Forecasting – Test Bank | Ch4 – 10th Global Edition

View Product website:

https://selldocx.com/docx/forecasting-test-bank-ch4-10th-global-edition-1334

Operations Management, 10e, Global Edition (Heizer/Render)

Chapter 4 Forecasting

1) A naïve forecast for September sales of a product would be equal to the forecast for August.

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

2) The forecasting time horizon and the forecasting techniques used tend to vary over the life cycle of a product.

Diff: 2

Topic: What is forecasting?

Objective: LO4-1

3) Demand (sales) forecasts serve as inputs to financial, marketing, and personnel planning.

Diff: 2

Topic: Types of forecasts

Objective: LO4-1

4) Forecasts of individual products tend to be more accurate than forecasts of product families.

Diff: 2

Topic: Seven steps in the forecasting system

Objective: LO4-1

5) Most forecasting techniques assume that there is some underlying stability in the system.

Diff: 2

Topic: Seven steps in the forecasting system

Objective: LO4-1

6) The sales force composite forecasting method relies on salespersons' estimates of expected sales.

Diff: 1

Topic: Forecasting approaches

Objective: LO4-2

7) A time-series model uses a series of past data points to make the forecast.

Diff: 2

Topic: Forecasting approaches

Objective: LO4-2

8) The quarterly "make meeting" of Lexus dealers is an example of a sales force composite forecast.

Diff: 1

Topic: Forecasting approaches

Objective: LO4-2

9) Cycles and random variations are both components of time series.

Diff: 1

Topic: Time-series forecasting

Objective: LO4-3

10) A naive forecast for September sales of a product would be equal to the sales in August.

Diff: 1

Topic: Time-series forecasting

Objective: LO4-3

11) One advantage of exponential smoothing is the limited amount of record keeping involved.

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

12) The larger the number of periods in the simple moving average forecasting method, the greater the method's responsiveness to changes in demand.

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

13) Mean Squared Error and Coefficient of Correlation are two measures of the overall error of a forecasting model.

Diff: 1

Topic: Time-series forecasting

Objective: LO4-4

14) In trend projection, the trend component is the slope of the regression equation.

Diff: 1

Topic: Time-series forecasting

Objective: LO4-3

15) In trend projection, a negative regression slope is mathematically impossible.

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

16) Seasonal indices adjust raw data for patterns that repeat at regular time intervals.

Diff: 2

Topic: Time-series forecasting

Objective: LO4-5

17) The best way to forecast a business cycle is by finding a leading variable.

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

18) Linear-regression analysis is a straight-line mathematical model to describe the functional relationships between independent and dependent variables.

Diff: 1

Topic: Associative forecasting methods: Regression and correlation analysis

Objective: LO4-6

19) The larger the standard error of the estimate, the more accurate the forecasting model.

Diff: 1

Topic: Associative forecasting methods: Regression and correlation analysis

Objective: LO4-4

20) A trend projection equation with a slope of 0.78 means that there is a 0.78 unit rise in Y for every unit of time that passes.

Diff: 2

Topic: Time-series forecasting: Trend projections

Objective: LO4-6

21) In a regression equation where Y is demand and X is advertising, a coefficient of determination (R2) of .70 means that 70% of the variance in advertising is explained by demand.

Diff: 2

Topic: Associative forecasting methods: Regression and correlation analysis

Objective: LO4-6

22) Demand cycles for individual products can be driven by product life cycles.

Diff: 2

Topic: Time-series forecasting

Objective: LO4-5

23) If a forecast is consistently greater than (or less than) actual values, the forecast is said to be biased.

Diff: 2

Topic: Monitoring and controlling forecasts

Objective: LO4-4

24) Focus forecasting tries a variety of computer models and selects the best one for a particular application.

Diff: 2

Topic: Monitoring and controlling forecasts

Objective: LO4-4

25) Many service firms use point-of-sale computers to collect detailed records needed for accurate short-term forecasts.

Diff: 2

Topic: Forecasting in the service sector

Objective: LO4-4

26) Economic forecasts drive a company's projections for production.

Diff: 1

Topic: Types of forecasts

Objective: LO4-1

27) Regression lines graphically depict "cause-and-effect" relationships.

Diff: 2

Topic: Correlation coefficients for regression lines

Objective: LO4-6

28) What two numbers are contained in the daily report to the CEO of Walt Disney Parks & Resorts regarding the six Orlando parks?

A) yesterday's forecasted attendance and yesterday's actual attendance

B) yesterday's actual attendance and today's forecasted attendance

C) yesterday's forecasted attendance and today's forecasted attendance

D) yesterday's actual attendance and last year's actual attendance

E) yesterday's forecasted attendance and the year-to-date average daily forecast error

Diff: 2

Topic: Global company profile

Objective: no LO

29) Forecasts

A) become more accurate with longer time horizons

B) are rarely perfect

C) are more accurate for individual items than for groups of items

D) all of the above

E) none of the above

Diff: 2

Topic: What is forecasting?

Objective: LO4-1

30) One use of short-range forecasts is to determine

A) production planning

B) inventory budgets

C) research and development plans

D) facility location

E) job assignments

Diff: 2

Topic: What is forecasting?

Objective: LO4-1

31) Forecasts are usually classified by time horizon into three categories

A) short-range, medium-range, and long-range

B) finance/accounting, marketing, and operations

C) strategic, tactical, and operational

D) exponential smoothing, regression, and time series

E) departmental, organizational, and industrial

Diff: 1

Topic: What is forecasting?

Objective: LO4-1

32) A forecast with a time horizon of about 3 months to 3 years is typically called a

A) long-range forecast

B) medium-range forecast

C) short-range forecast

D) weather forecast

E) strategic forecast

Diff: 2

Topic: What is forecasting?

Objective: LO4-1

33) Forecasts used for new product planning, capital expenditures, facility location or expansion, and R&D typically utilize a

A) short-range time horizon

B) medium-range time horizon

C) long-range time horizon

D) naive method, because there is no data history

E) all of the above

Diff: 2

Topic: What is forecasting?

Objective: LO4-1

34) The three major types of forecasts used by business organizations are

A) strategic, tactical, and operational

B) economic, technological, and demand

C) exponential smoothing, Delphi, and regression

D) causal, time-series, and seasonal

E) departmental, organizational, and territorial

Diff: 2

Topic: Types of forecasts

Objective: LO4-2

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

A) Determine the use of the forecast.

B) Eliminate any assumptions.

C) Determine the time horizon.

D) Select forecasting model.

E) Validate and implement the results.

Diff: 2

Topic: The strategic importance of forecasting

Objective: LO4-2

36) The two general approaches to forecasting are

A) qualitative and quantitative

B) mathematical and statistical

C) judgmental and qualitative

D) historical and associative

E) judgmental and associative

Diff: 1

Topic: Forecasting approaches

Objective: LO4-2

37) Which of the following uses three types of participants: decision makers, staff personnel, and respondents?

A) executive opinions

B) sales force composites

C) the Delphi method

D) consumer surveys

E) time series analysis

Diff: 2

Topic: Forecasting approaches

Objective: LO4-2

38) The forecasting model that pools the opinions of a group of experts or managers is known as the

A) sales force composition model

B) multiple regression

C) jury of executive opinion model

D) consumer market survey model

E) management coefficients model

Diff: 2

Topic: Forecasting approaches

Objective: LO4-2

39) Which of the following is not a type of qualitative forecasting?

A) executive opinions

B) sales force composites

C) consumer surveys

D) the Delphi method

E) moving average

Diff: 2

Topic: Forecasting approaches

Objective: LO4-2

40) Which of the following techniques uses variables such as price and promotional expenditures, which are related to product demand, to predict demand?

A) associative models

B) exponential smoothing

C) weighted moving average

D) simple moving average

E) time series

Diff: 2

Topic: Forecasting approaches

Objective: LO4-2

41) Which of the following statements about time-series forecasting is true?

A) It is based on the assumption that future demand will be the same as past demand.

B) It makes extensive use of the data collected in the qualitative approach.

C) The analysis of past demand helps predict future demand.

D) Because it accounts for trends, cycles, and seasonal patterns, it is more powerful than causal forecasting.

E) All of the above are true.

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

42) Time-series data may exhibit which of the following behaviors?

A) trend

B) random variations

C) seasonality

D) cycles

E) They may exhibit all of the above.

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

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

A) seasonal variation

B) cycles

C) trends

D) exponential variation

E) random variation

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

44) Which of the following is not present in a time series?

A) seasonality

B) operational variations

C) trend

D) cycles

E) random variations

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

45) The fundamental difference between cycles and seasonality is the

A) duration of the repeating patterns

B) magnitude of the variation

C) ability to attribute the pattern to a cause

D) all of the above

E) none of the above

Diff: 2

Topic: Time-series forecasting

Objective: LO4-5

46) In time series, which of the following cannot be predicted?

A) large increases in demand

B) technological trends

C) seasonal fluctuations

D) random fluctuations

E) large decreases in demand

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

47) What is the approximate forecast for May using a four-month moving average?

A) 38

B) 42

C) 43

D) 44

E) 47

Diff: 2

Topic: Time-series forecasting

AACSB: Analytic Skills

Objective: LO4-3

48) Which time-series model below assumes that demand in the next period will be equal to the most recent period's demand?

A) naive approach

B) moving average approach

C) weighted moving average approach

D) exponential smoothing approach

E) none of the above

Diff: 1

Topic: Time-series forecasting

Objective: LO4-3

49) John's House of Pancakes uses a weighted moving average method to forecast pancake sales. It assigns a weight of 5 to the previous month's demand, 3 to demand two months ago, and 1 to demand three months ago. If sales amounted to 1000 pancakes in May, 2200 pancakes in June, and 3000 pancakes in July, what should be the forecast for August?

A) 2400

B) 2511

C) 2067

D) 3767

E) 1622

Diff: 2

Topic: Time series forecasting

AACSB: Analytic Skills

Objective: LO4-3

50) A six-month moving average forecast is better than a three-month moving average forecast if demand

A) is rather stable

B) has been changing due to recent promotional efforts

C) follows a downward trend

D) follows a seasonal pattern that repeats itself twice a year

E) follows an upward trend

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

51) Increasing the number of periods in a moving average will accomplish greater smoothing, but at the expense of

A) manager understanding

B) accuracy

C) stability

D) responsiveness to changes

E) All of the above are diminished when the number of periods increases.

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

52) Which of the following statements comparing the weighted moving average technique and exponential smoothing is true?

A) Exponential smoothing is more easily used in combination with the Delphi method.

B) More emphasis can be placed on recent values using the weighted moving average.

C) Exponential smoothing is considerably more difficult to implement on a computer.

D) Exponential smoothing typically requires less record keeping of past data.

E) Exponential smoothing allows one to develop forecasts for multiple periods, whereas weighted moving averages does not.

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

53) Which time-series model uses past forecasts and past demand data to generate a new forecast?

A) naive

B) moving average

C) weighted moving average

D) exponential smoothing

E) regression analysis

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

54) Which is not a characteristic of exponential smoothing?

A) smoothes random variations in the data

B) easily altered weighting scheme

C) weights each historical value equally

D) has minimal data storage requirements

E) None of the above; they are all characteristics of exponential smoothing.

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

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

A) 0

B) 1 divided by the number of periods

C) 0.5

D) 1.0

E) cannot be determined

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

56) Given an actual demand of 103, a previous forecast value of 99, and an alpha of .4, the exponential smoothing forecast for the next period would be

A) 94.6

B) 97.4

C) 100.6

D) 101.6

E) 103.0

Diff: 2

Topic: Time-series forecasting

AACSB: Analytic Skills

Objective: LO4-3

57) A forecast based on the previous forecast plus a percentage of the forecast error is a(n)

A) qualitative forecast

B) naive forecast

C) moving average forecast

D) weighted moving average forecast

E) exponentially smoothed forecast

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

58) Given an actual demand of 61, a previous forecast of 58, and an of .3, what would the forecast for the next period be using simple exponential smoothing?

A) 45.5

B) 57.1

C) 58.9

D) 61.0

E) 65.5

Diff: 2

Topic: Time-series forecasting

AACSB: Analytic Skills

Objective: LO4-3

59) Which of the following values of alpha would cause exponential smoothing to respond the most slowly to forecast errors?

A) 0.10

B) 0.20

C) 0.40

D) 0.80

E) cannot be determined

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

60) A forecasting method has produced the following over the past five months. What is the mean absolute deviation?

A) -0.2

B) -1.0

C) 0.0

D) 1.2

E) 8.6

Diff: 2

Topic: Time-series forecasting

AACSB: Analytic Skills

Objective: LO4-4

61) The primary purpose of the mean absolute deviation (MAD) in forecasting is to

A) estimate the trend line

B) eliminate forecast errors

C) measure forecast accuracy

D) seasonally adjust the forecast

E) all of the above

Diff: 2

Topic: Time-series forecasting

Objective: LO4-4

62) Given forecast errors of -1, 4, 8, and -3, what is the mean absolute deviation?

A) 2

B) 3

C) 4

D) 8

E) 16

Diff: 2

Topic: Time-series forecasting

AACSB: Analytic Skills

Objective: LO4-4

63) The last four months of sales were 8, 10, 15, and 9 units. The last four forecasts were 5, 6, 11, and 12 units. The Mean Absolute Deviation (MAD) is

A) 2

B) -10

C) 3.5

D) 9

E) 10.5

Diff: 2

Topic: Time-series forecasting

AACSB: Analytic Skills

Objective: LO4-4

64) A time series trend equation is 25.3 + 2.1 X. What is your forecast for period 7?

A) 23.2

B) 25.3

C) 27.4

D) 40.0

E) cannot be determined

Diff: 2

Topic: Time-series forecasting

AACSB: Analytic Skills

Objective: LO4-3

65) For a given product demand, the time series trend equation is 53 - 4 X. The negative sign on the slope of the equation

A) is a mathematical impossibility

B) is an indication that the forecast is biased, with forecast values lower than actual values

C) is an indication that product demand is declining

D) implies that the coefficient of determination will also be negative

E) implies that the cumulative error will be negative

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

66) Yamaha manufactures which set of products with complementary demands to address seasonal fluctuations?

A) golf clubs and skis

B) swimming suits and winter jackets

C) jet skis and snowmobiles

D) pianos and guitars

E) ice skates and water skis

Diff: 2

Topic: Time-series forecasting

Objective: LO4-5

67) Which of the following is true regarding the two smoothing constants of the Forecast Including Trend (FIT) model?

A) One constant is positive, while the other is negative.

B) They are called MAD and cumulative error.

C) Alpha is always smaller than beta.

D) One constant smoothes the regression intercept, whereas the other smoothes the regression slope.

E) Their values are determined independently.

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

68) Demand for a certain product is forecast to be 800 units per month, averaged over all 12 months of the year. The product follows a seasonal pattern, for which the January monthly index is 1.25. What is the seasonally-adjusted sales forecast for January?

Diff: 2

Topic: Time-series forecasting

AACSB: Analytic Skills

Objective: LO4-5

69) A seasonal index for a monthly series is about to be calculated on the basis of three years' accumulation of data. The three previous July values were 110, 150, and 130. The average over all months is 190. The approximate seasonal index for July is

A) 0.487

B) 0.684

C) 1.462

D) 2.053

E) cannot be calculated with the information given

Diff: 2

Topic: Time-series forecasting

AACSB: Analytic Skills

Objective: LO4-5

70) A fundamental distinction between trend projection and linear regression is that

A) trend projection uses least squares while linear regression does not

B) only linear regression can have a negative slope

C) in trend projection the independent variable is time; in linear regression the independent variable need not be time, but can be any variable with explanatory power

D) linear regression tends to work better on data that lack trends

E) trend projection uses two smoothing constants, not just one

Diff: 2

Topic: Associative forecasting methods: Regression and correlation analysis

Objective: LO4-6

71) The degree or strength of a relationship between two variables is shown by the

A) alpha

B) mean

C) mean absolute deviation

D) correlation coefficient

E) cumulative error

Diff: 2

Topic: Associative forecasting methods: Regression and correlation analysis

Objective: LO4-6

72) If two variables were perfectly correlated, the correlation coefficient r would equal

A) 0

B) -1

C) 1

D) B or C

E) none of the above

Diff: 2

Topic: Associative forecasting methods: Regression and correlation analysis

Objective: LO4-6

73) The last four weekly values of sales were 80, 100, 105, and 90 units. The last four forecasts were 60, 80, 95, and 75 units. These forecasts illustrate

A) qualitative methods

B) adaptive smoothing

C) slope

D) bias

E) trend projection

Diff: 1

Topic: Monitoring and controlling forecasts

Objective: LO4-7

74) The tracking signal is the

A) standard error of the estimate

B) absolute deviation of the last periods forecast

C) mean absolute deviation (MAD)

D) ratio of cumulative error/MAD

E) mean absolute percentage error (MAPE)

Diff: 2

Topic: Monitoring and controlling forecasts

Objective: LO4-7

75) Computer monitoring of tracking signals and self-adjustment if a signal passes a preset limit is characteristic of

A) exponential smoothing including trend

B) adaptive smoothing

C) trend projection

D) focus forecasting

E) multiple regression analysis

Diff: 2

Topic: Monitoring and controlling forecasts

Objective: LO4-7

76) Many services maintain records of sales noting

A) the day of the week

B) unusual events

C) weather

D) holidays

E) all of the above

Diff: 2

Topic: Forecasting in the service sector

Objective: LO4-2

77) Taco Bell's unique employee scheduling practices are partly the result of using

A) point-of-sale computers to track food sales in 15 minute intervals

B) focus forecasting

C) a six-week moving average forecasting technique

D) multiple regression

E) A and C are both correct.

Diff: 2

Topic: Forecasting in the service sector

Objective: LO4-3

78) Long-range forecasting is generally done in planning for

A) job scheduling

B) production levels

C) cash budgeting

D) capital expenditures

E) planning purchasing

Diff: 2

Topic: Forecasting time horizons

Objective: LO4-1

79) Short-range forecasting tends to be __________ than longer-range forecasts

A) less accurate

B) more accurate

C) about the same

D) significantly more difficult

E) significantly less difficult

Diff: 2

Topic: Forecasting time horizons

Objective: LO4-1

80) __________ expresses the error as a percent of the actual values, undistorted by a single large value.

A) MAD

B) MSE

C) MAPE

D) FIT

E) The smoothing constant

Diff: 2

Topic: Measuring forecast error

Objective: LO4-4

81) If Brandon Edward were working to develop a forecast using a moving averages approach, but he noticed a detectable trend in the historical data, he should

A) use weights to place more emphasis on recent data

B) use weights to minimize the importance of the trend

C) change to a naïve approach

D) use a simple moving average

E) change to a qualitative approach

Diff: 2

Topic: Moving averages

Objective: LO4-3

82) __________ forecasts are concerned with rates of technological progress, which can result in the birth of exciting new products, requiring new plants and equipment.

Diff: 1

Topic: Types of forecasts

Objective: LO4-1

83) __________ forecasts address the business cycle by predicting inflation rates, money supplies, housing starts, and other planning indicators.

Diff: 2

Topic: Types of forecasts

Objective: LO4-1

84) Demand forecasts, also called __________ forecasts, are projections of demand for a company's products or services.

Diff: 2

Topic: Types of forecasts

Objective: LO4-1

85) __________ forecasts employ one or more mathematical models that rely on historical data and/or associative variables to forecast demand.

Diff: 2

Topic: Forecasting approaches

Objective: LO4-2

86) __________ is a forecasting technique based upon salespersons' estimates of expected sales.

Diff: 2

Topic: Forecasting approaches

Objective: LO4-2

87) __________ forecasts use a series of past data points to make a forecast.

Diff: 2

Topic: Forecasting approaches

Objective: LO4-2

88) A(n) __________ forecast uses an average of the most recent periods of data to forecast the next period.

Diff: 2

Topic: Forecasting approaches

Objective: LO4-3

89) The smoothing constant is a weighting factor used in __________.

Diff: 2

Topic: Forecasting approaches

Objective: LO4-3

90) Linear regression is known as a(n) __________ because it incorporates variables or factors that might influence the quantity being forecast.

Diff: 1

Topic: Forecasting approaches

Objective: LO4-2

91) A measure of forecast error that does not depend on the magnitude of the item being forecast is the __________.

Diff: 1

Topic: Forecasting approaches

Objective: LO4-4

92) __________ is a measure of overall forecast error for a model.

Diff: 2

Topic: Forecasting approaches

Objective: LO4-4

93) When one constant is used to smooth the forecast average and a second constant is used to smooth the trend, the forecasting method is __________.

Diff: 2

Topic: Forecasting approaches

Objective: LO4-3

94) __________ is a time-series forecasting method that fits a trend line to a series of historical data points and then projects the line into the future for forecasts.

Diff: 2

Topic: Forecasting approaches

Objective: LO4-2

95) The __________ measures the strength of the relationship between two variables.

Diff: 2

Topic: Associative forecasting methods: Regression and correlation analysis

Objective: LO4-6

96) If a barbershop operator noted that Tuesday's business was typically twice as heavy as Wednesday's, and that Friday's business was typically the busiest of the week, business at the barbershop is subject to __________.

Diff: 2

Topic: Forecasting approaches: Seasonal variations in data

Objective: LO4-5

97) __________ forecasting tries a variety of computer models and selects the best one for a particular application.

Diff: 2

Topic: Monitoring and controlling forecasts

Objective: LO4-7

98) __________ are useful if we can assume that market demands will stay fairly steady over time.

Diff: 2

Topic: Moving averages

Objective: LO4-3

99) An approach to exponential smoothing in which the smoothing constant is automatically changed to keep errors to a minimum is called __________.

Diff: 2

Topic: Monitoring and controlling forecasts

Objective: LO4-3

100) A skeptical manager asks what short-range forecasts can be used for. Give her three possible uses/purposes.

Diff: 2

Topic: What is forecasting?

Objective: LO4-1

101) A skeptical manager asks what long-range forecasts can be used for. Give her three possible uses/purposes.

Diff: 2

Topic: What is forecasting?

Objective: LO4-1

102) Describe the three forecasting time horizons and their use.

Diff: 2

Topic: What is forecasting?

Objective: LO4-1

103) List and briefly describe the three major types of forecasts.

Diff: 2

Topic: Types of forecasts

Objective: LO4-2

104) Identify the seven steps involved in forecasting.

1. Determine the use of the forecast.

2. Select the items that are to be forecast.

3. Determine the time horizon of the forecast.

4. Select the forecasting model(s).

5. Gather the data needed to make the forecast.

6. Make the forecast.

7. Validate the forecasting mode and implement the results.

Diff: 2

Topic: Seven steps in the forecasting system

Objective: LO4-2

105) What are the realities of forecasting that companies face?

Diff: 2

Topic: Seven steps in the forecasting system

Objective: LO4-2

106) What are the differences between quantitative and qualitative forecasting methods?

Diff: 2

Topic: Forecasting approaches

Objective: LO4-2

107) Identify four quantitative forecasting methods.

Diff: 2

Topic: Forecasting approaches

Objective: LO4-2

108) What is a time-series forecasting model?

Diff: 1

Topic: Forecasting approaches

Objective: LO4-2

109) What is the difference between an associative model and a time-series model?

Diff: 2

Topic: Forecasting approaches

Objective: LO4-2

110) Name and discuss three qualitative forecasting methods.

Diff: 2

Topic: Forecasting approaches

Objective: LO4-2

111) Identify four components of a time series. Which one of these is rarely forecast? Why is this so?

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

112) Compare seasonal effects and cyclical effects.

Diff: 2

Topic: Time-series forecasting

Objective: LO4-5

113) Distinguish between a moving average model and an exponential smoothing model.

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

114) Describe three popular measures of forecast accuracy.

Diff: 2

Topic: Forecasting approaches: Measuring forecast error

Objective: LO4-4

115) Give an example–other than a restaurant or other food-service firm–of an organization that experiences an hourly seasonal pattern. (That is, each hour of the day has a pattern that tends to repeat day after day.) Explain.

Diff: 2

Topic: Time-series forecasting

AACSB: Reflective Thinking

Objective: LO4-5

116) Explain the role of regression models (time series and otherwise) in forecasting. That is, how is trend projection able to forecast? How is regression used for causal forecasting?

Diff: 3

Topic: Time-series forecasting

Objective: LO4-6

117) Identify three advantages of the moving average forecasting model. Identify three disadvantages of the moving average forecasting model.

Diff: 2

Topic: Time-series forecasting

Objective: LO4-3

118) What does it mean to "decompose" a time series?

Diff: 1

Topic: Time-series forecasting

Objective: LO4-3

119) Distinguish a dependent variable from an independent variable.

Diff: 2

Topic: Associative forecasting methods: Regression and correlation

Objective: LO4-6

120) Explain, in your own words, the meaning of the coefficient of determination.

Diff: 2

Topic: Associative forecasting methods: Regression and correlation

Objective: LO4-6

121) What is a tracking signal? Explain the connection between adaptive smoothing and tracking signals.

Diff: 2

Topic: Monitoring and controlling forecasts

Objective: LO4-7

122) What is focus forecasting?

Diff: 1

Topic: Monitoring and controlling forecasts

Objective: LO4-7

123) What is the key difference between weighted moving average and simple moving average approaches to forecasting?

Diff: 2

Topic: Time series forecasting; Moving averages

Objective: LO4-3

124) Weekly sales of ten-grain bread at the local organic food market are in the table below. Based on this data, forecast week 9 using a five-week moving average.

Week Sales

1 415

2 389

3 420

4 382

5 410

6 432

7 405

8 421

Diff: 1

Topic: Time-series forecasting

AACSB: Analytic Skills

Objective: LO4-3

125) Given the following data, calculate the three-year moving averages for years 4 through 10.

Diff: 2

Topic: Time-series forecasting

AACSB: Analytic Skills

Objective: LO4-3

126) What is the forecast for May based on a weighted moving average applied to the following past demand data and using the weights: 4, 3, 2 (largest weight is for most recent data)?

Diff: 1

Topic: Time-series forecasting

AACSB: Analytic Skills

Objective: LO4-3

127) Weekly sales of copy paper at Cubicle Suppliers are in the table below. Compute a three-period moving average and a four-period moving average for weeks 5, 6, and 7. Compute MAD for each forecast. Which model is more accurate? Forecast week 8 with the more accurate method.

Week Sales (cases)

1 17

2 21

3 27

4 31

5 19

6 17

7 21

The four-week moving average is more accurate. The forecast with the 4-moving average is 22.0.

Diff: 2

Topic: Time-series forecasting

AACSB: Analytic Skills

Objective: LO4-4

128) The last four weekly values of sales were 80, 100, 105, and 90 units. The last four forecasts (for the same four weeks) were 60, 80, 95, and 75 units. Calculate MAD, MSE, and MAPE for these four weeks.

Diff: 2

Topic: Time series forecasting: Measuring forecast error

AACSB: Analytic Skills

Objective: LO4-4

129) A management analyst is using exponential smoothing to predict merchandise returns at an upscale branch of a department store chain. Given an actual number of returns of 154 items in the most recent period completed, a forecast of 172 items for that period, and a smoothing constant of 0.3, what is the forecast for the next period? How would the forecast be changed if the smoothing constant were 0.6? Explain the difference in terms of alpha and responsiveness.

Diff: 1

Topic: Time-series forecasting

AACSB: Analytic Skills

Objective: LO4-3

130) The following trend projection is used to predict quarterly demand: Y = 250 - 2.5t, where t = 1 in the first quarter. Seasonal (quarterly) indices are Quarter 1 = 1.5; Quarter 2 = 0.8; Quarter 3 = 1.1; and Quarter 4 = 0.6. What is the seasonally adjusted forecast for the next four quarters?

1 247.5 371.25

2 245 196

3 242.5 266.75

4 240 144

Diff: 2

Topic: Time-series forecasting

AACSB: Analytic Skills

Objective: LO4-5

131) Jim's department at a local department store has tracked the sales of a product over the last ten weeks. Forecast demand using exponential smoothing with an alpha of 0.4, and an initial forecast of 28.0 for period 1. Calculate the MAD. What do you recommend?

The tracking signal RSFE/MAD = 2.64/3.56 = .742 is low; therefore, keep using the forecasting method.

Diff: 2

Topic: Time-series forecasting, and monitoring and controlling forecasts

AACSB: Analytic Skills

Objective: LO4-4

132) Favors Distribution Company purchases small imported trinkets in bulk, packages them, and sells them to retail stores. They are conducting an inventory control study of all their items. The following data are for one such item, which is not seasonal.

a. Use trend projection to estimate the relationship between time and sales (state the equation).

b. Calculate forecasts for the first four months of the next year.

Diff: 2

Topic: Time-series forecasting

AACSB: Analytic Skills

Objective: LO4-6

133) Use exponential smoothing with trend adjustment to forecast deliveries for period 10. Let alpha = 0.4, beta = 0.2, and let the initial trend value be 4 and the initial forecast be 200.

Diff: 2

Topic: Time-series forecasting

AACSB: Analytic Skills

Objective: LO4-3

134) A small family-owned restaurant uses a seven-day moving average model to determine manpower requirements. These forecasts need to be seasonalized because each day of the week has its own demand pattern. The seasonal indices for each day of the week are: Monday, 0.445; Tuesday, 0.791; Wednesday, 0.927; Thursday, 1.033; Friday, 1.422; Saturday, 1.478; and Sunday 0.903. Average daily demand based on the most recent moving average is 194 patrons. What is the seasonalized forecast for each day of next week?

Diff: 2

Topic: Associative forecasting methods: Regression and correlation

AACSB: Analytic Skills

Objective: LO4-5

135) A restaurant has tracked the number of meals served at lunch over the last four weeks. The data shows little in terms of trends, but does display substantial variation by day of the week. Use the following information to determine the seasonal (daily) index for this restaurant.

Diff: 2

Topic: Time-series forecasting

AACSB: Analytic Skills

Objective: LO4-5

136) A firm has modeled its experience with industrial accidents and found that the number of accidents per year (Y) is related to the number of employees (X) by the regression equation Y = 3.3 + 0.049*X. R-Square is 0.68. The regression is based on 20 annual observations. The firm intends to employ 480 workers next year. How many accidents do you project? How much confidence do you have in that forecast?

Diff: 2

Topic: Associative forecasting methods: Regression and correlation

AACSB: Analytic Skills

Objective: LO4-6

137) Demand for a certain product is forecast to be 8,000 units per month, averaged over all 12 months of the year. The product follows a seasonal pattern, for which the January monthly index is 1.25. What is the seasonally-adjusted sales forecast for January?

Diff: 1

Topic: Time-series forecasting

AACSB: Analytic Skills

Objective: LO4-5

138) A seasonal index for a monthly series is about to be calculated on the basis of three years' accumulation of data. The three previous July values were 110, 135, and 130. The average over all months is 160. The approximate seasonal index for July is:

Diff: 2

Topic: Time-series forecasting

AACSB: Analytic Skills

Objective: LO4-5

139) Marie Bain is the production manager at a company that manufactures hot water heaters. Marie needs a demand forecast for the next few years to help decide whether to add new production capacity. The company's sales history (in thousands of units) is shown in the table below. Use exponential smoothing with trend adjustment, to forecast demand for period 6. The initial forecast for period 1 was 11 units; the initial estimate of trend was 0. The smoothing constants are α = .3 and β = .3

Diff: 2

Topic: Time-series forecasting

AACSB: Analytic Skills

Objective: LO4-3

140) The quarterly sales for specific educational software over the past three years are given in the following table. Compute the four seasonal factors.

Diff: 2

Topic: Time-series forecasting

AACSB: Analytic Skills

Objective: LO4-5

141) An innovative restaurateur owns and operates a dozen "Ultimate Low-Carb" restaurants in northern Arkansas. His signature item is a cheese-encrusted beef medallion wrapped in lettuce. Sales (X, in millions of dollars) is related to Profits (Y, in hundreds of thousands of dollars) by the regression equation Y = 8.21 + 0.76 X. What is your forecast of profit for a store with sales of $40 million? $50 million?

Diff: 2

Topic: Associative forecasting methods: Regression and correlation

AACSB: Analytic Skills

Objective: LO4-6

142) Arnold Tofu owns and operates a chain of 12 vegetable protein "hamburger" restaurants in northern Louisiana. Sales figures and profits for the stores are in the table below. Sales are given in millions of dollars; profits are in hundreds of thousands of dollars. Calculate a regression line for the data. What is your forecast of profit for a store with sales of $24 million? $30 million?

Diff: 2

Topic: Associative forecasting methods: Regression and correlation

Objective: LO4-6

143) The department manager using a combination of methods has forecast sales of toasters at a local department store. Calculate the MAD for the manager's forecast. Compare the manager's forecast against a naive forecast. Which is better?

The manager's forecast has a MAD of 18.83, while the naive is 19.33. Therefore, the manager's forecast is slightly better than the naive.

Diff: 2

Topic: Monitoring and controlling forecasts

AACSB: Analytic Skills

Objective: LO4-4

144) The last seven weeks of demand at a new car dealer are shown below. Use a three-period weighted-moving average to determine a forecast for the 8th week using weights of 1, 2, and 3. Calculate the MAD for this forecast. What does the MAD indicate?

Week Sales

1 25

2 30

3 27

4 31

5 27

6 29

7 30

Week Sales 3WMA |error|

1 25

2 30

3 27

4 31 28 3

5 27 30 3

6 29 28 1

7 30 29 1

8 29

MAD = 8/4 = 2

An MAD of 2 means that the forecasting technique used was typically off by 2 units each period.

Diff: 2

Topic: Time-series forecasting, moving averages, and measuring forecast error

Objective: LO4-4