting

55. Which of the following forecasting methodologies is considered a time series forecasting technique? A. Simple moving averageB. Market researchC. Leading indicatorsD. Historical analogyE. Simulation


56. Which of the following forecasting methodologies is considered a time series forecasting technique? A. Delphi methodB. Exponential averagingC. Simple movement smoothingD. Weighted moving averageE. Simulation


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57. Which of the following forecasting methodologies is considered a causal forecasting technique? A. Exponential smoothingB. Weighted moving averageC. Linear regressionD. Historical analogyE. Market research

AACSB: AnalyticDifficulty: EasyLearning Objective: 4Learning Objective: 5Taxonomy: UnderstandingTopic: Qualitative Techniques in Forecasting, Time Series Analysis

58. Which of the following forecasting methods uses executive judgment as its primary component for forecasting? A. Historical analogyB. Time series analysisC. Panel consensusD. Market researchE. Linear regression

AACSB: AnalyticDifficulty: EasyLearning Objective: 4Taxonomy: KnowledgeTopic: Qualitative Techniques in Forecasting

59. Which of the following forecasting methods is very dependent on selection of the right individuals who will judgmentally be used to actually generate the forecast? A. Time series analysisB. Simple moving averageC. Weighted moving averageD. Delphi methodE. Panel consensus

AACSB: AnalyticDifficulty: MediumLearning Objective: 4Taxonomy: UnderstandingTopic: Qualitative Techniques in Forecasting

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60. In business forecasting, what is usually considered a short-term time period? A. Four weeks or lessB. More than three monthsC. Six months or moreD. Less than three monthsE. One year

AACSB: AnalyticDifficulty: MediumLearning Objective: 1Taxonomy: KnowledgeTopic: Wal-Mart's Data Warehouse

61. In business forecasting, what is usually considered a medium-term time period? A. Six weeks to one yearB. Three months to two yearsC. One to five yearsD. One to six monthsE. Six months to six years


62. In business forecasting, what is usually considered a long-term time period? A. Three months or longerB. Six months or longerC. One year or longerD. Two years or longerE. Ten years or longer


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63. In general, which forecasting time frame compensates most effectively for random variation and short term changes? A. Short-term forecastsB. Quick-time forecastsC. Long range forecastsD. Medium term forecastsE. Rapid change forecasts

AACSB: AnalyticDifficulty: MediumLearning Objective: 1Taxonomy: UnderstandingTopic: Wal-Mart's Data Warehouse

64. In general, which forecasting time frame best identifies seasonal effects? A. Short-term forecastsB. Quick-time forecastsC. Long range forecastsD. Medium term forecastsE. Rapid change forecasts


65. In general, which forecasting time frame is best to detect general trends? A. Short-term forecastsB. Quick-time forecastsC. Long range forecastsD. Medium term forecastsE. Rapid change forecasts


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66. Which of the following forecasting methods can be used for short-term forecasting? A. Simple exponential smoothingB. Delphi techniqueC. Market researchD. Hoskins-Hamilton smoothingE. Serial regression


67. Which of the following considerations is not usually a factor in deciding which forecasting model a firm should choose? A. Time horizon to forecastB. ProductC. Accuracy requiredD. Data availabilityE. Analyst sophistication


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68. A company wants to forecast demand using the simple moving average. If the company uses four prior yearly sales values (i.e., year 2007 = 100, year 2008 = 120, year 2009 = 140, and year 2010 = 210), which of the following is the simple moving average forecast for year 2011? A. 100.5B. 140.0C. 142.5D. 145.5E. 155.0

AACSB: AnalyticDifficulty: MediumLearning Objective: 5Taxonomy: AnalysisTopic: Time Series Analysis

69. A company wants to forecast demand using the simple moving average. If the company uses three prior yearly sales values (i.e., year 2008 = 130, year 2009 = 110, and year 2010 =160), which of the following is the simple moving average forecast for year 2011? A. 100.5B. 122.5C. 133.3D. 135.6E. 139.3


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70. A company wants to forecast demand using the weighted moving average. If the company uses two prior yearly sales values (i.e., year 2009 = 110 and year 2010 = 130), and we want to weight year 2009 at 10% and year 2010 at 90%, which of the following is the weighted moving average forecast for year 2011? A. 120B. 128C. 133D. 138E. 142


71. A company wants to forecast demand using the weighted moving average. If the company uses three prior yearly sales values (i.e., year 2008 = 160, year 2009 = 140 and year 2010 = 170), and we want to weight year 2008 at 30%, year 2009 at 30% and year 2010 at 40%, which of the following is the weighted moving average forecast for year 2011? A. 170B. 168C. 158D. 152E. 146


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72. Which of the following is the major reason that exponential smoothing has become well accepted as a forecasting technique? A. AccuracyB. Sophistication of analysisC. Predicts turning pointsD. Ease of useE. Ability to Forecast lagging data trends

AACSB: AnalyticDifficulty: MediumLearning Objective: 5Taxonomy: UnderstandingTopic: Time Series Analysis

73. The exponential smoothing method requires which of the following data to forecast the future? A. The most recent forecastB. Precise actual demand for the past several yearsC. The value of the smoothing constant deltaD. Overall industry demand dataE. Tracking values


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74. Given a prior forecast demand value of 230, a related actual demand value of 250, and a smoothing constant alpha of 0.1, what is the exponential smoothing forecast value for the following period? A. 230B. 232C. 238D. 248E. 250

AACSB: AnalyticDifficulty: EasyLearning Objective: 5Taxonomy: AnalysisTopic: Time Series Analysis

75. If a firm produced a standard item with relatively stable demand, the smoothing constant alpha used in an exponential smoothing forecasting model would tend to be in which of the following ranges? A. 5 % to 10 %B. 20 % to 50 %C. 20 % to 80 %D. 60 % to 120 %E. 90 % to 100 %


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76. If a firm produced a product that is experiencing growth in demand, the smoothing constant alpha used in an exponential smoothing forecasting model would tend to be which of the following? A. Close to zeroB. A very low percentage, less than 10%C. The more rapid the growth, the higher the percentageD. The more rapid the growth, the lower the percentageE. 50 % or more

AACSB: AnalyticDifficulty: EasyLearning Objective: 5Taxonomy: UnderstandingTopic: Time Series Analysis

77. Given a prior forecast demand value of 1,100, a related actual demand value of 1,000, and a smoothing constant alpha of 0.3, what is the exponential smoothing forecast value? A. 1,000B. 1,030C. 1,070D. 1,130E. 970


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78. A company wants to generate a forecast for unit demand for year 2011 using exponential smoothing. The actual demand in year 2010 was 120. The forecast demand in year 2010 was 110. Using this data and a smoothing constant alpha of 0.1, which of the following is the resulting year 2011 forecast value? A. 100B. 110C. 111D. 114E. 120


79. As a consultant you have been asked to generate a unit demand forecast for a product for year 2011 using exponential smoothing. The actual demand in year 2010 was 750. The forecast demand in year 2010 was 960. Using this data and a smoothing constant alpha of 0.3, which of the following is the resulting year 2008 forecast value? A. 766B. 813C. 897D. 1,023E. 1,120


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80. Which of the following is a possible source of bias error in forecasting? A. Failing to include the right variablesB. Using the wrong forecasting methodC. Employing less sophisticated analysts than necessaryD. Using incorrect dataE. Using standard deviation rather than MAD


81. Which of the following is used to describe the degree of error? A. Weighted moving averageB. RegressionC. Moving averageD. Forecast as a percent of actualE. Mean absolute deviation


82. A company has actual unit demand for three consecutive years of 124, 126, and 135. The respective forecasts for the same three years are 120, 120, and 130. Which of the following is the resulting MAD value that can be computed from this data? A. 1B. 3C. 5D. 15E. 123


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83. A company has actual unit demand for four consecutive years of 100, 105, 135, and 150. The respective forecasts were 120 for all four years. Which of the following is the resulting MAD value that can be computed from this data? A. 2.5B. 10C. 20D. 22.5E. 30


84. If you were selecting a forecasting model based on MAD, which of the following MAD values reflects the most accurate model? A. 0.2B. 0.8C. 1.0D. 10.0E. 100.0

AACSB: AnalyticDifficulty: EasyLearning Objective: 5Taxonomy: UnderstandingTopic: Time Series Analysis

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85. A company has calculated its running sum of forecast errors to be 500 and its mean absolute deviation is exactly 35. Which of the following is the company's tracking signal? A. Cannot be calculated based on this informationB. About 14.3C. More than 35D. Exactly 35E. About 0.07


86. A company has a MAD of 10. It wants to have a 99.7 percent control limits on its forecasting system. Its most recent tracking signal value is 31. What can the company conclude from this information? A. The forecasting model is operating acceptablyB. The forecasting model is out of control and needs to be correctedC. The MAD value is incorrectD. The upper control value is less than 20E. It is using an inappropriate forecasting methodology

AACSB: AnalyticDifficulty: MediumLearning Objective: 5Taxonomy: SynthesisTopic: Time Series Analysis

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87. You are hired as a consultant to advise a small firm on forecasting methodology. Based on your research you find the company has a MAD of 3. It wants to have a 99.7 percent control limits on its forecasting system. Its most recent tracking signal value is 15. What should be your report to the company? A. The forecasting model is operating acceptablyB. The forecasting model is out of control and needs to be correctedC. The MAD value is incorrectD. The upper control value is less than 20E. The company is using an inappropriate forecasting methodology

AACSB: AnalyticDifficulty: MediumLearning Objective: 5Taxonomy: KnowledgeTopic: Time Series Analysis

88. Which of the following is the portion of observations you would expect to see lying within a plus or minus 3 MAD range? A. 57.048 percentB. 88.946 percentC. 98.334 percentD. 99.856 percentE. 100 percent


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89. Which of the following is the portion of observations you would expect to see lying within a plus or minus 2 MAD range? A. 57.048B. 88.946C. 98.334D. 99.856E. 100


90. If the intercept value of a linear regression model is 40, the slope value is 40, and the value of X is 40, which of the following is the resulting forecast value using this model? A. 120B. 1,600C. 1,640D. 2,200E. 64,000


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91. A company hires you to develop a linear regression forecasting model. Based on the company's historical sales information, you determine the intercept value of the model to be 1,200. You also find the slope value is -50. If after developing the model you are given a value of X = 10, which of the following is the resulting forecast value using this model? A. -3,800B. 700C. 1,700D. 1,040E. 12,000


92. Heavy sales of umbrellas during a rain storm is an example of which of the following? A. A trendB. A causal relationshipC. A statistical correlationD. A coincidenceE. A fad


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93. You are using an exponential smoothing model for forecasting. The running sum of the forecast error statistics (RSFE) are calculated each time a forecast is generated. You find the last RSFE to be 34. Originally the forecasting model used was selected because it's relatively low MAD of 0.4. To determine when it is time to re-evaluate the usefulness of the exponential smoothing model you compute tracking signals. Which of the following is the resulting tracking system? A. 85B. 60C. 13.6D. 12.9E. 8


Fill in the Blank Questions

94. Name the four basic types of forecasting.1. _____________________;2. _____________________;3. _____________________;4. _____________________. (1.) Qualitative; (2.) Time series analysis; (3.) Causal; (4.) Simulation.


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95. A company has calculated its running sum of forecast errors to be 400 and its mean absolute deviation is exactly 25. What is the company's tracking signal? _____________________. 16


96. A company has calculated its running sum of forecast errors to be 1,000 and its tracking signal is 50. What is the company's mean absolute deviation? ___________ 20


97. A company wants to forecast demand using the simple moving average. If the company uses three prior yearly sales values (i.e., year 2008 = 185, year 2009 = 215, and year 2010 =230), what is the simple moving average forecast for year 2011? ____________ 210


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98. A company wants to forecast demand using the weighted moving average. If the company uses two prior yearly sales values (i.e., year 2009 = 11,000 and year 2010 = 13,000), and we want to weight year 2009 at 35% and year 2010 at 65%, what is the weighted moving average forecast for Year 2011? 12,300


99. As a consultant you have been asked to generate a unit demand forecast for a product for Year 2011 using exponential smoothing. Actual demand in year 2010 was 950 but the forecast for that year 1,060. Using this data and a smoothing constant alpha of 0.5, which of the following is the resulting year 2011 forecast value? __________ 1,005


100. A company has had actual unit demand for four consecutive years of 100, 110, 125, and 150. The respective forecasts using exponential smoothing were 120 for each of those four years. What value of alpha, the smoothing constant, was the firm using? ___________ 0 (zero)


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101. What are the five steps of CPFR (collaborative planning, forecasting and replenishment?)1. _____________________;2. _____________________;3. _____________________;4. _____________________;5. _____________________. (1.) Create a front-end partnership agreement; (2.) Joint business planning; (3.) Development of demand forecasts; (4.) Sharing forecasts; (5.) Inventory replenishment.

AACSB: AnalyticDifficulty: HardLearning Objective: 1Taxonomy: KnowledgeTopic: Wal-Mart's Data Warehouse

102. When analyzing time series data, if demand data contains both seasonal and trend effects at the same time, what are the two ways that they relate to each other discussed in the text? 1) ___________________________2) ___________________________ 1) Additive and 2) Multiplicative.

AACSB: AnalyticDifficulty: MediumLearning Objective: 6Taxonomy: KnowledgeTopic: Time Series Analysis

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Essay Questions

103. What does the text mean when it states that rather than to search for the perfect forecast one should learn to live with inaccurate forecasts?

The text makes this statement on page 337 in the context of "perfect forecasts are virtually impossible." And, further, analysts should not go to unreasonable lengths to improve the precision of a forecast. Rather, the analyst should look at several methodologies for forecasting the same phenomena and try to cull out the "commonsense" view from them. It is far more important to continually review forecasts and learn to live with inaccurate forecasts than it is to try to pin down a forecast with too much precision.

AACSB: AnalyticDifficulty: MediumLearning Objective: 1Taxonomy: SynthesisTopic: Wal-Mart's Data Warehouse

104. Distinguish between "dependent" and "independent" demand.

Starting on page 307 the text distinguishes between demand that is "dependent" upon (or can be derived from) demand of some other product (as in demand for an end-product's component) and demand that is "independent" or that which is the result of incoming orders from customers, etc.

AACSB: AnalyticDifficulty: EasyLearning Objective: 2Taxonomy: UnderstandingTopic: Demand Management

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105. Distinguish between errors in statistics and errors in forecasting.

In statistics, the term for errors is "residuals" which means the deviation of observations from a standard such as a regression line. These residuals are used to measure the "goodness of fit" of a model to the data it represents. In forecasting, the term "error" is used to denote the deviation that an actual value had from a forecast. These can be either "bias errors" (a systematic mistake such as using the wrong relationship between variables) or "random errors," deviations that simply can not be explained by the model being used.

AACSB: AnalyticDifficulty: HardLearning Objective: 5Taxonomy: KnowledgeTopic: Time Series Analysis

106. Describe the collaborative planning, forecasting and replenishment (CPFR) technique.

CPFR is described on pages 335-36 of the text. It is a sharing of information between trading partners across multiple levels in a supply chain which allows the entire supply chain to operate with lower levels of inventory and increased responsiveness.


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Chap 011

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