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ch3 – Forecasting statements (True/False)

Total questions: 157

Worksheet time: 1hrs 20mins

Name
Class
Date
1.

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

a)

True

b)

False

2.

For new products in a strong growth mode, a low alpha will minimize forecast errors when using exponential smoothing techniques. True False

a)

True

b)

False

3.

Once accepted by managers, forecasts should be held firm regardless of new input since many plans have been made using the original forecast. True False

a)

True

b)

False

4.

Forecasts for groups of items tend to be less accurate than forecasts for individual items because forecasts for individual items don't include as many influencing factors. True False

a)

True

b)

False

5.

Forecasts help managers plan both the system itself and provide valuable information for using the system. True False

a)

True

b)

False

6.

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

a)

True

b)

False

7.

When new products or services are introduced, focus forecasting models are an attractive option. True False

a)

True

b)

False

8.

The purpose of the forecast should be established first so that the level of detail, amount of resources, and accuracy level can be understood. True False

a)

True

b)

False

9.

Forecasts based on time series (historical) data are referred to as associative forecasts. True False

a)

True

b)

False

10.

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

a)

True

b)

False

11.

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

a)

True

b)

False

12.

The Delphi approach involves the use of a series of questionnaires to achieve a consensus forecast. True False

a)

True

b)

False

13.

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

a)

True

b)

False

14.

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

a)

True

b)

False

15.

Forecasting techniques that are based on time series data assume that future values of the series will duplicate past values. True False

a)

True

b)

False

16.

Trend adjusted exponential smoothing uses double smoothing to add twice the forecast error to last period's actual demand. True False

a)

True

b)

False

17.

Forecasts based on an average tend to exhibit less variability than the original data. True False

a)

True

b)

False

18.

The naive approach to forecasting requires a linear trend line. True False

a)

True

b)

False

19.

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

a)

True

b)

False

20.

The naive forecast can serve as a quick and easy standard of comparison against which to judge the cost and accuracy of other techniques. True False

a)

True

b)

False

21.

A moving average forecast tends to be more responsive to changes in the data series when more data points are included in the average. True False

a)

True

b)

False

22.

In order to update a moving average forecast, the values of each data point in the average must be known. True False

a)

True

b)

False

23.

Forecasts of future demand are used by operations people to plan capacity. True False

a)

True

b)

False

24.

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

a)

True

b)

False

25.

Exponential smoothing is a form of weighted averaging. True False

a)

True

b)

False

26.

A smoothing constant of .1 will cause an exponential smoothing forecast to react more quickly to a sudden change than a smoothing constant value of .3. True False

a)

True

b)

False

27.

The T in the model TAF = S+T represents the time dimension (which is usually expressed in weeks or months). True False

a)

True

b)

False

28.

Trend adjusted exponential smoothing requires selection of two smoothing constants. True False

a)

True

b)

False

29.

An advantage of "trend adjusted exponential smoothing" over the "linear trend equation" is its ability to adjust over time to changes in the trend. True False

a)

True

b)

False

30.

A seasonal relative (or seasonal indexes) is expressed as a percentage of average or trend. True False

a)

True

b)

False

31.

In order to compute seasonal relatives, the trend of past data must be computed or known which means that for brand new products this approach can't be used. True False

a)

True

b)

False

32.

Removing the seasonal component from a data series (de-seasonalizing) can be accomplished by dividing each data point by its appropriate seasonal relative. True False

a)

True

b)

False

33.

If a pattern appears when a dependent variable is plotted against time, one should use time series analysis instead of regression analysis. True False

a)

True

b)

False

34.

Curvilinear and multiple regression procedures permit us to extend associative models to relationships that are non-linear or involve more than one predictor variable. True False

a)

True

b)

False

35.

The sample standard deviation of forecast error is equal to the square root of MSE. True False

a)

True

b)

False

36.

Correlation measures the strength and direction of a relationship between variables. True False

a)

True

b)

False

37.

MAD is equal to the square root of MSE which is why we calculate the easier MSE and then calculate the more difficult MAD. True False

a)

True

b)

False

38.

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

a)

True

b)

False

39.

A forecast method is generally deemed to perform adequately when the errors exhibit an identifiable pattern. True False

a)

True

b)

False

40.

A control chart involves setting action limits for cumulative forecast error. True False

a)

True

b)

False

41.

A tracking signal focuses on the ratio of cumulative forecast error to the corresponding value of MAD. True False

a)

True

b)

False

42.

The use of a control chart assumes that errors are normally distributed about a mean of zero. True False

a)

True

b)

False

43.

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

a)

True

b)

False

44.

Bias is measured by the cumulative sum of forecast errors. True False

a)

True

b)

False

45.

Seasonal relatives can be used to de-seasonalize data or incorporate seasonality in a forecast. True False

a)

True

b)

False

46.

The best forecast is not necessarily the most accurate.

a)

True

b)

False

47.

A proactive approach to forecasting views forecasts as probable descriptions of future demand, and requires action to be taken to meet that demand.

a)

True

b)

False

48.

Simple linear regression applies to linear relationships with no more than three independent variables.

a)

True

b)

False

49.

An important goal of forecasting is to minimize the average forecast error.

a)

True

b)

False

50.

Forecasting techniques such as moving averages, exponential smoothing, and the naive approach all represent smoothed (averaged) values of time series data.

a)

True

b)

False

51.

In exponential smoothing, an alpha of 0.300.30 will cause a forecast to react more quickly to a large error than will an alpha of 0.200.20 .

a)

True

b)

False

52.

Forecasts based on judgment and opinion don't include:

a)

executive opinion

b)

salesperson opinion

c)

second opinions

d)

customer surveys

e)

Delphi methods

53.

In business, forecasts are the basis for:

a)

capacity planning

b)

budgeting

c)

sales planning

d)

production planning

e)

all of the above

54.

Which of the following features would not generally be considered common to all forecasts?

a)

Assumption of a stable underlying causal system.

b)

Actual results will differ somewhat from predicted values.

c)

Historical data is available on which to base the forecast.

d)

Forecasts for groups of items tend to be more accurate than forecasts for individual items.

e)

Accuracy decreases as the time horizon increases.

55.

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

a)

determine the purpose and level of detail required

b)

eliminate all assumptions

c)

establish a time horizon

d)

select a forecasting model

e)

monitor the forecast

56.

Minimizing the sum of the squared deviations around the line is called:

a)

mean squared error technique

b)

mean absolute deviation

c)

double smoothing

d)

least squares estimation

e)

predictor regression

57.

The two general approaches to forecasting are:

a)

mathematical and statistical

b)

qualitative and quantitative

c)

judgmental and qualitative

d)

historical and associative

e)

precise and approximation

58.

Which of the following is not a type of judgmental forecasting?

a)

executive opinions

b)

sales force opinions

c)

consumer surveys

d)

the Delphi method

e)

time series analysis

59.

Accuracy in forecasting can be measured by:

a)

MSE

b)

MRP

c)

MAPE

d)

MTM

e)

A & C

60.

Which of the following would be an advantage of using a sales force composite to develop a demand forecast?

a)

The sales staff is least affected by changing customer needs.

b)

The sales force can easily distinguish between customer desires and probable actions.

c)

The sales staff is often aware of customers' future plans.

d)

Salespeople are least likely to be influenced by recent events.

e)

Salespeople are least likely to be biased by sales quotas.

61.

Which phrase most closely describes the Delphi technique?

a)

associative forecast

b)

consumer survey

c)

series of questionnaires

d)

developed in India

e)

historical data

62.

The forecasting method which uses anonymous questionnaires to achieve a consensus forecast is:

a)

sales force opinions

b)

consumer surveys

c)

the Delphi method

d)

time series analysis

e)

executive opinions

63.

One reason for using the Delphi method in forecasting is to:

a)

avoid premature consensus (bandwagon effect)

b)

achieve a high degree of accuracy

c)

maintain accountability and responsibility

d)

be able to replicate results

e)

prevent hurt feelings

64.

Detecting non-randomness in errors can be done using:

a)

MSEs

b)

MAPEs

c)

Control Charts

d)

Correlation Coefficients

e)

Strategies

65.

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

a)

seasonal variation

b)

cycles

c)

irregular variation

d)

trend

e)

random variation

66.

The primary difference between seasonality and cycles is:

a)

the duration of the repeating patterns

b)

the magnitude of the variation

c)

the ability to attribute the pattern to a cause

d)

the direction of the movement

e)

there are only 4 seasons but 30 cycles

67.

Averaging techniques are useful for:

a)

distinguishing between random and non-random variations

b)

smoothing out fluctuations in time series

c)

eliminating historical data

d)

providing accuracy in forecasts

e)

average people

68.

Putting forecast errors into perspective is best done using:

a)

Exponential smoothing

b)

MAPE

c)

Linear decision rules

d)

MAD

e)

Hindsight

69.

Using the latest observation in a sequence of data to forecast the next period is:

a)

a moving average forecast

b)

a naive forecast

c)

an exponentially smoothed forecast

d)

an associative forecast

e)

regression analysis

70.

For the data given below, what would the naive forecast be for the next period (period #5)? Period and demand values are: 1: 5858 , 2: 5959 , 3: 6060 , 4: 6161 .

a)

58

b)

62

c)

59.5

d)

61

e)

cannot tell from the data given

71.

Moving average forecasting techniques do the following:

a)

immediately reflect changing patterns in the data

b)

lead changes in the data

c)

smooth variations in the data

d)

operate independently of recent data

e)

assist when organizations are relocating

72.

Which is not a characteristic of simple moving averages applied to time series data?

a)

smoothes random variations in the data

b)

weights each historical value equally

c)

lags changes in the data

d)

requires only last period's forecast and actual data

e)

smoothes real variations in the data

73.

In order to increase the responsiveness of a forecast made using the moving average technique, the number of data points in the average should be:

a)

decreased

b)

increased

c)

multiplied by a larger alpha

d)

multiplied by a smaller alpha

e)

eliminated if the MAD is greater than the MSE

74.

A forecast based on the previous forecast plus a percentage of the forecast error is:

a)

a naive forecast

b)

a simple moving average forecast

c)

a centered moving average forecast

d)

an exponentially smoothed forecast

e)

an associative forecast

75.

Which is not a characteristic of exponential smoothing?

a)

smoothes random variations in the data

b)

weights each historical value equally

c)

has an easily altered weighting scheme

d)

has minimal data storage requirements

e)

smoothes real variations in the data

76.

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

a)

0

b)

.01

c)

.1

d)

.5

e)

1.0

77.

Simple exponential smoothing is being used to forecast demand. The previous forecast of 66 turned out to be four units less than actual demand. The next forecast is 66.6, implying a smoothing constant, alpha, equal to:

a)

.01

b)

.10

c)

.15

d)

.20

e)

.60

78.

Given an actual demand of 59, a previous forecast of 64, and an alpha of .3, what would the forecast for the next period be using simple exponential smoothing?

a)

36.9

b)

57.5

c)

60.5

d)

62.5

e)

65.5

79.

Given an actual demand of 105, a forecasted value of 97, and an alpha of .4, the simple exponential smoothing forecast for the next period would be:

a)

80.8

b)

93.8

c)

100.2

d)

101.8

e)

108.2

80.

Which of the following possible values of alpha would cause exponential smoothing to respond the most quickly to forecast errors?

a)

0

b)

.01

c)

.05

d)

.10

e)

.15

81.

A manager uses the following equation to predict monthly receipts: Y_t = 40,000 + 150t. What is the forecast for July if t = 0 in April of this year?

a)

40,450

b)

40,600

c)

42,100

d)

42,250

e)

42,400

82.

In trend-adjusted exponential smoothing, the trend adjusted forecast (TAF) consists of:

a)

an exponentially smoothed forecast and a smoothed trend factor

b)

an exponentially smoothed forecast and an estimated trend value

c)

the old forecast adjusted by a trend factor

d)

the old forecast and a smoothed trend factor

e)

a moving average and a trend factor

83.

In the "additive" model for seasonality, seasonality is expressed as a __________ adjustment to the average; in the multiplicative model, seasonality is expressed as a __________ adjustment to the average.

a)

quantity, percentage

b)

percentage, quantity

c)

quantity, quantity

d)

percentage, percentage

e)

qualitative, quantitative

84.

Which technique is used in computing seasonal relatives?

a)

double smoothing

b)

Delphi

c)

Mean Squared Error (MSE)

d)

centered moving average

e)

exponential smoothing

85.

A persistent tendency for forecasts to be greater than or less than the actual values is called:

a)

bias

b)

tracking

c)

control charting

d)

positive correlation

e)

linear regression

86.

Which of the following might be used to indicate the cyclical component of a forecast?

a)

leading variable

b)

Mean Squared Error (MSE)

c)

Delphi technique

d)

exponential smoothing

e)

Mean Absolute Deviation (MAD)

87.

The primary method for associative forecasting is:

a)

sensitivity analysis

b)

regression analysis

c)

simple moving averages

d)

centered moving averages

e)

exponential smoothing

88.

Which term most closely relates to associative forecasting techniques?

a)

time series data

b)

expert opinions

c)

Delphi technique

d)

consumer survey

e)

predictor variables

89.

Which of the following corresponds to the predictor variable in simple linear regression?

a)

regression coefficient

b)

dependent variable

c)

independent variable

d)

predicted variable

e)

demand coefficient

90.

The mean absolute deviation (MAD) is used to:

a)

estimate the trend line

b)

eliminate forecast errors

c)

measure forecast accuracy

d)

seasonally adjust the forecast

e)

all of the above

91.

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

a)

4

b)

3

c)

5

d)

6

e)

12

92.

Given forecast errors of 5, 0, - 4, and 3, what is the mean absolute deviation?

a)

4

b)

3

c)

2.5

d)

2

e)

1

93.

Given forecast errors of 5, 0, - 4, and 3, what is the bias?

a)

- 4

b)

4

c)

5

d)

12

e)

6

94.

Which of the following is used for constructing a control chart?

a)

mean absolute deviation (MAD)

b)

mean squared error (MSE)

c)

tracking signal (TS)

d)

bias

e)

none of the above

95.

The two most important factors in choosing a forecasting technique are:

a)

cost and time horizon

b)

accuracy and time horizon

c)

cost and accuracy

d)

quantity and quality

e)

objective and subjective components

96.

The degree of management involvement in short range forecasts is:

a)

none

b)

low

c)

moderate

d)

high

e)

total

97.

Which of the following is not necessarily an element of a good forecast?

a)

estimate of accuracy

b)

timeliness

c)

meaningful units

d)

low cost

e)

written

98.

Current information on __________ can have a significant impact on forecast accuracy:

a)

prices

b)

promotion

c)

inventory

d)

competition

e)

all of the above

99.

A managerial approach toward forecasting which seeks to actively influence demand is

a)

reactive

b)

proactive

c)

influential

d)

protracted

e)

retroactive

100.

Customer service levels can be improved by better

a)

mission statements

b)

control charting

c)

short term forecast accuracy

d)

exponential smoothing

e)

customer selection

101.

Given the following historical data, what is the simple three-period moving average forecast for period 6? Period and Value: 1 — 73, 2 — 68, 3 — 65, 4 — 72, 5 — 67

a)

67

b)

115

c)

69

d)

68

e)

68.67

102.

Given the following historical data and weights of .5, .3, and .2, what is the three-period moving average forecast for period 5? Period and Value: 1 — 138, 2 — 142, 3 — 148, 4 — 144

a)

144.20

b)

144.80

c)

144.67

d)

143.00

e)

144.00

103.

Use of simple linear regression analysis assumes that

a)

Variations around the line are random.

b)

Deviations around the line are normally distributed.

c)

Predictions are to be made only within the range of observed values of the predictor variable.

d)

all of the above

e)

none of the above

104.

Given forecast errors of -5, -10, and +15, what is the MAD?

a)

0

b)

10

c)

30

d)

175

e)

none of these

105.

Develop a forecast for the next period using a 3-period moving average. Period and Demand: 1 — 19, 2 — 20, 3 — 18, 4 — 19, 5 — 17

a)

17

b)

18

c)

19

d)

20

106.

Consider the data below. Using exponential smoothing with alpha = .2, and assuming the forecast for period 11 was 80, what would the forecast for period 14 be? Period and Demand: 11 — 81, 12 — 75, 13 — 82

a)

79.16

b)

79.73

c)

80.20

d)

82.00

107.

A manager is using exponential smoothing to predict merchandise returns at a suburban branch of a department store chain. Given a previous forecast of 140 items, an actual number of returns of 148 items, and a smoothing constant equal to .15, what is the forecast for the next period?

a)

140.0

b)

141.2

c)

148.0

d)

142.0

108.

A manager is using the equation below to forecast quarterly demand for a product: Y_t = 6,000 + 80 t where t = 0 at Q2 of last year. Quarter relatives are Q1 = 0.6, Q2 = 0.9, Q3 = 1.3, and Q4 = 1.2. What forecasts are appropriate for the last quarter of this year and the first quarter of next year?

a)

Q4 this year: 7,776; Q1 next year: 3,936

b)

Q4 this year: 7,200; Q1 next year: 3,600

c)

Q4 this year: 6,480; Q1 next year: 4,680

d)

Q4 this year: 7,776; Q1 next year: 4,320

109.

Over the past five years, a firm's sales have averaged 250 units in the first quarter of each year, 100 units in the second quarter, 150 units in the third quarter, and 300 units in the fourth quarter. Which set of quarter relatives is appropriate for this firm's sales?

a)

Q1 1.25, Q2 0.50, Q3 0.75, Q4 1.50

b)

Q1 1.20, Q2 0.80, Q3 1.00, Q4 1.40

c)

Q1 1.00, Q2 1.00, Q3 1.00, Q4 1.00

d)

Q1 0.75, Q2 1.25, Q3 1.50, Q4 0.50

110.

A manager has been using a certain technique to forecast demand for gallons of ice cream for the past six periods. Actual and predicted amounts are shown below. Would a naive forecast have produced better results? Period, Demand, Forecast: 1 — 90, 87; 2 — 85, 88; 3 — 91, 87; 4 — 92, 89; 5 — 95, 90; 6 — 88, 92

a)

Yes

b)

No

c)

They would be the same

111.

A new car dealer has been using exponential smoothing with an alpha of .2 to forecast weekly new car sales. Given the data below, would a naive forecast have provided greater accuracy? Assume an initial exponential forecast of 60 units in period 2 (no forecast for period 1). Period and Demand: 1 — 57, 2 — 62, 3 — 58, 4 — 60, 5 — 60, 6 — 56

a)

A naive forecast would be more accurate.

b)

Exponential smoothing would be more accurate.

c)

Both methods would have the same accuracy.

112.

A CPA firm has been using the following equation to predict annual demand for tax audits: Y_t = 55 + 4 t. Demand for the past few years is shown below. Is the forecast performing as well as it might? Year and Demand: 2 — 60, 3 — 65, 4 — 69, 5 — 76, 6 — 85, 7 — 85

a)

Yes, the model fits very well across all years.

b)

No, it underestimates later years and should be re-estimated.

c)

Yes, because errors are exactly zero.

d)

No, it overestimates early years and late years by large amounts.

113.

Given the data below, develop a forecast for period 6 using a four-period weighted moving average and weights of .4, .3, .2 and .1. Period and Demand: 1 — 19, 2 — 20, 3 — 18, 4 — 19, 5 — 17

a)

17.9

b)

18.1

c)

18.5

d)

19.0

114.

Use linear regression to develop a predictive model for demand for burial vaults based on sales of caskets. Year, Sales of Caskets (000), Demand for Vaults (000): 1 — 8, 5; 2 — 7, 2; 3 — 10, 6; 4 — 6, 4. What is the best-fit regression equation for demand (y) as a function of casket sales (x)?

a)

y = -1.29 + 0.71 x

b)

y = 0.50 + 0.80 x

c)

y = 2.00 + 0.50 x

d)

y = -0.75 + 1.10 x

115.

Given the following data, develop a linear regression model for y as a function of x. Data pairs (x, y): (8, 10), (4, 5), (10, 15), (15, 20), (7, 10)

a)

y = -0.11 + 1.38 x

b)

y = 1.00 + 1.10 x

c)

y = 0.00 + 1.20 x

d)

y = 2.50 + 0.90 x

116.

Given the following data, develop a linear regression model for y as a function of x. Data pairs (x, y): (2, 20), (4, 25), (6, 30), (6, 32), (8, 40)

a)

y = 12.50 + 3.25 x

b)

y = 10.00 + 2.00 x

c)

y = 15.00 + 3.00 x

d)

y = 5.00 + 4.00 x

117.

Develop a linear trend equation for the data on bread deliveries shown below. Forecast deliveries for period 11 through 14. Period | Deliveries (dozens) 1 | 648 2 | 590 3 | 631 4 | 769 5 | 745 6 | 856 7 | 760 8 | 962 9 | 990 10 | 1100
Y14 = ?

(a)  

118.

The president of State University wants to forecast student enrollments for this academic year based on the following historical data: Year | Enrollments 5 years ago | 1500015000 4 years ago | 1600016000 3 years ago | 1800018000 2 years ago | 2000020000 Last year | 2100021000 What is the forecast for this year using the naive approach?

a)

1875018750

b)

1950019500

c)

2100021000

d)

2200022000

e)

2280022800

119.

Demand for the last four months was: Month | Demand March | 66 April | 88 May | 1010 June | 88 Predict demand for July using a 3-period moving average.

(a)  

120.

Demand for the last four months was: Month | Demand March | 66 April | 88 May | 1010 June | 88 Predict demand for July using exponential smoothing with alpha equal to 0.20.2 (use a naive forecast for April for your first forecast).

(a)  

121.

If the naive approach had been used to predict demand for April through June using March, April, and May actuals respectively, what would MAD have been for those months? Month | Actual | Naive Forecast April | 88 | 66 May | 1010 | 88 June | 88 | 1010

a)

1

b)

1.31.3

c)

1.71.7

d)

22

122.

A manager wants to choose one of two forecasting alternatives. Each alternative was tested using historical data. The resulting forecast errors for the two are shown in the table. Analyze the data and recommend a course of action to the manager. Period (t): 1 2 3 4 5 6 7 8 9 Alt #1 errors: 3 −2 0 2 1 −2 2 −1 2 Alt #2 errors: 4 3 −3 −1 1 −1 0 1 0

a)

Choose Alternative #1 based on lower MSE

b)

Choose Alternative #2 based on lower MAD

c)

Both alternatives are identical; choose either

d)

Neither alternative is usable

123.

A manager uses this equation to predict demand: Yt=20+4tY_t = 20 + 4t . Over the past 8 periods, demand has been as follows: 25, 28, 31, 34, 36, 43, 50, 54. Are the results acceptable?

a)

Yes, errors are small with little bias

b)

No, errors are very large and steadily increasing

c)

No, the model predicts a decreasing trend

d)

Yes, because forecasts exactly match actuals

124.

The president of State University wants to forecast student enrollments for this academic year based on the following historical data: Year | Enrollments 5 years ago | 1500015000 4 years ago | 1600016000 3 years ago | 1800018000 2 years ago | 2000020000 Last year | 2100021000 What is the forecast for this year using a four-year simple moving average?

a)

1875018750

b)

1950019500

c)

2100021000

d)

2265022650

e)

2280022800

125.

What is the forecast for this year using exponential smoothing with alpha = 0.50.5 , if the forecast for two years ago was 1600016000 ? Year | Enrollments 5 years ago | 1500015000 4 years ago | 1600016000 3 years ago | 1800018000 2 years ago | 2000020000 Last year | 2100021000

a)

1875018750

b)

1950019500

c)

2100021000

d)

2265022650

e)

2280022800

126.

What is the forecast for this year using the least squares trend line for these data? Year | Enrollments 5 years ago | 1500015000 4 years ago | 1600016000 3 years ago | 1800018000 2 years ago | 2000020000 Last year | 2100021000

a)

1875018750

b)

1950019500

c)

2100021000

d)

2265022650

e)

2280022800

127.

What is the forecast for this year using trend adjusted (double) smoothing with alpha = 0.050.05 and beta = 0.30.3 , if the forecast for last year was 2100021000 , the forecast for two years ago was 1900019000 , and the trend estimate for last year's forecast was 15001500 ?

a)

1875018750

b)

1950019500

c)

2100021000

d)

2265022650

e)

2280022800

128.

The business analyst for Video Sales, Inc. wants to forecast this year's demand for DVD decoders based on the following historical data: Year | Demand 5 years ago | 900900 4 years ago | 700700 3 years ago | 600600 2 years ago | 500500 Last year | 300300 What is the forecast for this year using the naive approach?

a)

163163

b)

180180

c)

300300

d)

420420

e)

510510

129.

What is the forecast for this year using a three-year weighted moving average with weights of 0.50.5 , 0.30.3 , and 0.20.2 (most recent gets weight 0.50.5 )? Year | Demand 3 years ago | 600600 2 years ago | 500500 Last year | 300300

a)

163163

b)

180180

c)

300300

d)

420420

e)

510510

130.

What is the forecast for this year using exponential smoothing with alpha = 0.40.4 , if the forecast for two years ago was 750750 ? Year | Demand 5 years ago | 900900 4 years ago | 700700 3 years ago | 600600 2 years ago | 500500 Last year | 300300

a)

163163

b)

180180

c)

300300

d)

420420

e)

510510

131.

What is the forecast for this year using the least squares trend line for these data? Year | Demand 5 years ago | 900900 4 years ago | 700700 3 years ago | 600600 2 years ago | 500500 Last year | 300300

a)

163163

b)

180180

c)

300300

d)

420420

e)

510510

132.

What is the forecast for this year using trend adjusted (double) smoothing with alpha = 0.30.3 and beta = 0.20.2 , if the forecast for last year was 310310 , the forecast for two years ago was 430430 , and the trend estimate for last year's forecast was −150-150 ?

a)

162.4162.4

b)

180.3180.3

c)

301.4301.4

d)

403.2403.2

e)

510.0510.0

133.

Professor Very Busy needs to allocate time next week to include time for office hours. He needs to forecast the number of students who will seek appointments. He has gathered the following data: Week | # Students 6 weeks ago | 8383 5 weeks ago | 110110 4 weeks ago | 9595 3 weeks ago | 8080 2 weeks ago | 6565 Last week | 5050 What is this week's forecast using the naive approach?

a)

4545

b)

5050

c)

5252

d)

6565

e)

7878

134.

Professor Very Busy has the following recent data on student appointments: 3 weeks ago | 8080 2 weeks ago | 6565 Last week | 5050 What is this week's forecast using a three-week simple moving average?

a)

4949

b)

5050

c)

5252

d)

6565

e)

7878

135.

What is this week's forecast using exponential smoothing with alpha = 0.20.2 , if the forecast for two weeks ago was 9090 ? Week | Actuals 2 weeks ago | 6565 Last week | 5050

a)

4949

b)

5050

c)

5252

d)

6565

e)

7777

136.

What is this week's forecast using the least squares trend line for these data? Week | # Students 6 weeks ago | 8383 5 weeks ago | 110110 4 weeks ago | 9595 3 weeks ago | 8080 2 weeks ago | 6565 Last week | 5050

a)

4949

b)

5050

c)

5252

d)

6565

e)

7878

137.

What is this week's forecast using trend adjusted (double) smoothing with alpha = 0.50.5 and beta = 0.10.1 , if the forecast for last week was 6565 , the forecast for two weeks ago was 7575 , and the trend estimate for last week's forecast was −5-5 ?

a)

49.349.3

b)

50.650.6

c)

51.351.3

d)

65.465.4

e)

78.778.7

138.

A concert promoter is forecasting this year's attendance for one of his concerts based on the following historical data: Four years ago 10,000; Three years ago 12,000; Two years ago 18,000; Last year 20,000. What is this year's forecast using the naive approach?

a)

22,000

b)

20,000

c)

18,000

d)

15,000

e)

12,000

139.

A concert promoter is forecasting this year's attendance for one of his concerts using a two-year weighted moving average with weights of 0.7 and 0.3. Historical data: Four years ago 10,000; Three years ago 12,000; Two years ago 18,000; Last year 20,000. What is this year's forecast?

a)

19,400

b)

18,600

c)

19,000

d)

11,400

e)

10,600

140.

A concert promoter uses exponential smoothing with alpha = 0.2. Historical data: Four years ago 10,000; Three years ago 12,000; Two years ago 18,000; Last year 20,000. If last year's smoothed forecast was 15,000, what is this year's forecast?

a)

20,000

b)

19,000

c)

17,500

d)

16,000

e)

15,000

141.

A concert promoter is forecasting this year's attendance using the least squares trend line for these data: Four years ago 10,000; Three years ago 12,000; Two years ago 18,000; Last year 20,000. What is this year's forecast?

a)

20,000

b)

21,000

c)

22,000

d)

23,000

e)

24,000

142.

The previous trend line had predicted 18,500 for two years ago and 19,700 for last year. Based on the actual attendances of 18,000 two years ago and 20,000 last year, what was the mean absolute deviation (MAD) for these forecasts?

a)

100

b)

200

c)

400

d)

500

e)

800

143.

The dean of a school of business is forecasting total student enrollment for this year's summer session classes based on the following historical data: Four years ago 2,000; Three years ago 2,200; Two years ago 2,800; Last year 3,000. What is this year's forecast using the naive approach?

a)

2,000

b)

2,200

c)

2,800

d)

3,000

e)

none of the above

144.

Using the same enrollment data: Four years ago 2,000; Three years ago 2,200; Two years ago 2,800; Last year 3,000. What is this year's forecast using a three-year simple moving average?

a)

2,667

b)

2,600

c)

2,500

d)

2,400

e)

2,333

145.

Using exponential smoothing with alpha = 0.4 and last year's smoothed forecast of 2,600, what is this year's forecast for total student enrollment if last year's actual enrollment was 3,000?

a)

2,600

b)

2,760

c)

2,800

d)

3,840

e)

3,000

146.

For the enrollment data: Four years ago 2,000; Three years ago 2,200; Two years ago 2,800; Last year 3,000. What is the annual rate of change (slope) of the least squares trend line for these data?

a)

0

b)

200

c)

400

d)

180

e)

360

147.

For the enrollment data: Four years ago 2,000; Three years ago 2,200; Two years ago 2,800; Last year 3,000. What is this year's forecast using the least squares trend line for these data?

a)

3,600

b)

3,500

c)

3,400

d)

3,300

e)

3,200

148.

The owner of Darkest Tans Unlimited in a local mall is forecasting this month's (October's) demand for one new tanning booth based on the following historical data: April 100; May 140; June 110; July 150; August 120; September 160. What is this month's forecast using the naive approach?

a)

100

b)

160

c)

130

d)

140

e)

120

149.

Using the tanning booth visit data: April 100; May 140; June 110; July 150; August 120; September 160. What is this month's forecast using a four-month weighted moving average with weights of 0.4, 0.3, 0.2, and 0.1 applied to the most recent months?

a)

120

b)

129

c)

141

d)

135

e)

140

150.

Using exponential smoothing with alpha = 0.2, and given that August's forecast was 145, what is this month's forecast if August's actual was 120 and September's actual was 160?

a)

144

b)

140

c)

142

d)

148

e)

163

151.

What is the monthly rate of change (slope) of the least squares trend line for these data?

a)

320

b)

102

c)

8

d)

-0.4

e)

-8

152.

What is this month's forecast using the least squares trend line for these data?

a)

1250

b)

128.6

c)

102

d)

158

e)

164

153.

Which of the following mechanisms for enhancing profitability is most likely to result from improving short term forecast performance?

a)

increased inventory

b)

reduced flexibility

c)

higher-quality products

d)

greater customer satisfaction

e)

greater seasonality

154.

Which of the following changes would tend to shorten the time frame for short term forecasting?

a)

bringing greater variety into the product mix

b)

increasing the flexibility of the production system

c)

ordering fewer weather-sensitive items

d)

adding more special-purpose equipment

e)

none of the above

155.

Which of the following helps improve supply chain forecasting performance?

a)

contracts that require supply chain members to formulate long term forecasts

b)

penalties for supply chain members that adjust forecasts

c)

sharing forecasts or demand data across the supply chain

d)

increasing lead times for critical supply chain members

e)

increasing the number of suppliers at critical junctures in the supply chain

156.

Inaccuracies in forecasts along the supply chain lead to:

a)

shortages or excesses of materials

b)

reduced customer service

c)

excess capacity

d)

missed deliveries

157.

Which of the following is the most valuable piece of information the sales force can bring into forecasting situations?

a)

what customers are most likely to do in the future

b)

what customers most want to do in the future

c)

what customers' future plans are

d)

whether customers are satisfied or dissatisfied with their performance in the past

e)

what the salesperson's appropriate sales quota should be

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