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SCMT Exam 1 2024 Quizzes & Ans!!

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SCMT Exam 1 2024 Quizzes & Ans!!

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  • September 4, 2024
  • 18
  • 2024/2025
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52 Multiple choice questions

Term 1 of 52
To be of value for the purpose of forecasting, any independent variable must be a

leading indicator

model

current business inventory indicator

multiple regression

Term 2 of 52
When considering more than one variable, along with the effects of each item on the item of
interest, which forecasting model should be used?

exponential smoothing

multiple correlation

linear regression

multiple regression

Definition 3 of 52
exponential smoothing

simple moving average amount of data

the most used of forecasting techniques


the two types of seasonal variation discussed are

trend and seasonal models amount of data

,Term 4 of 52
Identifying and separating the time series data into its components.

qualitative

decomposition


multiple regression


exponential smoothing

Term 5 of 52
The values selected for alpha and delta depend on how much __________variation there is in demand
and how ___________the trend factor is

uniform and variable

random and steady

constant and unpredictable


consistent and fluctuating

Definition 6 of 52
all individual data elements must be carried as data.

The main disadvantage of the moving average is that

The two types of seasonal variation discussed are


the most used of forecasting techniques

Identify the basic classifications of forecasting.

, Term 7 of 52
What is the first step in CPFR?

Decomposition

Development of demand forecasts

Joint business planning


Creation of a front-end partnership agreement

Term 8 of 52
The real value of the MAPE is that it allows you to compare forecasts between products that have
very different

average demand


tracking signal

peak demand

baseload demand

Term 9 of 52
When demand for a product is neither growing nor declining rapidly, and if it does not have
seasonal characteristics, a ______________ ________________ can be useful in removing the random
fluctuations for forecasting.

seasonal adjustment

moving average


exponential smoothing

trend analysis

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