Ch12 Forecasting Verified Test Bank

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. Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel. 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.

6. Describe the uses of data mining and its associated tools. Data mining is a process of exploring, categorizing, and modelling large amounts of data in order to discover trends, predict future events, and assess possible courses of action. Data mining uses and analyzes data that are stored in databases, data warehouses, and data marts, and employs tools such as association rule learning, clustering analysis, classification, prediction, and summarization

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

Blooms: Knowledge

AACSB: Reflective Thinking

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

Blooms: Knowledge

AACSB: Reflective Thinking

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

Blooms: Knowledge

AACSB: Reflective Thinking

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

Blooms: Comprehension

AACSB: Reflective Thinking

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

Blooms: Comprehension

AACSB: Reflective Thinking

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

Blooms: Knowledge

AACSB: Reflective Thinking

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

Blooms: Comprehension

AACSB: Reflective Thinking

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

Blooms: Comprehension

AACSB: Reflective Thinking

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

Blooms: Comprehension

AACSB: Reflective Thinking

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

Blooms: Comprehension

AACSB: Reflective Thinking

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

Blooms: Comprehension

AACSB: Reflective Thinking

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

Blooms: Comprehension

AACSB: Reflective Thinking

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

Blooms: Comprehension

AACSB: Reflective Thinking

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 Components of Forecasting Demand

Blooms: Knowledge

AACSB: Reflective Thinking

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 Components of Forecasting Demand

Blooms: Knowledge

AACSB: Reflective Thinking

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 Components of Forecasting Demand

Blooms: Knowledge

AACSB: Reflective Thinking

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 Components of Forecasting Demand

Blooms: Comprehension

AACSB: Reflective Thinking

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 Components of Forecasting Demand

Blooms: Comprehension

AACSB: Reflective Thinking

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 Components of Forecasting Demand

Blooms: Comprehension

AACSB: Reflective Thinking

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 Components of Forecasting Demand

Blooms: Comprehension

AACSB: Reflective Thinking

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 Components of Forecasting Demand

Blooms: Comprehension

AACSB: Reflective Thinking

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

Difficulty: Medium

Learning Objective: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Comprehension

AACSB: Reflective Thinking

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

Difficulty: Medium

Learning Objective: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Knowledge

AACSB: Reflective Thinking

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

Difficulty: Medium

Learning Objective: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Comprehension

AACSB: Reflective Thinking

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

Difficulty: Medium

Learning Objective: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Knowledge

AACSB: Reflective Thinking

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

Difficulty: Medium

Learning Objective: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Comprehension

AACSB: Reflective Thinking

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

Difficulty: Medium

Learning Objective: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Comprehension

AACSB: Reflective Thinking

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

Difficulty: Medium

Learning Objective: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Comprehension

AACSB: Reflective Thinking

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

Difficulty: Medium

Learning Objective: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Knowledge

AACSB: Reflective Thinking

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

Blooms: Knowledge

AACSB: Reflective Thinking

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

Blooms: Knowledge

AACSB: Reflective Thinking

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

Blooms: Comprehension

AACSB: Reflective Thinking

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

Blooms: Comprehension

AACSB: Reflective Thinking

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

Blooms: Comprehension

AACSB: Reflective Thinking

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

Blooms: Comprehension

AACSB: Reflective Thinking

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

Blooms: Comprehension

AACSB: Reflective Thinking

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

Blooms: Comprehension

AACSB: Reflective Thinking

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

Blooms: Knowledge

AACSB: Reflective Thinking

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

Blooms: Comprehension

AACSB: Reflective Thinking

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

Blooms: Comprehension

AACSB: Reflective Thinking

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

Blooms: Knowledge

AACSB: Reflective Thinking

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

Blooms: Comprehension

AACSB: Reflective Thinking

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

Blooms: Knowledge

AACSB: Reflective Thinking

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

Blooms: Knowledge

AACSB: Reflective Thinking

N45. Which of the following is a trend in supply chain design where continuous updating of data is shared between suppliers and customers?

a) continuous improvement

b) continuous replenishment

c) supply chain management

d) information sharing

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

Blooms: Comprehension

AACSB: Reflective Thinking

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

a) supply

b) demand

c) time

d) money

Difficulty: Medium

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

Section Reference: 12.2 Components of Forecasting Demand

Blooms: Comprehension

AACSB: Reflective Thinking

47. 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 Components of Forecasting Demand

Blooms: Comprehension

AACSB: Reflective Thinking

48. 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 Components of Forecasting Demand

Blooms: Knowledge

AACSB: Reflective Thinking

N49. A forecast that typically encompasses the immediate future – daily up to two years is a

a) short-range forecast

b) mid-range forecast

c) short- to mid-range forecast

d) mid-to long-range forecast

Difficulty: Easy

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

Section Reference: 12.2 Components of Forecasting Demand

Blooms: Knowledge

AACSB: Reflective Thinking

N50. Qualitative forecasting methods are commonly used for which strategic planning timeframe?

a) short-term

b) medium-term

c) long-term

d) mid-to long-term

Difficulty: Medium

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

Section Reference: 12.2 Components of Forecasting Demand

Blooms: Comprehension

AACSB: Reflective Thinking

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: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Comprehension

AACSB: Reflective Thinking

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: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Comprehension

AACSB: Reflective Thinking

53. 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: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Knowledge

AACSB: Reflective Thinking

54. 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: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Comprehension

AACSB: Reflective Thinking

55. 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: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Knowledge

AACSB: Reflective Thinking

56. 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: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Comprehension

AACSB: Reflective Thinking

57. 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: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Comprehension

AACSB: Reflective Thinking

58. 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: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Knowledge

AACSB: Reflective Thinking

59. 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: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Application

AACSB: Analytic

60. 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: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Application

AACSB: Analytic

61. 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) 4,983.33.

b) 4,992.50.

c) 4,962.50.

d) 5,000.00.

Difficulty: Medium

Learning Objective: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Application

AACSB: Analytic

62. 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) 3,760.

b) 3,700.

c) 3,650.

d) 3,325.

Difficulty: Medium

Learning Objective: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Application

AACSB: Analytic

N63. In calculating an exponentially smoothed forecast, the previously determined forecast for the present period is denoted by which of the following variables?

a) F_t+1

b) F_t-1

c) F_t

d) D_t

Difficulty: Medium

Learning Objective: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Comprehension

AACSB: Reflective Thinking

64. 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: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Application

AACSB: Analytic

65. 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: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Application

AACSB: Analytic

66. 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) 1,200.

b) 1,220.

c) 1,222.

d) 1,225.

Difficulty: Hard

Learning Objective: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Application

AACSB: Analytic

67. 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: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Application

AACSB: Analytic

68. 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: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Application

AACSB: Analytic

69. 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: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

Section Reference: 12.3 Time Series Methods

Blooms: Application

AACSB: Analytic

70. 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

Blooms: Knowledge

AACSB: Reflective Thinking

71. 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

Blooms: Application

AACSB: Analytic

72. 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

Blooms: Application

AACSB: Analytic

73. 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

Blooms: Application

AACSB: Analytic

74. 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

Blooms: Application

AACSB: Analytic

75. 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: Apply the different measures of forecast error.

Section Reference: 12.4 Forecast Accuracy

Blooms: Application

AACSB: Analytic

76. 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

Blooms: Comprehension

AACSB: Reflective Thinking

77. 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

Blooms: Comprehension

AACSB: Reflective Thinking

78. 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

Blooms: Comprehension

AACSB: Reflective Thinking

79. 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

Blooms: Comprehension

AACSB: Reflective Thinking

80. 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

Blooms: Comprehension

AACSB: Reflective Thinking

81. 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

Blooms: Comprehension

AACSB: Reflective Thinking

82. 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

Blooms: Application

AACSB: Analytic

83. 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

Blooms: Application

AACSB: Analytic

N84. The demand and forecast values are shown in the table below:

The tracking signal at the end of August would be

a) 0.103

b) 1.000

c) 1.138

d) 2.000

Difficulty: Hard

Learning Objective: Apply the different measures of forecast error.

Section Reference: 12.4 Forecast Accuracy

Blooms: Application

AACSB: Analytic

85. 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: 12.4 Components of Forecasting Demand

Blooms: Comprehension

AACSB: Reflective Thinking

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

Section Reference: 12.5 Regression Methods

Blooms: Comprehension

AACSB: Reflective Thinking

87. 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

Blooms: Knowledge

AACSB: Reflective Thinking

N88. In linear regression, the independent variable is denoted by which of the following variables?

a) a

b) b

c) x

d) y

Difficulty: Easy

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

Section Reference: 12.5 Regression Methods

Blooms: Knowledge

AACSB: Reflective Thinking

N89. A causal method for observing the relationship of demand to two or more independent variables is

a) least squares regression

b) linear regression

c) multiple regression

d) correlative regression

Difficulty: Easy

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

Section Reference: 12.5 Regression Methods

Blooms: Knowledge

AACSB: Reflective Thinking

N90. Which of the following is a common problem often encountered in multiple regression?

a) covariance

b) correlation

c) multicollinearity

d) auto-correlation

Difficulty: Easy

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

Section Reference: 12.5 Regression Methods

Blooms: Knowledge

AACSB: Reflective Thinking

N91. A process for analyzing large amounts of data to identify patterns, trends, and relationships in groups is known as

a) data mining

b) regression

c) forecasting

d) trend analysis

Difficulty: Easy

Learning Objective: Describe the uses of data mining and its associated tools.

Section Reference: 12.6 Data Mining

Blooms: Knowledge

AACSB: Reflective Thinking

N92. Which of the following is not a common use for data mining?

a) discover trends

b) predict past events

c) assess possible courses of action

d) business forecasting

Difficulty: Medium

Learning Objective: Describe the uses of data mining and its associated tools.

Section Reference: 12.6 Data Mining

Blooms: Comprehension

AACSB: Reflective Thinking

N93. A company’s repository of current and historical data is a

a) database

b) data warehouses

c) data store

d) data mart

Difficulty: Medium

Learning Objective: Describe the uses of data mining and its associated tools.

Section Reference: 12.6 Data Mining

Blooms: Comprehension

AACSB: Reflective Thinking

N94. Which of the following is a data mining tool that searches for relationships between variables?

a) linear regression

b) multiple regression

c) association rule learning

d) data analysis

Difficulty: Medium

Learning Objective: Describe the uses of data mining and its associated tools.

Section Reference: 12.6 Data Mining

Blooms: Comprehension

AACSB: Reflective Thinking

N95. Which of the following is a not a software package used to mine data?

a) Excel

b) SAS

c) SPSS

d) Access

Difficulty: Medium

Learning Objective: Describe the uses of data mining and its associated tools.

Section Reference: 12.6 Data Mining

Blooms: Comprehension

AACSB: Reflective Thinking

SHORT-ANSWER ESSAY QUESTIONS

96. 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

Blooms: Synthesis

AACSB: Reflective Thinking; Communication

97. 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 Components of Forecasting Demand

Blooms: Comprehension

AACSB: Reflective Thinking; Communication

98. 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 Components of Forecasting Demand

Blooms: Evaluation

AACSB: Reflective Thinking; Communication

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

Difficulty: Medium

Learning Objective: Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.

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

Blooms: Evaluation

AACSB: Reflective Thinking; Communication

N100. Discuss the purpose and usefulness of forecast error analysis.

Forecasts are never completely accurate and there will always be a deviation between the actual demand and the forecast (known as the forecast error). Forecast error analysis helps identify which type of forecasting model can produce the lowest forecast errors thus helping increase forecast accuracy. Tracking signals can also identify if forecasts have a high or low bias.

Difficulty: Medium

Learning Objective: Apply the different measures of forecast error.

Section Reference: 12.4 Forecast Accuracy

Blooms: Synthesis

AACSB: Reflective Thinking; Communication

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