Question 1

______ is a forecasting method in which it is assumed that the demand in the next period will be the same as it is in the current period.
A)Moving average
B)Naïve approach
C)Simple average
D)Sales force opinion

Accepted Answer

The method described in the question is known as the "naïve approach" or "naïve forecasting." This method assumes that the demand in the next period will be the same as the demand in the most recent period. Therefore, the answer is option B - Naïve approach.

Question 2

Simple exponential smoothing or the first-order smoothing model can be used when ______.
A)demand exhibits different types of trend patterns
B)demand is constant or level
C)demand exhibits both trend and seasonal patterns
D)demand is fluctuating

Accepted Answer

Simple exponential smoothing or the first-order smoothing model is suitable for data with a constant or level demand. It is not appropriate for data with trend, seasonal, or fluctuating demand patterns.

Question 3

Which of the following is a component of trend-adjusted forecast (TAF)?
A)leading indicators
B)seasonal indices
C)trend factors
D)tracking signals

Accepted Answer

Trend factors are a key component of trend-adjusted forecasting (TAF), which adjusts for the trend component of historical data to forecast future values. Leading indicators, seasonal indices, and tracking signals are not components of TAF.

Question 4

A ______ trend often occurs when new products are introduced.
A)linear
B)nonlinear
C)no trend (level demand)
D)damped

Accepted Answer

The answer of A ______ trend often occurs when new...

Question 5

______ is a short-term time series forecasting method in which forecasters assign more weight to most recent values in the time series if they feel that these values reflect how the actual demand will behave in the near future.
A)Moving average
B)Linear trend multiplicative method
C)Weighted moving average
D)Linear regression technique

Accepted Answer

Weighted moving average method assigns more weight to recent values to make the forecast more responsive to changes in the time series. This method is suitable for short-term forecasting where recent data holds more relevance.

Question 6

Increased retail sales in December and peak demand for snow shovels during winter are examples of ______
A)cyclical variations
B)seasonal variations
C)irregular variations
D)random variations

Accepted Answer

Increased retail sales in December and peak demand for snow shovels during winter are examples of seasonal variations, which occur due to predictable and recurring patterns associated with specific times of the year.

Question 7

New forecast = [(α *latest observation)+((1 - α)* old forecast)] is the formula for ______.
A)moving average
B)weighted moving average
C)exponential smoothing
D)factor rating method

Accepted Answer

The formula given is for exponential smoothing, which is a time-series forecasting method that gives more weight to the most recent observations while gradually decreasing the weight of older observations. The parameter α is the smoothing coefficient, which determines the rate at which the weights decrease. The other options (moving average, weighted moving average, factor rating method) use different formulas to calculate forecasts.

Question 8

In order to begin exponential smoothing,a forecaster needs ______.
A)seasonal indices
B)tracking signals
C)the value of α
D)leading indicators

Accepted Answer

Exponential smoothing is a time-series forecasting method that uses a smoothing constant (α) to give more weight to recent observations while also considering past observations. Thus, the value of α is crucial for exponential smoothing, and a forecaster needs to determine the optimal value of α for the specific time series they are forecasting. Seasonal indices are useful for forecasting seasonal trends, tracking signals are used to monitor the forecast errors, and leading indicators are used to predict future changes in the time series. However, none of these are necessary to begin exponential smoothing.

Question 9

______ variations are treated as outliers and are eliminated from consideration for forecasting purposes.
A)Cyclical
B)Seasonal
C)Irregular
D)Regular

Accepted Answer

Irregular variations are random fluctuations that cannot be attributed to any particular cause or pattern. They are typically treated as outliers and are eliminated from consideration for forecasting purposes, as they are not expected to repeat in the future. Seasonal and cyclical variations, on the other hand, are accounted for in forecasting models, while regular variations are part of the underlying trend and are also taken into account.

Question 10

Using the weighted average method,with W₁ = 0.5,W2 = 0.3 and W₃ = 0.2,compute the forecast for Week 5 with the data that follows. $$\begin{array} { | c | c | } 
\hline \text { Week } & \text { Number of Patients } \
\hline 1 & 100 \
\hline 2 & 80 \
\hline 3 & 90 \
\hline 4 & 100 \
\hline
\end{array}$$
A)93
B)96
C)98
D)100

Accepted Answer

The forecast for Week 5 using the weighted average method is calculated as follows: (0.5 * 100) + (0.3 * 90) + (0.2 * 80) = 50 + 27 + 16 = 93.

Question 11

Which of the following is a type of nonlinear trend pattern?
A)damped trend
B)trend line
C)no trend (level demand)
D)exponential growth

Accepted Answer

Exponential growth is a type of nonlinear trend pattern where the rate of growth increases over time, unlike linear trends which have a constant rate of change.

Question 12

Using the moving average technique,compute the forecast for Week 5 with n = 3 with the data that follows. $$\begin{array} { | c | c | } 
\hline \text { Week } & \text { Number of Patients } \
\hline 1 & 100 \
\hline 2 & 80 \
\hline 3 & 90 \
\hline 4 & 100 \
\hline
\end{array}$$
A)100
B)90
C)80
D)75

Accepted Answer

To use the moving average technique with n=3, we first take the sum of the first three numbers and divide by 3:

(100 + 80 + 90) / 3 = 90

We then move one week ahead and repeat the process, dropping the first number from the previous calculation and adding the next number in the sequence:

(80 + 90 + 100) / 3 = 90

Therefore, the forecast for Week 5 is 90.

Question 13

When the smoothing constant (α)is set to 1,then exponential smoothing is equivalent to ______.
A)naïve approach
B)weighted moving average
C)moving average
D)factor rating method

Accepted Answer

When the smoothing constant (α) is set to 1 in exponential smoothing, it means that only the most recent observation will be used to forecast future values. This is equivalent to using the naïve approach, which also only takes the most recent observation as the forecast for the next period. Therefore, the correct choice is A, naïve approach. The other options (B, weighted moving average; C, moving average; and D, factor rating method) all involve more than one observation in forecasting future values.

Question 14

The smoothing constant can take a value between ______.
A)-1 to +1
B)0 to 1
C)-1 to 0
D)-∞ to +∞

Accepted Answer

The smoothing constant represents the weight given to the most recent observation in forecasting. It must lie between 0 and 1 as it is used to calculate the weighted average of the observations. A value of 0 means no weight is given to recent observations, and the forecast is based on historical average, whereas a value of 1 gives full weightage to the most recent observation. Hence, the best choice is B, which is 0 to 1.

Question 15

Which of the following is NOT an example of short-term time series forecasting methods?
A)naïve approach
B)moving averages
C)exponential smoothing
D)expert opinion

Accepted Answer

Expert opinion is usually considered a qualitative forecasting method and not a short-term time series forecasting method. The other three options (naïve approach, moving averages, and exponential smoothing) are commonly used for short-term time series forecasting.

Question 16

Which of the following is FALSE about exponential smoothing models?
A)They create accurate forecasts.
B)They lend themselves to large-scale forecasting.
C)They are not very adaptive.
D)They can be easily applied and automated.

Accepted Answer

Exponential smoothing models are known for their adaptability to changes in data trends, making them quite adaptive contrary to the statement in option C.

Question 17

A linear trend can be ______.
A)positive
B)cyclical
C)irregular
D)random

Accepted Answer

A linear trend is characterized by a consistent increase or decrease in values over time, indicating a positive or negative relationship between the variables being measured.

Question 18

______ is a short-term time series forecasting method in which the average of the most recent demand periods is used to predict demand in the future period.
A)Moving average
B)Naïve approach
C)Linear regression
D)Exponential smoothing

Accepted Answer

Moving average is a short-term time series forecasting method in which the average of the most recent demand periods is used to predict future demand. It is a simple and effective method for smoothing out fluctuations in data and is often used in inventory management and sales forecasting. Naïve approach only uses the demand from the previous period as the forecast for the next period, linear regression is a statistical method used for predicting the relationship between two variables, and exponential smoothing is a method that weights recent data more heavily than older data in order to create a forecast. Therefore, the best choice for this scenario is Moving average.

Question 19

______ are wave-like oscillations in demand about the trend line caused by changes in economic or business cycles or due to changes in political conditions.
A)Cyclical variations
B)Seasonal variations
C)Irregular variations
D)Random variations

Accepted Answer

Cyclical variations refer to wave-like oscillations in demand that occur due to changes in economic or business cycles or political conditions. These variations are different from seasonal variations, which occur due to regular and predictable annual patterns, as well as irregular variations, which occur due to sporadic events such as natural disasters or strikes. Random variations, on the other hand, do not follow any specific pattern and are unpredictable. Therefore, the best choice is A) Cyclical variations.

Question 20

Using the naïve approach,compute the forecast for Week 5 with the data that follows. $$\begin{array} { | c | c | } 
\hline \text { Week } & \text { Number of Patients } \
\hline 1 & 100 \
\hline 2 & 80 \
\hline 3 & 90 \
\hline 4 & 100 \
\hline
\end{array}$$
A)90
B)80
C)110
D)100

Accepted Answer

In naive forecasting, we simply take the last observed value and use it as the forecast for the next period. Hence, the forecast for Week 5 would be the number of patients in Week 4, which is 100. Therefore, the answer is D.

Quiz 16: Demand Forecasting Methods

______ is a forecasting method in which it is assumed that the demand in the next period will be the same as it is in the current period.

Simple exponential smoothing or the first-order smoothing model can be used when ______.

Which of the following is a component of trend-adjusted forecast (TAF) ?

A ______ trend often occurs when new products are introduced.

______ is a short-term time series forecasting method in which forecasters assign more weight to most recent values in the time series if they feel that these values reflect how the actual demand will behave in the near future.

Increased retail sales in December and peak demand for snow shovels during winter are examples of ______

New forecast = [(α latest observation) +((1 - α) old forecast) ] is the formula for ______.

In order to begin exponential smoothing,a forecaster needs ______.

______ variations are treated as outliers and are eliminated from consideration for forecasting purposes.

Which of the following is a type of nonlinear trend pattern?

Using the moving average technique,compute the forecast for Week 5 with n = 3 with the data that follows. $\begin{array} { | c | c | } \hline \text { Week } & \text { Number of Patients } \\\hline 1 & 100 \\\hline 2 & 80 \\\hline 3 & 90 \\\hline 4 & 100 \\\hline\end{array}$

When the smoothing constant (α) is set to 1,then exponential smoothing is equivalent to ______.

The smoothing constant can take a value between ______.

Which of the following is NOT an example of short-term time series forecasting methods?

Which of the following is FALSE about exponential smoothing models?

A linear trend can be ______.

______ is a short-term time series forecasting method in which the average of the most recent demand periods is used to predict demand in the future period.

______ are wave-like oscillations in demand about the trend line caused by changes in economic or business cycles or due to changes in political conditions.

Using the naïve approach,compute the forecast for Week 5 with the data that follows. $\begin{array} { | c | c | } \hline \text { Week } & \text { Number of Patients } \\\hline 1 & 100 \\\hline 2 & 80 \\\hline 3 & 90 \\\hline 4 & 100 \\\hline\end{array}$