Ch12 Verified Test Bank Forecasting

CHAPTER 12

FORECASTING

CHAPTER LEARNING OBJECTIVES

1. Discuss the importance of accurate forecasting in supply chain management, quality management, and strategic planning. Forecasts of product demand are a necessity for almost all aspects of operational planning. Short-range demand forecasts determine the daily resource requirements needed for production, including labour and material, as well as for developing work schedules and shipping dates and controlling inventory levels. Long-range forecasts are needed to plan new products for development and changes in existing products and to acquire the plant, equipment, personnel, resources, and supply chain necessary for future operations.

2. Explain the forecasting process, including the factors to be considered when making forecasting decisions. The type of forecasting method to use depends on several factors, including the time frame of the forecast (i.e., how far into the future is being forecast) and demand behaviour (which may or may not follow a pattern). Three basic types of forecasting methods are: time series methods, regression methods, and qualitative methods. Forecasting is not just identifying what demand will be in the future. It is a continuing process that requires constant monitoring and adjustment. See Figure 12.3 for steps in the forecasting process.

3. Apply time series forecasting methods, including using Excel and OM tools. A time series is a collection of observations taken at regular intervals. Time series methods relate the forecast to only one factor—time. These methods assume that identifiable historical patterns or trends for demand over time will repeat themselves. They include the moving average, exponential smoothing, and linear trend line; and they are among the most popular methods for short-range forecasting among service and manufacturing companies.

Many software packages, including Excel, can be used to develop forecasts using the moving average, exponential smoothing, adjusted exponential smoothing, and linear trend line techniques. Excel can also be used to develop more customized forecast models, such as seasonal forecasts, and calculate the forecast errors.

4. Apply the different measures of forecast error. Some of the more popular measures of forecast error are: mean absolute deviation (MAD), mean absolute percent deviation (MAPD), mean squared error (MSE), cumulative error (E), and average error or bias (E¯). See Section 12.4 for examples of the different measures of forecast error.

5. Use linear and basic multiple regression as a forecasting tool. Regression is used for forecasting by establishing a mathematical relationship between two or more variables. Linear regression relates demand to one other independent variable, whereas multiple regression reflects the relationship between a dependent variable and two or more independent variables. The coefficient of determination measures the percentage of the variation in the dependent variable that results from the independent variable.

TRUE-FALSE STATEMENTS

1. Forecasts based on mathematical formulas are referred to as qualitative forecasts.

Difficulty: Easy

Learning Objective: Discuss the importance of accurate forecasting in supply chain management, quality management, and strategic planning.

Section Reference: 12.1 The Strategic Role of Forecasting in Supply Chain Management

2. One way to deal with the bullwhip effect is to develop and share the forecasts with other supply chain members.

Difficulty: Easy

Learning Objective: Discuss the importance of accurate forecasting in supply chain management, quality management, and strategic planning.

Section Reference: 12.1 The Strategic Role of Forecasting in Supply Chain Management

3. Forecasting customer demand is rarely a key to providing good quality service.

Difficulty: Easy

Learning Objective: Discuss the importance of accurate forecasting in supply chain management, quality management, and strategic planning.

Section Reference: 12.1 The Strategic Role of Forecasting in Supply Chain Management

4. A gradual, long-term up or down movement of demand is referred to as a trend.

Difficulty: Medium

Learning Objective: Discuss the importance of accurate forecasting in supply chain management, quality management, and strategic planning.

Section Reference: 12.1 The Strategic Role of Forecasting in Supply Chain Management

5. A seasonal pattern is an oscillating movement in demand that occurs periodically over the short-run and is repetitive.

Difficulty: Medium

Learning Objective: Discuss the importance of accurate forecasting in supply chain management, quality management, and strategic planning.

Section Reference: 12.1 The Strategic Role of Forecasting in Supply Chain Management

6. Qualitative forecasts use mathematical techniques and statistical formulas.

Difficulty: Medium

Learning Objective: Discuss the importance of accurate forecasting in supply chain management, quality management, and strategic planning.

Section Reference: 12.1 The Strategic Role of Forecasting in Supply Chain Management

7. Because of globalization of markets, managers are finding it increasingly more difficult to create accurate demand forecasts.

Difficulty: Medium

Learning Objective: Discuss the importance of accurate forecasting in supply chain management, quality management, and strategic planning.

Section Reference: 12.1 The Strategic Role of Forecasting in Supply Chain Management

8. In today’s competitive environment, effective supply chain management requires accurate demand forecasts.

Difficulty: Medium

Learning Objective: Discuss the importance of accurate forecasting in supply chain management, quality management, and strategic planning.

Section Reference: 12.1 The Strategic Role of Forecasting in Supply Chain Management

9. Sharing demand forecasts with supply chain members has resulted in an increased bullwhip effect.

Difficulty: Medium

Learning Objective: Discuss the importance of accurate forecasting in supply chain management, quality management, and strategic planning.

Section Reference: 12.1 The Strategic Role of Forecasting in Supply Chain Management

10. The trend toward continuous replenishment in supply chain design has shifted the need for accurate forecasts from short-term to long-term.

Difficulty: Medium

Learning Objective: Discuss the importance of accurate forecasting in supply chain management, quality management, and strategic planning.

Section Reference: 12.1 The Strategic Role of Forecasting in Supply Chain Management

11. Because of advances in technology, many service industries no longer require accurate forecasts to provide high quality service.

Difficulty: Medium

Learning Objective: Discuss the importance of accurate forecasting in supply chain management, quality management, and strategic planning.

Section Reference: 12.1 The Strategic Role of Forecasting in Supply Chain Management

12. The type of forecasting method selected depends on time frame, demand behaviour, and causes of behaviour.

Difficulty: Hard

Learning Objective: Discuss the importance of accurate forecasting in supply chain management, quality management, and strategic planning.

Section Reference: 12.1 The Strategic Role of Forecasting in Supply Chain Management

13. Long-range qualitative forecasts are used to determine future demand for new products, markets, and customers.

Difficulty: Medium

Learning Objective: Discuss the importance of accurate forecasting in supply chain management, quality management, and strategic planning.

Section Reference: 12.1 The Strategic Role of Forecasting in Supply Chain Management

14. The Delphi method generates forecasts based on informed judgments and opinions from knowledgeable individuals.

Difficulty: Medium

Learning Objective: Explain the forecasting process, including the factors to be considered when making forecasting decisions.

Section Reference: 12.2 Forecasting Demand

15. A gradual, long-term up or down movement of demand is called a trend.

Difficulty: Medium

Learning Objective: Explain the forecasting process, including the factors to be considered when making forecasting decisions.

Section Reference: 12.2 Forecasting Demand

16. Movements in demand that do not follow a given pattern are referred to as random variations.

Difficulty: Easy

Learning Objective: Explain the forecasting process, including the factors to be considered when making forecasting decisions.

Section Reference: 12.2 Forecasting Demand

17. Many companies are shifting from long-term to short-term forecast for strategic planning.

Difficulty: Medium

Learning Objective: Explain the forecasting process, including the factors to be considered when making forecasting decisions.

Section Reference: 12.2 Forecasting Demand

18. The demand behaviour for skis is considered cyclical.

Difficulty: Medium

Learning Objective: Explain the forecasting process, including the factors to be considered when making forecasting decisions.

Section Reference: 12.2 Forecasting Demand

19. The long-term strategic planning process is dependent upon qualitative forecasting methods.

Difficulty: Medium

Learning Objective: Explain the forecasting process, including the factors to be considered when making forecasting decisions.

Section Reference: 12.2 Forecasting Demand

20. Short-mid-range forecasts tend to use quantitative models that forecast demand based on historical demand.

Difficulty: Medium

Learning Objective: Explain the forecasting process, including the factors to be considered when making forecasting decisions.

Section Reference: 12.2 Forecasting Demand

21. Because of the heightened competition resulting from globalization, most companies find little strategic value in long-range forecasts.

Difficulty: Medium

Learning Objective: Explain the forecasting process, including the factors to be considered when making forecasting decisions.

Section Reference: 12.2 Forecasting Demand

22. The type of forecasting method used depends entirely whether the supply chain is continuous replenishment or not.

Difficulty: Medium

Learning Objective: Apply time series forecasting methods, including using Excel and OM tools.

Section Reference: 12.3 Time Series Methods

23. Time series methods use historical data to predict future demand.

Difficulty: Medium

Learning Objective: Apply time series forecasting methods, including using Excel and OM tools.

Section Reference: 12.3 Time Series Methods

24. The most common type of forecasting method for long-term strategic planning is based on quantitative modelling.

Difficulty: Medium

Learning Objective: Apply time series forecasting methods, including using Excel and OM tools.

Section Reference: 12.3 Time Series Methods

25. One reason time series methods are popular for forecasting is that they are relatively easy to use and understand.

Difficulty: Medium

Learning Objective: Apply time series forecasting methods, including using Excel and OM tools.

Section Reference: 12.3 Time Series Methods

26. Exponential smoothing is an averaging method for forecasting that reacts more strongly to recent changes in demand.

Difficulty: Medium

Learning Objective: Apply time series forecasting methods, including using Excel and OM tools.

Section Reference: 12.3 Time Series Methods

27. Time series methods assume that demand patterns in the past are a good predictor of demand in the future.

Difficulty: Medium

Learning Objective: Apply time series forecasting methods, including using Excel and OM tools.

Section Reference: 12.3 Time Series Methods

28. The moving average method is used for creating forecasts when there is no variation in demand.

Difficulty: Medium

Learning Objective: Apply time series forecasting methods, including using Excel and OM tools.

Section Reference: 12.3 Time Series Methods

29. Because of ease of use and simplicity, exponential smoothing is preferred over smoothing average.

Difficulty: Medium

Learning Objective: Apply time series forecasting methods, including using Excel and OM tools.

Section Reference: 12.3 Time Series Methods

30. Continuous replenishment systems rely heavily on extremely accurate long-term forecasts.

Difficulty: Easy

Learning Objective: Apply the different measures of forecast error.

Section Reference: 12.4 Forecast Accuracy

31. The average, absolute difference between the forecast and demand is a popular measure of forecast error.

Difficulty: Medium

Learning Objective: Apply the different measures of forecast error.

Section Reference: 12.4 Forecast Accuracy

32. The larger the mean absolute deviation (MAD), the more accurate the forecast.

Difficulty: Medium

Learning Objective: Apply the different measures of forecast error.

Section Reference: 12.4 Forecast Accuracy

33. Forecast bias is measured by the per-period average of the sum of the forecast errors.

Difficulty: Medium

Learning Objective: Apply the different measures of forecast error.

Section Reference: 12.4 Forecast Accuracy

34. Because of the development of advanced forecasting models, managers no longer track forecast error.

Difficulty: Medium

Learning Objective: Apply the different measures of forecast error.

Section Reference: 12.4 Forecast Accuracy

35. A linear regression model that relates demand to time is known as a linear trend line.

Difficulty: Medium

Learning Objective: Use linear and basic multiple regression as a forecasting tool.

Section Reference: 12.5 Regression Methods

36. Linear regression relates two variables using a linear model.

Difficulty: Medium

Learning Objective: Use linear and basic multiple regression as a forecasting tool.

Section Reference: 12.5 Regression Methods

37. A correlation coefficient is a measure of the strength of the linear relationship between an independent and a dependent variable.

Difficulty: Medium

Learning Objective: Use linear and basic multiple regression as a forecasting tool.

Section Reference: 12.5 Regression Methods

38. Multiple regression analysis can be used to relate demand to two or more dependent variables.

Difficulty: Medium

Learning Objective: Use linear and basic multiple regression as a forecasting tool.

Section Reference: 12.5 Regression Methods

39. Regression is used for forecasting when there is a relationship between the dependent variable, demand, and one or more independent (explanatory) variables.

Difficulty: Medium

Learning Objective: Use linear and basic multiple regression as a forecasting tool.

Section Reference: 12.5 Regression Methods

40. Correlation in linear regression is a measure of the strength of the relationship between the dependent variable, demand, and an independent (explanatory) variable.

Difficulty: Medium

Learning Objective: Use linear and basic multiple regression as a forecasting tool.

Section Reference: 12.5 Regression Methods

MULTIPLE CHOICE QUESTIONS

41. Forecast methods based on judgment, opinion, past experiences, or best guesses are known as ___ methods.

a) quantitative

b) qualitative

c) time series

d) regression

Difficulty: Easy

Learning Objective: Discuss the importance of accurate forecasting in supply chain management, quality management, and strategic planning.

Section Reference: 12.1 The Strategic Role of Forecasting in Supply Chain Management

42. A long-range forecast would normally not be used to

a) design the supply chain.

b) implement strategic programs.

c) determine production schedules.

d) plan new products for changing markets

Difficulty: Medium

Learning Objective: Discuss the importance of accurate forecasting in supply chain management, quality management, and strategic planning.

Section Reference: 12.1 The Strategic Role of Forecasting in Supply Chain Management

43. A qualitative procedure used to develop a consensus forecast is known as

a) exponential smoothing.

b) regression methods.

c) the Delphi technique.

d) naïve forecasting.

Difficulty: Medium

Learning Objective: Discuss the importance of accurate forecasting in supply chain management, quality management, and strategic planning.

Section Reference: 12.1 The Strategic Role of Forecasting in Supply Chain Management

44. A forecast where the current period’s demand is used as the next period’s forecast is known as a

a) moving average forecast.

b) naïve forecast.

c) weighted moving average forecast.

d) Delphi forecast.

Difficulty: Medium

Learning Objective: Discuss the importance of accurate forecasting in supply chain management, quality management, and strategic planning.

Section Reference: 12.1 The Strategic Role of Forecasting in Supply Chain Management

45. Regression forecasting methods relate ___ to other factors that cause demand behaviour.

a) supply

b) demand

c) time

d) money

e) efficiency

Difficulty: Medium

Learning Objective: Explain the forecasting process, including the factors to be considered when making forecasting decisions.

Section Reference: 12.2 Forecasting Demand

46. Which of the following is not a type of predictable demand behaviour?

a) trend

b) random variation

c) cycle

d) seasonal pattern

Difficulty: Medium

Learning Objective: Explain the forecasting process, including the factors to be considered when making forecasting decisions.

Section Reference: 12.2 Forecasting Demand

47. A ___ is an up-and-down movement in demand that repeats itself over a lengthy time period of more than a year.

a) trend

b) seasonal pattern

c) random variation

d) cycle

Difficulty: Easy

Learning Objective: Explain the forecasting process, including the factors to be considered when making forecasting decisions.

Section Reference: 12.2 Forecasting Demand

48. The sum of the weights in a weighted moving average forecast

a) must equal the number of periods being averaged.

b) must equal 1.00.

c) must be less than 1.00.

d) must be greater than 1.00.

Difficulty: Easy

Learning Objective: Explain the forecasting process, including the factors to be considered when making forecasting decisions.

Section Reference: 12.2 Forecasting Demand

49. An exponential smoothing forecasting technique requires all of the following except

a) the forecast for the current period.

b) the actual demand for the current period.

c) a smoothing constant.

d) large amounts of historical demand data.

Difficulty: Medium

Learning Objective: Explain the forecasting process, including the factors to be considered when making forecasting decisions.

Section Reference: 12.2 Forecasting Demand

50. The smoothing constant, α, in the exponential smoothing forecast

a) must always be a value greater than 1.0.

b) must always be a value less than 0.10.

c) must be a value between 0.0 and 1.0.

d) should be equal to the time frame for the forecast.

Difficulty: Medium

Learning Objective: Explain the forecasting process, including the factors to be considered when making forecasting decisions.

Section Reference: 12.2 Forecasting Demand

51. The closer the smoothing constant, α, is to 1.0,

a) the greater the reaction to the most recent demand.

b) the greater the dampening, or smoothing, effect.

c) the more accurate the forecast.

d) the less accurate the forecast.

Difficulty: Medium

Learning Objective: Explain the forecasting process, including the factors to be considered when making forecasting decisions.

Section Reference: 12.2 Forecasting Demand

52. The exponential smoothing model produces a naïve forecast when the smoothing constant, α, is equal to

a) 0.00.

b) 1.00.

c) 0.50.

d) 2.00

Difficulty: Medium

Learning Objective: Explain the forecasting process, including the factors to be considered when making forecasting decisions.

Section Reference: 12.2 Forecasting Demand

53. If forecast errors are normally distributed, then

a) 1 MAD = 1σ.

b) 1 MAD ≈ 0.8 σ.

c) 0.8 MAD ≈ 1σ.

d) 1 MAD ≈ 1.96 σ.

Difficulty: Medium

Learning Objective: Explain the forecasting process, including the factors to be considered when making forecasting decisions.

Section Reference: 12.2 Forecasting Demand

54. Correlation is a measure of the strength of the

a) nonlinear relationship between two dependent variables.

b) nonlinear relationship between a dependent and independent variable.

c) linear relationship between two dependent variables.

d) linear relationship between a dependent and independent variable.

Difficulty: Medium

Learning Objective: Explain the forecasting process, including the factors to be considered when making forecasting decisions.

Section Reference: 12.2 Forecasting Demand

55. The ___ method uses demand in the first period to forecast demand in the next period.

a) naïve

b) moving average

c) exponential smoothing

d) linear trend

Difficulty: Medium

Learning Objective: Apply time series forecasting methods, including using Excel and OM tools.

Section Reference: 12.3 Time Series Methods

56. The ___ forecast method consists of an exponentially smoothed forecast with a trend adjustment factor added to it.

a) exponentially smoothed

b) adjusted exponentially smoothed

c) time series

d) moving average

Difficulty: Medium

Learning Objective: Apply time series forecasting methods, including using Excel and OM tools.

Section Reference: 12.3 Time Series Methods

57. Selecting the type of forecasting method to use depends on

a) the time frame of the forecast.

b) the behaviour of demand and demand patterns.

c) the causes of demand behaviour.

d) all of the above.

Difficulty: Easy

Learning Objective: Apply time series forecasting methods, including using Excel and OM tools.

Section Reference: 12.3 Time Series Methods

58. Given the following demand data for the past five months, the three-period moving average forecast for June is

a) 103.33.

b) 99.00.

c) 95.00.

d) 92.50.

Difficulty: Medium

Learning Objective: Apply time series forecasting methods, including using Excel and OM tools.

Section Reference: 12.3 Time Series Methods

59. Given the following demand data for the past five months, the four-period moving average forecast for June is

a) 96.25.

b) 99.00.

c) 110.00.

d) 93.75.

Difficulty: Medium

Learning Objective: Apply time series forecasting methods, including using Excel and OM tools.

Section Reference: 12.3 Time Series Methods

60. A company wants to product a weighted moving average forecast for April with the weights 0.40, 0.35, and 0.25 assigned to March, February, and January, respectively. If the company had demands of 5,000 in January, 4,750 in February, and 5,200 in March, then April’s forecast is

a) 4983.33.

b) 4992.50.

c) 4962.50.

d) 5000.00.

Difficulty: Medium

Learning Objective: Apply time series forecasting methods, including using Excel and OM tools.

Section Reference: 12.3 Time Series Methods

61. The weighted moving average forecast for the fifth period with weights of 0.15 for period 1, 0.20 for period 2, 0.25 for period 3, and 0.40 for period 4, using the demand data shown below is

a) 3760.

b) 3700.

c) 3650.

d) 3325.

Difficulty: Medium

Learning Objective: Apply time series forecasting methods, including using Excel and OM tools.

Section Reference: 12.3 Time Series Methods

62. The per period average of cumulative error is called

a) cumulative forecast variation.

b) absolute error.

c) average error.

d) noise.

Difficulty: Easy

Learning Objective: Apply the different measures of forecast error.

Section Reference: 12.4 Forecast Accuracy

63. A forecasting model has produced the following forecasts:

The mean absolute deviation (MAD) for the end of May is

a) 7.0.

b) 7.5.

c) 10.0

d) 3.0

Difficulty: Hard

Learning Objective: Apply the different measures of forecast error.

Section Reference: 12.4 Forecast Accuracy

64. A forecasting model has produced the following forecasts:

The mean absolute percentage deviation (MAPD) for the end of May is

a) 0.0250.

b) 0.0583.

c) 0.5830.

d) 0.6670.

Answer b

Difficulty: Hard

Learning Objective: Apply the different measures of forecast error.

Section Reference: 12.4 Forecast Accuracy

65. A forecasting model has produced the following forecasts:

At the end of May the average error would be

a) 7.

b) 5.

c) 3.

d) 1.

Difficulty: Medium

Learning Objective: Apply the different measures of forecast error.

Section Reference: 12.4 Forecast Accuracy

66. A forecasting model has produced the following forecasts:

At the end of May the tracking signal would be

a) 0.000.

b) 0.667.

c) 1.333.

d) 2.143.

Difficulty: Hard

Learning Objective: Apply the different measures of forecast error.

Section Reference: 12.4 Forecast Accuracy

67. The mean absolute percentage deviation (MAPD) measures the absolute error as a percentage of

a) all errors.

b) per period demand.

c) total demand.

d) the average error.

Difficulty: Medium

Learning Objective: Apply the different measures of forecast error.

Section Reference: 12.4 Forecast Accuracy

68. A large positive cumulative error indicates that the forecast is probably

a) higher than the actual demand.

b) lower than the actual demand.

c) unbiased.

d) biased.

Difficulty: Medium

Learning Objective: Apply the different measures of forecast error.

Section Reference: 12.4 Forecast Accuracy

69. Which of the following statements concerning average error is true?

a) A positive value indicates high bias, and a negative value indicates low bias.

b) A positive value indicates zero bias, and a negative value indicates low bias.

c) A negative value indicates zero bias, and a negative value indicates high bias.

d) A positive value indicates low bias, and a negative value indicates high bias.

Difficulty: Medium

Learning Objective: Apply the different measures of forecast error.

Section Reference: 12.4 Forecast Accuracy

70. Which of the following is a reason why a forecast can go “out of control?”

a) a change in trend

b) an irregular variation such as unseasonable weather

c) a promotional campaign

d) all of the above

Difficulty: Medium

Learning Objective: Apply the different measures of forecast error.

Section Reference: 12.4 Forecast Accuracy

71. Which of the following can be used to monitor a forecast to see if it is biased high or low?

a) a tracking signal

b) the mean absolute deviation (MAD)

c) the mean absolute percentage deviation (MAPD)

d) a linear trend line model

Difficulty: Medium

Learning Objective: Apply the different measures of forecast error.

Section Reference: 12.4 Forecast Accuracy

72. A tracking signal is computed by

a) multiplying the cumulative error by MAD.

b) multiplying the absolute error by MAD.

c) dividing MAD by the cumulative absolute error.

d) dividing the cumulative error by MAD.

Difficulty: Medium

Learning Objective: Apply the different measures of forecast error.

Section Reference: 12.4 Forecast Accuracy

73. The demand and forecast values are shown in the table below:

The forecast error for September is

a) 10.00.

b) –10.00.

c) 1.00.

d) 39.00.

Difficulty: Medium

Learning Objective: Apply the different measures of forecast error.

Section Reference: 12.4 Forecast Accuracy

74. The demand and forecast values are shown in the table below:

The MAD through the end of October would be

a) 9.20.

b) –9.20.

c) 1.00.

d) 7.00.

Difficulty: Hard

Learning Objective: Apply the different measures of forecast error.

Section Reference: 12.4 Forecast Accuracy

75. Given the demand and forecast values below, the naïve forecast for September is

a) 100.6.

b) 99.0.

c) 102.0.

d) cannot be determined.

Difficulty: Medium

Learning Objective: Use linear and basic multiple regression as a forecasting tool.

Section Reference: 12.5 Regression Methods

76. A forecasting model has produced the following forecasts:

The forecast error for February is

a) 10.

b) –10.

c) –15.

d) –5

Difficulty: Medium

Learning Objective: Use linear and basic multiple regression as a forecasting tool.

Section Reference: 12.5 Regression Methods

77. A mathematical technique for forecasting that relates the dependent variable to an independent variable is

a) correlation analysis.

b) exponential smoothing.

c) linear regression.

d) weighted moving average.

Difficulty: Easy

Learning Objective: Use linear and basic multiple regression as a forecasting tool.

Section Reference: 12.5 Regression Methods

78. For the demand values and the January forecast shown in the table below, the exponential smoothing forecast for March using α = 0.30 is

a) 489.

b) 486.

c) 483.

d) 480.

Difficulty: Hard

Learning Objective: Use linear and basic multiple regression as a forecasting tool.

Section Reference: 12.5 Regression Methods

79. For the demand values and the January forecast shown in the table below, the exponential smoothing forecast for March using α = 0.40 is

a) 1200.

b) 1220.

c) 1222.

d) 1225.

Difficulty: Hard

Learning Objective: Use linear and basic multiple regression as a forecasting tool.

Section Reference: 12.5 Regression Methods

80. If the forecast for July was 3300 and the actual demand for July was 3250, then the exponential smoothing forecast for August using α = 0.20 is

a) 3300.

b) 3290.

c) 3275.

d) 3250.

Difficulty: Medium

Learning Objective: Use linear and basic multiple regression as a forecasting tool.

Section Reference: 12.5 Regression Methods

81. Given the demand and forecast values shown in the table below:

The three-period moving average forecast for November is

a) 516.

b) 528.

c) 524.

d) 515.

Difficulty: Medium

Learning Objective: Use linear and basic multiple regression as a forecasting tool.

Section Reference: 12.5 Regression Methods

82. The demand and forecast values are shown in the table below:

The exponential smoothing forecast for November using α = 0.35 is

a) 552.45.

b) 553.50.

c) 554.55.

d) 557.50.

Difficulty: Medium

Learning Objective: Use linear and basic multiple regression as a forecasting tool.

Section Reference: 12.5 Regression Methods

SHORT-ANSWER ESSAY QUESTIONS

83. Discuss the importance of accurate forecasts in supply chain management.

Difficulty: Hard

Learning Objective: Discuss the importance of accurate forecasting in supply chain management, quality management, and strategic planning.

Section Reference: 12.1 The Strategic Role of Forecasting in Supply Chain Management

84. Explain the difference between qualitative and quantitative forecasting methods.

Difficulty: Medium

Learning Objective: Explain the forecasting process, including the factors to be considered when making forecasting decisions.

Section Reference: 12.2 Forecasting Demand

85. Compare and contrast short-mid-range forecasts and long-range forecasts.

Difficulty: Medium

Learning Objective: Explain the forecasting process, including the factors to be considered when making forecasting decisions.

Section Reference: 12.2 Forecasting Demand

86. Explain how and why time series and regression forecasting methods differ.

Difficulty: Medium

Learning Objective: Apply time series forecasting methods, including using Excel and OM tools.

Learning Objective: Use linear and basic multiple regression as a forecasting tool.

Section Reference: 12.3 Time Series Methods

Section Reference: 12.5 Regression Methods

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