Operations Management, Global Edition 12Th Edition By William Stevenson – Test Bank
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Sample Test
Chapter 03
Forecasting
True / False Questions
1. Forecasting
techniques generally assume an existing causal system that will continue to exist
in the future.
True False
2. For
new products in a strong growth mode, a low alpha will minimize forecast errors
when using exponential smoothing techniques.
True 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
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
5. Forecasts
help managers both to plan the system itself and to provide valuable
information for using the system.
True 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
7. When
new products or services are introduced, focus forecasting models are an
attractive option.
True 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
9. Forecasts
based on time-series (historical) data are referred to as associative
forecasts.
True False
10.
Time-series techniques involve the identification of explanatory
variables that can be used to predict future demand.
True 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
12.
The Delphi approach involves the use of a series of
questionnaires to achieve a consensus forecast.
True False
13.
Exponential smoothing adds a percentage (called alpha) of the
last period’s forecast to estimate the next period’s demand.
True False
14.
The shorter the forecast period, the more accurately the
forecasts tend to track what actually happens.
True False
15.
Forecasting techniques that are based on time-series data assume
that future values of the series will duplicate past values.
True False
16.
Trend-adjusted exponential smoothing uses double smoothing to
add twice the forecast error to last period’s actual demand.
True False
17.
Forecasts based on an average tend to exhibit less variability
than the original data.
True False
18.
The naive approach to forecasting requires a linear trend line.
True False
19.
The naive forecast is limited in its application to series that
reflect no trend or seasonality.
True 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
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
22.
In order to update a moving average forecast, the values of each
data point in the average must be known.
True False
23.
Forecasts of future demand are used by operations people to plan
capacity.
True 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
25.
Exponential smoothing is a form of weighted averaging.
True 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
27.
The T in the model TAF = S + T represents the time dimension
(which is usually expressed in weeks or months).
True False
28.
Trend-adjusted exponential smoothing requires selection of two
smoothing constants.
True 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
30.
A seasonal relative (or seasonal indexes) is expressed as a
percentage of average or trend.
True 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 cannot be used.
True False
32.
Removing the seasonal component from a data series
(deseasonalizing) can be accomplished by dividing each data point by its
appropriate seasonal relative.
True 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
34.
Curvilinear and multiple regression procedures permit us to
extend associative models to relationships that are nonlinear or involve more
than one predictor variable.
True False
35.
The sample standard deviation of forecast error is equal to the
square root of MSE.
True False
36.
Correlation measures the strength and direction of a relationship
between variables.
True 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
38.
In exponential smoothing, an alpha of 1.0 will generate the same
forecast that a naive forecast would yield.
True False
39.
A forecast method is generally deemed to perform adequately when
the errors exhibit an identifiable pattern.
True False
40.
A control chart involves setting action limits for cumulative
forecast error.
True False
41.
A tracking signal focuses on the ratio of cumulative forecast
error to the corresponding value of MAD.
True False
42.
The use of a control chart assumes that errors are normally
distributed about a mean of zero.
True False
43.
Bias exists when forecasts tend to be greater or less than the
actual values of time series.
True False
44.
Bias is measured by the cumulative sum of forecast errors.
True False
45.
Seasonal relatives can be used to deseasonalize data or
incorporate seasonality in a forecast.
True False
46.
The best forecast is not necessarily the most accurate.
True False
Multiple Choice Questions
47.
Which of the following is a potential shortcoming of using sales
force opinions in demand forecasting?
1. Members
of the sales force often have substantial histories of working with and
understanding their customers.
1. Members
of the sales force often are well aware of customers’ future plans.
1. Members
of the sales force have direct contact with consumers.
1. Members
of the sales force can have difficulty distinguishing between what customers
would like to do and what they actually will do.
1. Customers
often are quite open with members of the sales force with regard to future
plans.
48.
Suppose a four-period weighted average is being used to forecast
demand. Weights for the periods are as follows: wt-4 = 0.1, wt-3 = 0.2, wt-2 =
0.3 and wt-1 = 0.4. Demand observed in the previous four periods was as
follows: At-4 = 380, At-3 = 410, At-2 = 390, At-1 = 400. What will be the
demand forecast for period t?
1. 402
1. 397
1. 399
1. 393
1. 403
49.
Suppose a three-period weighted average is being used to
forecast demand. Weights for the periods are as follows: wt-3 = 0.2, wt-2 = 0.3
and wt-1 = 0.5. Demand observed in the previous three periods was as follows:
At-3 = 2,200, At-2 = 1,950, At-1 = 2,050. What will be the demand forecast for
period t?
1. 2,000
1. 2,095
1. 1,980
1. 2,050
1. 1,875
50.
When choosing a forecasting technique, a critical trade-off that
must be considered is that between:
1. time
series and associative.
1. seasonality
and cyclicality.
1. length
and duration.
1. simplicity
and complexity.
1. cost
and accuracy.
51.
The more novel a new product or service design is, the more
forecasters have to rely on:
1. subjective
estimates.
1. seasonality.
1. cyclicality.
1. historical
data.
1. smoothed
variation.
52.
Forecasts based on judgment and opinion do not include:
1. executive
opinion.
1. salesperson
opinion.
1. second
opinions.
1. customer
surveys.
1. Delphi
methods.
53.
Which of the following is/are a primary input into capacity,
sales, and production planning?
1. product
design
1. market
share
1. ethics
1. globalization
1. demand
forecasts
54.
Which of the following features would not generally be
considered common to all forecasts?
1. Assumption
of a stable underlying causal system.
1. Actual
results will differ somewhat from predicted values.
1. Historical
data is available on which to base the forecast.
1. Forecasts
for groups of items tend to be more accurate than forecasts for individual
items.
1. Accuracy
decreases as the time horizon increases.
55.
Which of the following is not a step in the forecasting process?
1. Determine
the purpose and level of detail required.
1. Eliminate
all assumptions.
1. Establish
a time horizon.
1. Select
a forecasting model.
1. Monitor
the forecast.
56.
Minimizing the sum of the squared deviations around the line is
called:
1. mean
squared error technique.
1. mean
absolute deviation.
1. double
smoothing.
1. least
squares estimation.
1. predictor
regression.
57.
The two general approaches to forecasting are:
1. mathematical
and statistical.
1. qualitative
and quantitative.
1. judgmental
and qualitative.
1. historical
and associative.
1. precise
and approximation.
58.
Which of the following is not a type of judgmental forecasting?
1. executive
opinions
1. sales
force opinions
1. consumer
surveys
1. the
Delphi method
1. time
series analysis
59.
Accuracy in forecasting can be measured by:
1. MSE.
1. MRP.
1. MPS.
1. MTM.
1. MTE.
60.
Which of the following would be an advantage of using a sales
force composite to develop a demand forecast?
1. The
sales staff is least affected by changing customer needs.
1. The
sales force can easily distinguish between customer desires and probable
actions.
1. The
sales staff is often aware of customers’ future plans.
1. Salespeople
are least likely to be influenced by recent events.
1. Salespeople
are least likely to be biased by sales quotas.
61.
Which phrase most closely describes the Delphi technique?
1. associative
forecast
1. consumer
survey
1. series
of questionnaires
1. developed
in India
1. historical
data
62.
The forecasting method which uses anonymous questionnaires to
achieve a consensus forecast is:
1. sales
force opinions.
1. consumer
surveys.
1. the
Delphi method.
1. time
series analysis.
1. executive
opinions.
63.
One reason for using the Delphi method in forecasting is to:
1. avoid
premature consensus (bandwagon effect).
1. achieve
a high degree of accuracy.
1. maintain
accountability and responsibility.
1. be
able to replicate results.
1. prevent
hurt feelings.
64.
Detecting nonrandomness in errors can be done using:
1. MSEs.
1. MAPs.
1. control
charts.
1. correlation
coefficients.
1. strategies.
65.
Gradual, long-term movement in time series data is called:
1. seasonal
variation.
1. cycles.
1. irregular
variation.
1. trend.
1. random
variation.
66.
The primary difference between seasonality and cycles is:
1. the
duration of the repeating patterns.
1. the
magnitude of the variation.
1. the
ability to attribute the pattern to a cause.
1. the
direction of the movement.
1. there
are only four seasons but 30 cycles.
67.
Averaging techniques are useful for:
1. distinguishing
between random and nonrandom variations.
1. smoothing
out fluctuations in time series.
1. eliminating
historical data.
1. providing
accuracy in forecasts.
1. average
people.
68.
Putting forecast errors into perspective is best done using
1. exponential
smoothing.
1. MAPE.
1. linear
decision rules.
1. MAD.
1. hindsight.
69.
Using the latest observation in a sequence of data to forecast
the next period is:
1. a
moving average forecast.
1. a
naive forecast.
1. an
exponentially smoothed forecast.
1. an
associative forecast.
1. regression
analysis.
70.
For the data given below, what would the naive forecast be for
period 5?
1. 58
1. 62
59.
59.5
1. 61
1. cannot
tell from the data given
71.
Moving average forecasting techniques do the following:
1. Immediately
reflect changing patterns in the data.
1. Lead
changes in the data.
1. Smooth
variations in the data.
1. Operate
independently of recent data.
1. Assist
when organizations are relocating.
72.
Which is not a characteristic of simple moving averages applied
to time series data?
1. smoothes
random variations in the data
1. weights
each historical value equally
1. lags
changes in the data
1. requires
only last period’s forecast and actual data
1. 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:
1. decreased.
1. increased.
1. multiplied
by a larger alpha.
1. multiplied
by a smaller alpha.
1. 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:
1. a
naive forecast.
1. a
simple moving average forecast.
1. a
centered moving average forecast.
1. an
exponentially smoothed forecast.
1. an
associative forecast.
75.
Which is not a characteristic of exponential smoothing?
1. smoothes
random variations in the data
1. weights
each historical value equally
1. has
an easily altered weighting scheme
1. has
minimal data storage requirements
1. smoothes
real variations in the data
76.
Which of the following smoothing constants would make an
exponential smoothing forecast equivalent to a naive forecast?
1. 0
1. .01
1. .1
1. .5
1. 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:
1. .01.
10.
.10.
15.
.15.
20.
.20.
60.
.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?
36.
36.9
57.
57.5
60.
60.5
62.
62.5
65.
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:
80.
80.8.
93.
93.8.
100.
100.2.
101.
101.8.
108.
108.2.
80.
Which of the following possible values of alpha would cause
exponential smoothing to respond the most quickly to forecast errors?
1. 0
1. .01
1. .05
1. .10
1. .15
81.
A manager uses the following equation to predict monthly
receipts: Yt = 40,000 + 150t. What is the forecast for July if t = 0 in April
of this year?
1. 40,450
1. 40,600
1. 42,100
1. 42,250
1. 42,400
82.
In trend-adjusted exponential smoothing, the trend-adjusted
forecast consists of:
1. an
exponentially smoothed forecast and a smoothed trend factor.
1. an
exponentially smoothed forecast and an estimated trend value.
1. the
old forecast adjusted by a trend factor.
1. the
old forecast and a smoothed trend factor.
1. 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.
1. quantity;
percentage
1. percentage;
quantity
1. quantity;
quantity
1. percentage;
percentage
1. qualitative;
quantitative
84.
Which technique is used in computing seasonal relatives?
1. double
smoothing
1. Delphi
1. mean
squared error
1. centered
moving average
1. exponential
smoothing
85.
A persistent tendency for forecasts to be greater than or less
than the actual values is called:
1. bias.
1. tracking.
1. control
charting.
1. positive
correlation.
1. linear
regression.
86.
Which of the following might be used to indicate the cyclical
component of a forecast?
1. leading
variable
1. mean
squared error
1. Delphi
technique
1. exponential
smoothing
1. mean
absolute deviation
87.
The primary method for associative forecasting is:
1. sensitivity
analysis.
1. regression
analysis.
1. simple
moving averages.
1. centered
moving averages.
1. exponential
smoothing.
88.
Which term most closely relates to associative forecasting
techniques?
1. time
series data
1. expert
opinions
1. Delphi
technique
1. consumer
survey
1. predictor
variables
89.
Which of the following corresponds to the predictor variable in
simple linear regression?
1. regression
coefficient
1. dependent
variable
1. independent
variable
1. predicted
variable
1. demand
coefficient
90.
The mean absolute deviation is used to:
1. estimate
the trend line.
1. eliminate
forecast errors.
1. measure
forecast accuracy.
1. seasonally
adjust the forecast.
1. compute
periodic forecast errors.
91.
Given forecast errors of 4, 8, and -3, what is the mean absolute
deviation?
1. 4
1. 3
1. 5
1. 6
1. 12
92.
Given forecast errors of 5, 0, -4, and 3, what is the mean
absolute deviation?
1. 4
1. 3
2. 2.5
1. 2
1. 1
93.
Given forecast errors of 5, 0, -4, and 3, what is the bias?
1. -4
1. 4
1. 5
1. 12
1. 6
94.
Which of the following is used for constructing a control chart?
1. mean
absolute deviation
1. mean
squared error
1. tracking
signal
1. bias
95.
The two most important factors in choosing a forecasting
technique are:
1. cost
and time horizon.
1. accuracy
and time horizon.
1. cost
and accuracy.
1. quantity
and quality.
1. objective
and subjective components.
96.
The degree of management involvement in short-range forecasts
is:
1. none.
1. low.
1. moderate.
1. high.
1. total.
97.
Which of the following is not necessarily an element of a good forecast?
1. estimate
of accuracy
1. timeliness
1. meaningful
units
1. low
cost
1. written
98.
Forecasting techniques generally assume:
1. the
absence of randomness.
1. continuity
of some underlying causal system.
1. a
linear relationship between time and demand.
1. accuracy
that increases the farther out in time the forecast projects.
1. accuracy
that is better when individual items, rather than groups of items, are being
considered.
99.
A managerial approach toward forecasting which seeks to actively
influence demand is:
1. reactive.
1. proactive.
1. influential.
1. protracted.
1. retroactive.
100.
Customer service levels can be improved by better:
1. mission
statements.
1. control
charting.
1. short-term
forecast accuracy.
1. exponential
smoothing.
1. customer
selection.
101.
Given the following historical data, what is the simple
three-period moving average forecast for period 6?
1. 67
1. 115
1. 69
1. 68
68.
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?
144.
144.20
144.
144.80
144.
144.67
143.
143.00
144.
144.00
103.
Use of simple linear regression analysis assumes that:
1. variations
around the line are nonrandom.
1. deviations
around the line are normally distributed.
1. predictions
can easily be made beyond the range of observed values of the predictor
variable.
1. all
possible predictor variables are included in the model.
1. the
variance of error terms (deviations) varies directly with the predictor
variable.
104.
Given forecast errors of -5, -10, and +15, the MAD is:
1. 0.
10.
10.
30.
30.
175.
175.
225.
225.
105.
The president of State University wants to forecast student
enrollments for this academic year based on the following historical data:
What is the forecast for this year using the naive approach?
1. 18,750
1. 19,500
1. 21,000
1. 22,000
1. 22,800
106.
The president of State University wants to forecast student
enrollments for this academic year based on the following historical data:
What is the forecast for this year using a four-year simple
moving average?
1. 18,750
1. 19,500
1. 21,000
1. 22,650
1. 22,800
107.
The president of State University wants to forecast student
enrollments for this academic year based on the following historical data:
What is the forecast for this year using exponential smoothing
with alpha = .5, if the forecast for two years ago was 16,000?
1. 18,750
1. 19,500
1. 21,000
1. 22,650
1. 22,800
108.
The president of State University wants to forecast student
enrollments for this academic year based on the following historical data:
What is the forecast for this year using the least squares trend
line for these data?
1. 18,750
1. 19,500
1. 21,000
1. 22,650
1. 22,800
109.
The president of State University wants to forecast student
enrollments for this academic year based on the following historical data:
What is the forecast for this year using trend-adjusted (double)
smoothing with alpha = .05 and beta = .3, if the forecast for last year was
21,000, the forecast for two years ago was 19,000, and the trend estimate for
last year’s forecast was 1,500?
1. 18,750
1. 19,500
1. 21,000
1. 22,650
1. 22,800
110.
The business analyst for Video Sales, Inc. wants to forecast
this year’s demand for DVD decoders based on the following historical data:
What is the forecast for this year using the naive approach?
1. 163
1. 180
1. 300
1. 420
1. 510
111.
The business analyst for Video Sales, Inc. wants to forecast
this year’s demand for DVD decoders based on the following historical data:
What is the forecast for this year using a three-year weighted
moving average with weights of .5, .3, and .2?
1. 163
1. 180
1. 300
1. 420
1. 510
112.
The business analyst for Video Sales, Inc. wants to forecast
this year’s demand for DVD decoders based on the following historical data:
What is the forecast for this year using exponential smoothing
with alpha = .4, if the forecast for two years ago was 750?
1. 163
1. 180
1. 300
1. 420
1. 510
113.
The business analyst for Video Sales, Inc. wants to forecast
this year’s demand for DVD decoders based on the following historical data:
What is the forecast for this year using the least squares trend
line for these data?
1. 163
1. 180
1. 300
1. 420
1. 510
114.
The business analyst for Video Sales, Inc. wants to forecast
this year’s demand for DVD decoders based on the following historical data:
What is the forecast for this year using trend-adjusted (double)
smoothing with alpha = .3 and beta = .2, if the forecast for last year was 310,
the forecast for two years ago was 430, and the trend estimate for last year’s
forecast was -150?
162.
162.4
180.
180.3
301.
301.4
403.
403.2
510.
510.0
115.
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:
What is this week’s forecast using the naive approach?
1. 45
1. 50
1. 52
1. 65
1. 78
116.
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:
What is this week’s forecast using a three-week simple moving
average?
1. 49
1. 50
1. 52
1. 65
1. 78
117.
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:
What is this week’s forecast using exponential smoothing with
alpha = .2, if the forecast for two weeks ago was 90?
1. 49
1. 50
1. 52
1. 65
1. 77
118.
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:
What is this week’s forecast using the least squares trend line
for these data?
1. 49
1. 50
1. 52
1. 65
1. 78
119.
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:
What is this week’s forecast using trend-adjusted (double)
smoothing with alpha = .5 and beta = .1, if the forecast for last week was 65,
the forecast for two weeks ago was 75, and the trend estimate for last week’s
forecast was -5?
49.
49.3
50.
50.6
51.
51.3
65.
65.4
78.
78.7
120.
A concert promoter is forecasting this year’s attendance for one
of his concerts based on the following historical data:
What is this year’s forecast using the naive approach?
1. 22,000
1. 20,000
1. 18,000
1. 15,000
1. 12,000
121.
A concert promoter is forecasting this year’s attendance for one
of his concerts based on the following historical data:
What is this year’s forecast using a two-year weighted moving
average with weights of .7 and .3?
1. 19,400
1. 18,600
1. 19,000
1. 11,400
1. 10,600
122.
A concert promoter is forecasting this year’s attendance for one
of his concerts based on the following historical data:
What is this year’s forecast using exponential smoothing with
alpha = .2, if last year’s smoothed forecast was 15,000?
1. 20,000
1. 19,000
1. 17,500
1. 16,000
1. 15,000
123.
A concert promoter is forecasting this year’s attendance for one
of his concerts based on the following historical data:
What is this year’s forecast using the least squares trend line
for these data?
1. 20,000
1. 21,000
1. 22,000
1. 23,000
1. 24,000
124.
A concert promoter is forecasting this year’s attendance for one
of his concerts based on the following historical data:
The previous trend line had predicted 18,500 for two years ago,
and 19,700 for last year. What was the mean absolute deviation for these
forecasts?
1. 100
1. 200
1. 400
1. 500
1. 800
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