Operations And Supply Chain Management The Core 5Th Edition By F. Robert Jacobs – Test Bank
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Operations and Supply Chain Management: The Core, 5e (Jacobs)
Chapter 3 Forecasting
1) Continual review and updating in light of new data is a
forecasting technique called second-guessing.
2) Cyclical influences on demand are often expressed graphically
as a linear function that is either upward or downward sloping.
3) Cyclical influences on demand may come from occurrences such
as political elections, war, or economic conditions.
4) Trend lines are usually the last things considered when
developing a forecast.
5) Time series forecasting models make predictions about the
future based on analysis of past data.
6) In the weighted moving average forecasting model the weights
must add up to one times the number of data points.
7) In a forecasting model using simple exponential smoothing the
data pattern should remain stationary.
8) In a forecasting model using simple moving average, the
shorter the time span used for calculating the moving average, the closer the
average follows volatile trends.
9) In the simple exponential smoothing forecasting model you
need at least 30 observations to set the smoothing constant alpha.
10) Experience and trial and error are the simplest ways to
choose weights for the weighted moving average forecasting model.
11) Bayesian analysis is the simplest way to choose weights for
the weighted moving average forecasting model.
12) The weighted moving average forecasting model uses a
weighting scheme to modify the effects of individual data points. This is its
major advantage over the simple moving average model.
13) A central premise of exponential smoothing is that more
recent data is less indicative of the future than data from the distant past.
14) The equation for exponential smoothing states that the new
forecast is equal to the old forecast plus the error of the old forecast.
15) Exponential smoothing is always the best and most accurate
of all forecasting models.
16) In exponential smoothing, it is desirable to use a higher
smoothing constant when forecasting demand for a product experiencing high
growth.
17) The value of the smoothing constant alpha in an exponential
smoothing model is between 0 and 1.
18) Simple exponential smoothing lags changes in demand.
19) Exponential smoothing forecasts always lag behind the actual
occurrence but can be corrected somewhat with a trend adjustment.
20) Because the factors governing demand for products are very
complex, all forecasts of demand contain error.
21) Random errors can be defined as those that cannot be
explained by the forecast model being used.
22) There are no differences in strategic and tactical forecasting.
A forecast is a mathematical projection and its ultimate purpose should make no
difference to the analyst.
23) Random errors in forecasting occur when an undetected
secular trend is not included in a forecasting model.
24) The MAD is used to generate tracking signals.
25) MAD statistics can be used to generate tracking signals.
26) RSFE in forecasting stands for “reliable safety function
error.”
27) In forecasting, RSFE stands for “running sum of forecast
errors.”
28) A tracking signal (TS) can be calculated using the
arithmetic sum of forecast deviations divided by the MAD.
29) A restriction in using linear regression is that it assumes
that past data and future projections fall on or near a straight line.
30) Regression is a functional relationship between two or more
correlated variables, where one or more variables are used to predict a single
variable of interest.
31) Linear regression is not useful for aggregate planning.
32) The standard error of the estimate of a linear regression is
not useful for judging the fit between the data and the regression line when
doing forecasts.
33) Multiple regression analysis uses several regression models
to generate a forecast.
34) For every forecasting problem there is one best forecasting
technique.
35) A good forecaster is one who develops special skills and
experience at one forecasting technique and is capable of applying it to widely
diverse situations.
36) In causal relationship forecasting leading indicators are
used to forecast occurrences.
37) Qualitative forecasting techniques generally take advantage
of the knowledge of experts and therefore do not require much judgment.
38) Market research is a quantitative method of forecasting.
39) Decomposition of a time series means identifying and
separating the time series data into its components.
40) A time series is defined in the text as chronologically
ordered data that may contain one or more components of demand variation:
trend, seasonal, cyclical, autocorrelation, and random.
41) It is difficult to identify the trend in time series data.
42) In decomposition of time series data it is relatively easy
identify cycles and autocorrelation components.
43) We usually associate the word “seasonal” with recurrent
periods of repetitive activity that happen on other than an annual cycle.
44) In time series data depicting demand which of the following
is not considered a component of demand variation?
1. A)
Trend
2. B)
Seasonal
3. C)
Cyclical
4. D)
Variance
5. E) Autocorrelation
45) Which of the following is not one of the basic forecasting
types discussed in the text?
1. A)
Qualitative
2. B)
Time series analysis
3. C)
Causal relationships
4. D)
Simulation
5. E)
Force field analysis
46) In most cases, demand for products or services can be broken
down into several components. Which of the following is not considered a
component of demand?
1. A)
Average demand for a period
2. B) A
trend
3. C)
Seasonal elements
4. D)
Past data
5. E)
Autocorrelation
47) In most cases, demand for products or services can be broken
into several components. Which of the following is considered a component of
demand?
1. A)
Cyclical elements
2. B)
Future demand
3. C)
Past demand
4. D)
Inconsistent demand
5. E)
Level demand
48) In most cases, demand for products or services can be broken
into several components. Which of the following is considered a component of
demand?
1. A)
Forecast error
2. B)
Autocorrelation
3. C)
Previous demand
4. D)
Consistent demand
5. E)
Repeat demand
49) Which of the following forecasting methodologies is
considered a qualitative forecasting technique?
1. A)
Simple moving average
2. B)
Market research
3. C)
Linear regression
4. D)
Exponential smoothing
5. E)
Multiple regression
50) Which of the following forecasting methodologies is
considered a time series forecasting technique?
1. A)
Simple moving average
2. B)
Market research
3. C)
Leading indicators
4. D)
Historical analogy
5. E)
Simulation
51) Which of the following forecasting methodologies is
considered a time series forecasting technique?
1. A)
Delphi method
2. B)
Exponential averaging
3. C)
Simple movement smoothing
4. D)
Weighted moving average
5. E)
Simulation
52) Which of the following forecasting methodologies is
considered a causal forecasting technique?
1. A)
Exponential smoothing
2. B)
Weighted moving average
3. C)
Linear regression
4. D)
Historical analogy
5. E)
Market research
53) Which of the following forecasting methods uses executive
judgment as its primary component for forecasting?
1. A)
Historical analogy
2. B)
Time series analysis
3. C)
Panel consensus
4. D)
Market research
5. E)
Linear regression
54) Which of the following forecasting methods is very dependent
on selection of the right individuals who will judgmentally be used to actually
generate the forecast?
1. A)
Time series analysis
2. B)
Simple moving average
3. C)
Weighted moving average
4. D)
Delphi method
5. E)
Panel consensus
55) In business forecasting, what is usually considered a
short-term time period?
1. A)
Four weeks or less
2. B)
More than three months
3. C)
Six months or more
4. D)
Less than three months
5. E)
One year
56) In business forecasting, what is usually considered a
medium-term time period?
1. A)
Six weeks to one year
2. B)
Three months to two years
3. C)
One to five years
4. D)
One to six months
5. E)
Six months to six years
57) In business forecasting, what is usually considered a long-term
time period?
1. A)
Three months or longer
2. B)
Six months or longer
3. C)
One year or longer
4. D)
Two years or longer
5. E)
Ten years or longer
58) In general, which forecasting time frame compensates most
effectively for random variation and short term changes?
1. A)
Short-term forecasts
2. B)
Quick-time forecasts
3. C)
Long range forecasts
4. D)
Medium term forecasts
5. E)
Rapid change forecasts
59) In general, which forecasting time frame best identifies
seasonal effects?
1. A)
Short-term forecasts
2. B) Quick-time
forecasts
3. C)
Long range forecasts
4. D)
Medium term forecasts
5. E)
Rapid change forecasts
60) In general, which forecasting time frame is best to detect
general trends?
1. A)
Short-term forecasts
2. B)
Quick-time forecasts
3. C)
Long range forecasts
4. D)
Medium term forecasts
5. E)
Rapid change forecasts
61) Which of the following forecasting methods can be used for
short-term forecasting?
1. A)
Simple exponential smoothing
2. B)
Delphi technique
3. C)
Market research
4. D)
Hoskins-Hamilton smoothing
5. E)
Serial regression
62) Which of the following considerations is not a factor in
deciding which forecasting model a firm should choose?
1. A)
Time horizon to forecast
2. B)
Product
3. C)
Accuracy required
4. D)
Data availability
5. E)
Analyst availability
63) A company wants to forecast demand using the simple moving
average. If the company uses four prior yearly sales values (i.e., year 2014 =
100, year 2015 = 120, year 2016 = 140, and year 2017 = 210), which of the
following is the simple moving average forecast for year 2018?
100.
A) 100.5
101.
B) 140.0
102.
C) 142.5
103.
D) 145.5
104.
E) 155.0
64) A company wants to forecast demand using the simple moving
average. If the company uses three prior yearly sales values (i.e., year 2015 =
130, year 2016 = 110, and year 2017 =160), which of the following is the simple
moving average forecast for year 2018?
100.
A) 100.5
101.
B) 122.5
102.
C) 133.3
103.
D) 135.6
104.
E) 139.3
65) A company wants to forecast demand using the weighted moving
average. If the company uses two prior yearly sales values (i.e., year 2013 =
110 and year 2015 = 130), and we want to weight year 2016 at 10% and year 2017
at 90%, which of the following is the weighted moving average forecast for year
2018?
1. A)
120
2. B)
128
3. C)
133
4. D)
138
5. E)
142
66) A company wants to forecast demand using the weighted moving
average. If the company uses three prior yearly sales values (i.e., year 2015 =
160, year 2016 = 140 and year 2017 = 170), and we want to weight year 2014 at
30%, year 2015 at 30% and year 2016 at 40%, which of the following is the
weighted moving average forecast for year 2018?
1. A)
170
2. B)
168
3. C)
158
4. D)
152
5. E)
146
67) Which one of the following are among the major reasons that
exponential smoothing has become well accepted as a forecasting technique?
1. A)
Accurate and easy to use
2. B)
Sophistication of analysis
3. C)
Predicts turning points
4. D)
Captures patterns in historical data
5. E)
Ability to forecast lagging data trends
68) The exponential smoothing method requires which of the
following data to forecast the future?
1. A)
The most recent forecast
2. B)
Precise actual demand for the past several years
3. C)
The value of the smoothing constant delta
4. D)
Overall industry demand data
5. E)
Tracking values
69) Given a prior forecast demand value of 230, a related actual
demand value of 250, and a smoothing constant alpha of 0.1, what is the
exponential smoothing forecast value for the following period?
1. A)
230
2. B)
232
3. C)
238
4. D)
248
5. E)
250
70) If a firm produced a standard item with relatively stable
demand, the smoothing constant alpha (reaction rate to differences) used in an
exponential smoothing forecasting model would tend to be in which of the
following ranges?
1. A) 5%
to 10%
2. B)
20% to 50%
3. C)
20% to 80%
4. D)
60% to 120%
5. E)
90% to 100%
71) If a firm produced a product that was experiencing growth in
demand, the smoothing constant alpha (reaction rate to differences) used in an
exponential smoothing forecasting model would tend to be which of the
following?
1. A)
Close to zero.
2. B) A
very low percentage, less than 10%.
3. C)
The more rapid the growth, the higher the percentage.
4. D)
The more rapid the growth, the lower the percentage.
5. E)
50% or more.
72) Given a prior forecast demand value of 1,100, a related
actual demand value of 1,000, and a smoothing constant alpha of 0.3, what is
the exponential smoothing forecast value?
1. A)
1,000
2. B)
1,030
3. C)
1,070
4. D)
1,130
5. E)
970
73) A company wants to generate a forecast for unit demand for
year 2018 using exponential smoothing. The actual demand in year 2017 was 120.
The forecast demand in year 2017 was 110. Using this data and a smoothing
constant alpha of 0.1, which of the following is the resulting year 2018
forecast value?
1. A)
100
2. B)
110
3. C)
111
4. D)
114
5. E)
120
74) As a consultant you have been asked to generate a unit
demand forecast for a product for year 2018 using exponential smoothing. The
actual demand in year 2017 was 750. The forecast demand in year 2017 was 960.
Using this data and a smoothing constant alpha of 0.3, which of the following
is the resulting year 2018 forecast value?
1. A)
766
2. B)
813
3. C)
897
4. D)
1,023
5. E)
1,120
75) Which of the following is a possible source of bias error in
forecasting?
1. A)
Failing to include the right variables
2. B)
Using the wrong forecasting method
3. C)
Employing less sophisticated analysts than necessary
4. D)
Using incorrect data
5. E)
Using standard deviation rather than MAD
76) Which of the following are used to describe the degree of
error?
1. A)
Weighted moving average
2. B)
Regression
3. C)
Moving average
4. D)
Forecast as a percent of actual
5. E)
Mean absolute deviation
77) A company has actual unit demand for three consecutive years
of 124, 126, and 135. The respective forecasts for the same three years are
120, 120, and 130. Which of the following is the resulting MAD value that can
be computed from this data?
1. A) 1
2. B) 3
3. C) 5
4. D) 15
5. E)
123
78) A company has actual unit demand for four consecutive years
of 100, 105, 135, and 150. The respective forecasts were 120 for all four
years. Which of the following is the resulting MAD value that can be computed
from this data?
2. A)
2.5
3. B) 10
4. C) 20
5. D)
22.5
6. E) 30
79) If you were selecting from a variety of forecasting models
based on MAD, which of the following MAD values from the same data would
reflect the most accurate model?
1. A)
0.2
2. B)
0.8
3. C)
1.0
4. D)
10.0
5. E)
100.0
80) A company has calculated its running sum of forecast errors
to be 500 and its mean absolute deviation is exactly 35. Which of the following
is the company’s tracking signal?
1. A)
Cannot be calculated based on this information
2. B)
About 14.3
3. C)
More than 35
4. D)
Exactly 35
5. E)
About 0.07
81) A company has a MAD of 10. Its wants to have a 99.7 percent
control limits on its forecasting system. It’s most recent tracking signal
value is 3.1. What can the company conclude from this information?
1. A)
The forecasting model is operating acceptably
2. B)
The forecasting model is out of control and needs to be corrected
3. C)
The MAD value is incorrect
4. D)
The upper control value is less than 20
5. E) It
is using an inappropriate forecasting methodology
82) You are hired as a consultant to advise a small firm on
forecasting methodology. Based on your research you find the company has a MAD
of 3. Its wants to have a 99.7 percent control limits on its forecasting
system. Its most recent tracking signal value is 15. What should be your report
to the company?
1. A)
The forecasting model is operating acceptably
2. B)
The forecasting model is out of control and needs to be corrected
3. C)
The MAD value is incorrect
4. D)
The upper control value is less than 20
5. E)
The company is using an inappropriate forecasting methodology
83) Which of the following is the portion of observations you
would expect to see lying within a plus or minus 3 MAD range?
57.
A) 57.05 percent
58.
B) 88.95 percent
59.
C) 98.36 percent
60.
D) 99.85 percent
61.
E) 100 percent
84) Which of the following is the portion of observations you
would expect to see lying within a plus or minus 2 MAD range?
57.
A) 57.04
58.
B) 89.04
59.
C) 98.33
60.
D) 99.86
61.
E) 100.00
85) If the intercept value of a linear regression model is 40,
the slope value is 40, and the value of X is 40, which of the following is the
resulting forecast value using this model?
1. A)
120
2. B)
1,600
3. C)
1,640
4. D)
2,200
5. E)
64,000
86) A company hires you to develop a linear regression
forecasting model. Based on the company’s historical sales information, you
determine the intercept value of the model to be 1,200. You also find the slope
value is minus 50. If after developing the model you are given a value of X =
10, which of the following is the resulting forecast value using this model?
1. A) –
1,800
2. B)
700
3. C)
1,230
4. D)
1,150
5. E)
12,000
87) Heavy sales of umbrellas during a rain storm is an example
of which of the following?
1. A) A
trend
2. B) A
causal relationship
3. C) A
statistical correlation
4. D) A
coincidence
5. E) A
fad
88) You are using an exponential smoothing model for
forecasting. The running sum of the forecast error statistics (RSFE) are
calculated each time a forecast is generated. You find the last RSFE to be 34.
Originally the forecasting model used was selected because it’s relatively low
MAD of 0.4. To determine when it is time to reevaluate the usefulness of the
exponential smoothing model you compute tracking signals. Which of the
following is the resulting tracking signal?
1. A) 85
2. B) 60
3. C)
13.6
4. D)
12.9
5. E) 8
89) Using the exponential smoothing model for forecasting, the
smoothing constant alpha determines the level of smoothing and
1. A)
the slope of the growth curve.
2. B)
the speed of reaction to differences between forecasts and actual results.
3. C)
the intercept on the Y-axis.
4. D)
the next forecast error.
5. E) a
measure of forecast accuracy.
90) The least squares method refers to
1. A) a
computation in linear regression.
2. B)
selecting participants for the Delphi Technique.
3. C)
time series decomposition into smaller and smaller units.
4. D)
determining the smallest sources of error in a forecast.
5. E)
calculating the running sum of forecast errors.
91) Collaborative Planning, Forecasting, and Replenishment
(CPFR) is a web-based tool used to coordinate demand forecasting, production
and purchase planning, and inventory replenishment between supply chain trading
partners. In practice CPFR often doesn’t deliver on its’ promise because;
1) Computer systems at supplier companies cannot be made to work
with each other.
2) Forecast errors accumulate as data exchanges are made down
the supply chain culminating in the “feast or famine” phenomena known as the
“bullwhip effect.”
3) Firms in a supply chain may not trust each other sufficiently
to share information openly.
4) conflicting objectives between the profit-maximizing vendor
and the cost-minimizing customer give rise to adversarial supply chain
relationships.
1. A)
All of these
2. B) 2
and 4 above
3. C) 1
and 3 above
4. D) 1,
3, and 4 above
5. E) 3
and 4 above
92) A company wants to forecast demand using the simple moving
average. The company uses four positive prior yearly (2014, 2015, 2016 and
2017) sales values. All yearly sales figures are unique (no repetitions). Which
of the following is most accurate about the moving average forecast for year
2018?
1. Has
to be smaller than at least one of the four yearly sales figures.
1. Has
to be larger than at least one of the four yearly sales figures.
1. Has
to be between the smallest and largest yearly sales figures.
1. Has
to greater than all four yearly sales figures.
1. A)
Choice A
2. B)
Choice B
3. C)
Choice C
4. D)
Choice D
5. E)
Choice A, B and C only
93) As a consultant, you have been asked to generate a unit
demand forecast for a product for year 2018 using exponential smoothing. You
have data for the past three years and the forecast and the actual are the same
for the first period of your data (3 years ago). Which of the following is most
accurate?
1. Forecast
for year 2018 will be higher than the actual for 2017, if your α is close to
1.0
1. Forecast
for 2018 will be between all the actual sales
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