Operations And Supply Chain Management The Core 4th Edition by F. Robert Jacobs – Test Bank
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Sample Test
1. Continual
review and updating in light of new data is a forecasting technique called
second-guessing.
FALSE
Second guessing is not a forecasting technique.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-01
Topic: Quantitative Forecasting Models
2. Cyclical
influences on demand are often expressed graphically as a linear function that
is either upward or downward sloping.
FALSE
By their nature, cyclical influences are non-linear.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Components of Demand
3. Cyclical
influences on demand may come from occurrences such as political elections,
war, or economic conditions.
TRUE
Cyclical influence on demand may come from such occurrences as
political elections, war, economic conditions, or sociological pressures.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Components of Demand
4. Trend
lines are usually the last things considered when developing a forecast.
FALSE
Trend lines are the usual starting point in developing a
forecast.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Components of Demand
5. Time
series forecasting models make predictions about the future based on analysis
of past data.
TRUE
Time series forecasting models try to predict the future based
on past data.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
6. In
the weighted moving average forecasting model the weights must add up to one
times the number of data points.
FALSE
A weighted moving average (model) allows any weights to be
placed on each element, providing, of course, that the sum of all weights
equals 1 (one).
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
7. In a
forecasting model using simple exponential smoothing the data pattern should
remain stationary.
TRUE
See exhibit 3.3
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
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.
TRUE
While a shorter time span produces more oscillation, there is a
closer following of the trend. Conversely, a longer time span gives a smoother
response but lags the trend.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
9. In
the simple exponential smoothing forecasting model you need at least 30
observations to set the smoothing constant alpha.
FALSE
In the exponential smoothing method, only three pieces of data
are needed to forecast the future: the most recent forecast, the actual demand
that occurred for that forecast period, and a smoothing constant alpha. This smoothing
constant determines the level of smoothing and the speed of reaction to
differences between forecasts and actual occurrences. The value for the
constant is determined both by the nature of the product and by the manager’s
sense of what constitutes a good response rate.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
10.
Experience and trial and error are the simplest ways to choose
weights for the weighted moving average forecasting model.
TRUE
Experience and trial and error are the simplest ways to choose
weights for the weighted moving average forecasting model.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
11.
Bayesian analysis is the simplest way to choose weights for the
weighted moving average forecasting model.
FALSE
Experience and trial and error are the simplest ways to choose
weights for the weighted moving average forecasting model.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
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.
TRUE
The weighted moving average has a definite advantage over the
simple moving average in being able to vary the effects of past data.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
13.
A central premise of exponential smoothing is that more recent
data is less indicative of the future than data from the distant past.
FALSE
If the premise that the importance of data diminishes as the
past becomes more distant is valid then exponential smoothing may be the most
logical and easiest method to use.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
14.
The equation for exponential smoothing states that the new
forecast is equal to the old forecast plus the error of the old forecast.
FALSE
The equation for exponential smoothing states that the new
forecast is equal to the old forecast plus aportion of the
error (the difference between the previous forecast and what actually
occurred).
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
15.
Exponential smoothing is always the best and most accurate of
all forecasting models.
FALSE
No model is best and most accurate in every situation. That is
why there are so many models. A perfect forecast is virtually impossible. Too
many factors in the business environment cannot be predicted with certainty.
Therefore, rather than search for the perfect forecast, it is far more
important to establish the practice of continual review of forecasts and to
learn to live with inaccurate forecasts.
AACSB: Analytic
Accessibility: Keyboard Navigation
Bloom’s: Create
Difficulty: 1 Easy
Learning Objective: 03-01
Topic: From Bean to Cup: Starbucks Global Supply Chain Challenge
16.
In exponential smoothing, it is desirable to use a higher
smoothing constant when forecasting demand for a product experiencing high
growth.
TRUE
The more rapid the growth, the higher the reaction rate (e.g.,
smoothing constant) should be.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
17.
17. The value of the smoothing constant alpha in an exponential
smoothing model is between 0 and 1.
TRUE
Exponential smoothing requires that the smoothing constant alpha
be given a value between 0 and 1.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
18.
Simple exponential smoothing lags changes in demand.
TRUE
Single exponential smoothing has the shortcoming of lagging
changes in demand.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
19.
Exponential smoothing forecasts always lag behind the actual
occurrence but can be corrected somewhat with a trend adjustment.
TRUE
The forecast lags during an increase or decrease but overshoots
when a change in direction occurs. To more closely track actual demand, a trend
factor may be added.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis.
20.
Because the factors governing demand for products are very
complex, all forecasts of demand contain error.
TRUE
Demand for a product is generated through the interaction of a
number of factors too complex to describe accurately in a model. Therefore, all
forecasts certainly contain some error.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
21.
Random errors can be defined as those that cannot be explained
by the forecast model being used.
TRUE
Random errors can be defined as those that cannot be explained
by the forecast model being used.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
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.
FALSE
In considering what forecasting approach to use it is important
to consider the purpose of the forecast. Some forecasts are for very high-level
demand analysis. What do we expect the demand to be for a group of products
over the next year, for example? Some forecasts are used to help set the
strategy of how, in an aggregate sense, we will meet demand. We will call these
strategic forecasts. Forecasts are also needed for how a firm operates
processes on a day-to-day basis. For example, when should the inventory for an
item be replenished, or how much production should we schedule for an item next
week? These are tactical forecasts where the goal is to estimate demand in the
relative short term, a few weeks or months.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 2 Medium
Learning Objective: 03-01
Topic: From Bean to Cup: Starbucks Global Supply Chain Challenge
23.
Random errors in forecasting occur when an undetected secular
trend is not included in a forecasting model.
FALSE
Random errors can be defined as those that cannot be explained
by the forecast model being used.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
24.
When forecast errors occur in a normally distributed pattern,
the ratio of the mean absolute deviation to the standard deviation is 2 to 1,
or 2 x MAD = 1 standard deviation.
FALSE
When the errors that occur in the forecast are normally
distributed (the usual case,) the mean absolute deviation (MAD) relates to the
standard deviation as: one standard deviation = 1.25 MAD.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
25.
MAD statistics can be used to generate tracking signals.
TRUE
In recent years, MAD has made a comeback because of its
simplicity and usefulness in obtaining tracking signals.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
26.
RSFE in forecasting stands for “reliable safety function error.”
FALSE
RSFE (stands for) running sum of forecast errors.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
27.
In forecasting, RSFE stands for “running sum of forecast
errors.”
TRUE
RSFE (stands for ) running sum of forecast errors.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
28.
A tracking signal (TS) can be calculated using the arithmetic
sum of forecast deviations divided by the MAD.
TRUE
A tracking signal can be calculated using the arithmetic sum of
forecast deviations divided by the mean absolute deviation
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
29.
A restriction in using linear regression is that it assumes that
past data and future projections fall on or near a straight line.
TRUE
The major restriction in using linear regression forecasting is,
as the name implies, that past data and future projections are assumed to fall
on or near a straight line.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
30.
Regression is a functional relationship between two or more
correlated variables, where one variable is used to predict another.
TRUE
Regression can be defined as a functional relationship between
two or more correlated variables. It is used to predict one variable given the
other.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
31.
Linear regression is not useful for aggregate planning.
FALSE
Linear regression is useful for long-term forecasting of major
occurrences and aggregate planning.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
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.
FALSE
The standard error of estimate indicates how well the line fits
the data.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
33.
Multiple regression analysis uses several regression models to
generate a forecast.
FALSE
Multiple regression analysis, (is where) a number of variables
are considered, together with the effects of each on the item of interest.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Causal Relationship Forecasting
34.
34. For every forecasting problem there is one best forecasting
technique.
FALSE
When forecasting, a good strategy is to use two or three methods
and look at them for the commonsense view.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-01
Topic: From Bean to Cup: Starbucks Global Supply Chain Challenge
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.
FALSE
When forecasting, a good strategy is to use two or three methods
and look at them for the commonsense view.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-01
Topic: Quantitative Forecasting Models
36.
In causal relationship forecasting leading indicators are used
to forecast occurrences.
FALSE
Often leading indicators are not causal relationships, but in
some indirect way, they may suggest that some other things might happen.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-01
Topic: Causal Relationship Forecasting
37.
Qualitative forecasting techniques generally take advantage of
the knowledge of experts and therefore do not require much judgment.
FALSE
Qualitative forecasting techniques generally take advantage of
the knowledge of experts and require much judgment.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-03
Topic: Qualitative Techniques in Forecasting
38.
Market research is a quantitative method of forecasting.
FALSE
Market research is used mostly for product research in the sense
of looking for new product ideas, likes and dislikes about existing products,
which competitive products within a particular class are preferred, and so on.
Again, the data collection methods are primarily surveys and interviews. It is
a discussed under the qualitative techniques in forecasting
topic area.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-03
Topic: Qualitative Techniques in Forecasting
39.
Decomposition of a time series means identifying and separating
the time series data into its components.
TRUE
Decomposition of a time series means identifying and separating
the time series data into its components.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
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.
TRUE
A time series can be defined as chronologically ordered data
that may contain one or more components of demand: trend, seasonal, cyclical,
autocorrelation, and random
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
41.
It is difficult to identify the trend in time series data.
FALSE
In practice, it is relatively easy to identify the trend and the
seasonal component (by comparing the same period year to year).
42.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
In decomposition of time series data it is relatively easy
identify cycles and autocorrelation components.
FALSE
It is considerably more difficult (than trend detection) to
identify the cycles, autocorrelation, and random components.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
43.
We usually associate the word “seasonal” with recurrent periods
of repetitive activity that happen on other than an annual cycle.
FALSE
We usually associate seasonal with a period of the year
characterized by some particular activity. We use the word cyclical to
indicate other than annual recurrent periods of repetitive activity.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
44.
In time series data depicting demand which of the following is
not considered a component of demand variation?
1. Trend
1. Seasonal
1. Cyclical
D.Variance
1. Autocorrelation
Variance is a measure of the degree of error, not a component of
demand variation. E.g., several common terms used to describe the degree of error
are standard error, mean squared error (or variance), and mean absolute
deviation.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
45.
Which of the following is not one of the basic forecasting types
discussed in the text?
1. Qualitative
1. Time
series analysis
1. Causal
relationships
1. Simulation
E.Force field analysis
Forecasting can be classified into four basic types: qualitative,
time series analysis, causal relationships, and simulation.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-01
Topic: Quantitative Forecasting Models
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. Average
demand for a period
1. A
trend
1. Seasonal
elements
D.Past data
1. Autocorrelation
In most cases, demand for products or services can be broken
down into six components: average demand for the period, a trend, seasonal
elements, cyclical elements, random variation, and autocorrelation.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 2 Medium
Learning Objective: 03-02
Topic: Components of Demand
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?
A.Cyclical elements
1. Future
demand
1. Past
demand
1. Inconsistent
demand
1. Level
demand
In most cases, demand for products or services can be broken
down into six components: average demand for the period, a trend, seasonal
elements, cyclical elements, random variation, and autocorrelation.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Components of 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. Forecast
error
B.Autocorrelation
1. Previous
demand
1. Consistent
demand
1. Repeat
demand
In most cases, demand for products or services can be broken
down into six components: average demand for the period, a trend, seasonal
elements, cyclical elements, random variation, and autocorrelation.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Components of Demand
49.
Which of the following forecasting methodologies is considered a
qualitative forecasting technique?
1. Simple
moving average
B.Market research
1. Linear
regression
1. Exponential
smoothing
1. Multiple
regression
Market research is used mostly for product research in the sense
of looking for new product ideas, likes and dislikes about existing products,
which competitive products within a particular class are preferred, and so on.
Again, the data collection methods are primarily surveys and interviews. It is
a discussed under the qualitative techniques in forecasting
topic area.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-03
Topic: Qualitative Techniques in Forecasting
50.
50. Which of the following forecasting methodologies is
considered a time series forecasting technique?
A.Simple moving average
1. Market
research
1. Leading
indicators
1. Historical
analogy
1. Simulation
Simple moving average is the only choice that attempts to
predict future values of demand based upon past data.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
51.
Which of the following forecasting methodologies is considered a
time series forecasting technique?
1. Delphi
method
1. Exponential
averaging
1. Simple
movement smoothing
D.Weighted moving average
1. Simulation
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Time Series Analysis
52.
Which of the following forecasting methodologies is considered a
causal forecasting technique?
1. Exponential
smoothing
1. Weighted
moving average
1. Linear
regression
1. Historical
analogy
1. Market
research
Causal forecasting, which we discuss using the linear regression
technique, assumes that demand is related to some underlying factor or factors
in the environment.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Understand
Difficulty: 1 Easy
Learning Objective: 03-02
Topic: Causal Relationship Forecasting
53.
Which of the following forecasting methods uses executive
judgment as its primary component for forecasting?
1. Historical
analogy
1. Time
series analysis
C.Panel consensus
1. Market
research
1. Linear
regression
In a panel consensus, the idea that two heads are better than
one is extrapolated to the idea that a panel of people from a variety of
positions can develop a more reliable forecast than a narrower group. Panel
forecasts are developed through open meetings with free exchange of ideas from
all levels of management and individuals. When decisions in forecasting are at
a broader, higher level (as when introducing a new product line or concerning
strategic product decisions such as new marketing areas), the term executive
judgment is generally used.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 1 Easy
Learning Objective: 03-03
Topic: Qualitative Techniques in Forecasting
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. Time
series analysis
1. Simple
moving average
1. Weighted
moving average
D.Delphi method
1. Panel
consensus
The step-by-step procedure for the Delphi method is: 1. Choose
the experts to participate. There should be a variety of knowledgeable people
in different areas.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Understand
Difficulty: 2 Medium
Learning Objective: 03-03
Topic: Qualitative Techniques in Forecasting
55.
In business forecasting, what is usually considered a short-term
time period?
1. Four
weeks or less
1. More
than three months
1. Six
months or more
D.Less than three months
1. One
year
In business forecasting short term usually refers to under three
months.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Bloom’s: Remember
Difficulty: 2 Medium
Learning Objective: 03-02
Topic: Time Series Analysis
56.
(p. 52) In
business forecasting, what is usually considered a medium-term time period?
1. Six
weeks to one year
B.Three months to two years
1. One
to five years
1. One
to six months
1. Six
months to six years
In business forecasting medium term (refers to) three months to
two years.
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
Difficulty: 2 Medium
Learning Objective: 03-02
Topic: Time Series Analysis
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