Question 1

Yamaha manufactures which set of products with complementary demands to address seasonal variations?
A) golf clubs and skis
B) swimming suits and winter jackets
C) jet skis and snowmobiles
D) pianos and guitars
E) ice skates and water skis

Accepted Answer

Jet skis and snowmobiles have complementary demands as they are both used for recreational activities in different seasons. Jet skis are used during summer for water activities while snowmobiles are used during winter for snow activities. This allows Yamaha to have a consistent demand for their products throughout the year.

Question 2

Which of the following is TRUE regarding the two smoothing constants of the Forecast Including Trend (FIT) model?
A) One constant is positive, while the other is negative.
B) They are called MAD and cumulative error.
C) Alpha is always smaller than beta.
D) One constant smooths the regression intercept, whereas the other smooths the regression slope.
E) Their values are determined independently.

Accepted Answer

The answer of Which of the following is TRUE regarding...

Question 3

Which time-series model uses BOTH past forecasts and past demand data to generate a new forecast?
A) naive
B) moving average
C) weighted moving average
D) exponential smoothing
E) trend projection

Accepted Answer

Exponential smoothing uses both past forecasts and past demand data to generate a new forecast by assigning more weight to recent data and less weight to older data. This helps to capture any changes or trends in the data over time. The other models listed (naive, moving average, weighted moving average, and trend projection) only use past demand data to generate a new forecast.

Question 4

A six-month moving average forecast is generally better than a three-month moving average forecast if demand:
A) is rather stable.
B) has been changing due to recent promotional efforts.
C) follows a downward trend.
D) exceeds one million units per year.
E) follows an upward trend.

Accepted Answer

A six-month moving average forecast is generally better than a three-month moving average forecast if demand is rather stable. This is because a six-month moving average is better suited to smoothing out random fluctuations in demand that may occur over shorter periods of time, such as three months. However, if demand has been changing due to recent promotional efforts or follows an upward/downward trend, a shorter-term moving average forecast may be more appropriate.

Question 5

Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast?
A) 0
B) 1 divided by the number of periods
C) 0.5
D) 1.0
E) cannot be determined

Accepted Answer

A naive forecast simply uses the last observed value as the forecast for the next period. An exponential smoothing forecast with a smoothing constant (alpha) of 1.0 would give the full weight to the most recent observation, effectively making it the same as a naive forecast.

Question 6

A forecast based on the previous forecast plus a percentage of the forecast error is a(n):
A) qualitative forecast.
B) naive forecast.
C) moving average forecast.
D) weighted moving average forecast.
E) exponential smoothing forecast.

Accepted Answer

The forecast described in the question is the formula for exponential smoothing forecast, where the forecast for the next period is equal to the previous forecast plus a percentage (alpha) of the difference between the actual value and the previous forecast.

Question 7

A time-series trend equation is 25.3 + 21x. What is your forecast for period 7?
A) 324.1
B) 25.3
C) 32.3
D) 40.0
E) 172.3

Accepted Answer

The forecast for period 7 is calculated by substituting x=7 into the equation: 25.3 + 21(7) = 25.3 + 147 = 172.3.

Question 8

Which time-series model below assumes that demand in the next period will be equal to the most recent period's forecast?
A) naive approach
B) moving average approach
C) weighted moving average approach
D) exponential smoothing approach with α = 0
E) trend projection

Accepted Answer

Exponential smoothing with α = 0 assumes that demand in the next period will be equal to the most recent period's forecast.

Question 9

Which of the following is NOT a characteristic of exponential smoothing?
A) smooths random variations in the data
B) uses an easily altered weighting scheme
C) weights each historical value equally
D) has minimal data storage requirements
E) uses the previous period's forecast

Accepted Answer

Exponential smoothing does not weight each historical value equally, as it places greater weight on more recent observations.

Question 10

For a given product demand, the time-series trend equation is 53 - 4x. The negative sign on the slope of the equation:
A) is a mathematical impossibility.
B) is an indication that the forecast is biased, with forecast values lower than actual values.
C) is an indication that product demand is declining.
D) implies that the coefficient of determination will also be negative.
E) implies that the cumulative error will be negative.

Accepted Answer

The negative slope of the equation (−4) indicates that product demand is declining over time. Option A is incorrect because it is not a mathematical impossibility for a time-series trend equation to have a negative slope. Option B is incorrect because the bias of the forecast cannot be inferred from the slope of the trend equation alone. Option D and E are incorrect because the coefficient of determination and cumulative error are not necessarily related to the slope of the trend equation.

Question 11

The primary purpose of the mean absolute deviation (MAD) in forecasting is to:
A) estimate the trend line.
B) eliminate forecast errors.
C) measure forecast accuracy.
D) seasonally adjust the forecast.
E) remove random variations.

Accepted Answer

The primary purpose of MAD in forecasting is to measure forecast accuracy. It helps to determine how close the forecasted values are to the actual values by calculating the average difference between them.

Question 12

Which of the following values of alpha would cause exponential smoothing to respond the QUICKEST to changes in demand data?
A) 0.10
B) 0.2246
C) 0.50
D) 0.90
E) cannot be determined

Accepted Answer

In exponential smoothing, a higher alpha value (closer to 1) causes the formula to give more weight to the most recent data, thus responding quicker to changes. Therefore, 0.90 would cause the quickest response.

Question 13

Increasing the number of periods in a moving average will accomplish greater smoothing, but at the expense of:
A) manager understanding.
B) accuracy.
C) stability.
D) sensitivity to real changes in the data.
E) All of the these are diminished when the number of periods increases.

Accepted Answer

Increasing the number of periods in a moving average will increase the smoothing and reduce the impact of short-term fluctuations, but it will also reduce sensitivity to real changes in the data. This means that it may not respond adequately to sudden changes or outliers, which can affect the accuracy of the forecast. The other options (manager understanding, stability) are not necessarily affected by the number of periods in the moving average.

Question 14

Given an actual demand this period of 67, a forecast for this period of 58, and an alpha of 0.3, what would the forecast for the next period be using exponential smoothing?
A) 55.3
B) 57.1
C) 58.9
D) 61.0
E) 60.7

Accepted Answer

The answer of Given an actual demand this period of...

Question 15

Suppose that the last four months of sales were 8, 10, 15, and 9 units, respectively. Suppose further that the last four forecasts were 5, 6, 11, and 12 units, respectively. What is the Mean Absolute Deviation (MAD) of these forecasts?
A) 2
B) -10
C) 3.5
D) 9
E) 10.5

Accepted Answer

MAD is defined as the average of the absolute values of the forecast errors. The forecast errors are simply the differences between the actual sales and the forecasted sales for each month. So the forecast errors for each month are:
-3 (5-8)
-4 (6-10)
4 (15-11)
-3 (9-12)

Taking the absolute values, we get:
3
4
4
3

The average of these values is (3+4+4+3)/4 = 3.5. Therefore, the MAD is 3.5 units.

Question 16

John's House of Pancakes uses a weighted moving average method to forecast pancake sales. It assigns a weight of 5 to the previous month's demand, 3 to demand two months ago, and 1 to demand three months ago. If sales amounted to 3000 pancakes in May, 2200 pancakes in June, and 1000 pancakes in July, what should be the forecast for August?
A) 2400
B) 2511
C) 2067
D) 3767
E) 1622

Accepted Answer

The answer of John's House of Pancakes uses a weighted...

Question 17

Given an actual demand this period of 103, a forecast value for this period of 99, and an alpha of .6, what is the exponential smoothing forecast for next period?
A) 96.6
B) 97.4
C) 100.6
D) 101.4
E) -105.4

Accepted Answer

Using exponential smoothing, we can find the forecast for next period as:
Forecast next period = alpha x actual demand this period + (1 - alpha) x forecast value for this period
= 0.6 x 103 + 0.4 x 99
= 101.4

Question 18

Which of the following statements comparing exponential smoothing to the weighted moving average technique is TRUE?
A) Exponential smoothing is more easily used in combination with the Delphi method.
B) More emphasis can be placed on recent values using the weighted moving average.
C) Exponential smoothing is considerably more difficult to implement on a computer.
D) Exponential smoothing typically requires less record keeping of past data.
E) Exponential smoothing allows one to develop forecasts for multiple periods, whereas the weighted moving average technique does not.

Accepted Answer

Exponential smoothing requires less record keeping of past data because it uses a smoothing constant to give more weight to recent data, without the need to keep all historical data.

Question 19

Given forecast errors of -1, 4, 4, and -3, what is the mean absolute deviation?
A) 1
B) 3
C) 4
D) 8
E) 12

Accepted Answer

1. Find the absolute values of each forecast error: 1, 4, 4, 3
2. Find the mean of these absolute values: (1+4+4+3)/4 = 3
3. Therefore, the mean absolute deviation is 3. Choice B is correct.

Question 20

A forecasting method has produced the following over the past five months. What is the mean absolute deviation? 
A) -0.2
B) 12.0
C) 6.0
D) 1.2
E) 2.4

Accepted Answer

The answer of A forecasting method has produced the following...

Quiz 4: Forecasting

Yamaha manufactures which set of products with complementary demands to address seasonal variations?

Which of the following is TRUE regarding the two smoothing constants of the Forecast Including Trend (FIT) model?

Which time-series model uses BOTH past forecasts and past demand data to generate a new forecast?

A six-month moving average forecast is generally better than a three-month moving average forecast if demand:

Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast?

A forecast based on the previous forecast plus a percentage of the forecast error is a(n) :

A time-series trend equation is 25.3 + 21x. What is your forecast for period 7?

Which time-series model below assumes that demand in the next period will be equal to the most recent period's forecast?

Which of the following is NOT a characteristic of exponential smoothing?

For a given product demand, the time-series trend equation is 53 - 4x. The negative sign on the slope of the equation:

The primary purpose of the mean absolute deviation (MAD) in forecasting is to:

Which of the following values of alpha would cause exponential smoothing to respond the QUICKEST to changes in demand data?

Increasing the number of periods in a moving average will accomplish greater smoothing, but at the expense of:

Given an actual demand this period of 67, a forecast for this period of 58, and an alpha of 0.3, what would the forecast for the next period be using exponential smoothing?

Suppose that the last four months of sales were 8, 10, 15, and 9 units, respectively. Suppose further that the last four forecasts were 5, 6, 11, and 12 units, respectively. What is the Mean Absolute Deviation (MAD) of these forecasts?

Given an actual demand this period of 103, a forecast value for this period of 99, and an alpha of .6, what is the exponential smoothing forecast for next period?

Which of the following statements comparing exponential smoothing to the weighted moving average technique is TRUE?

Given forecast errors of -1, 4, 4, and -3, what is the mean absolute deviation?

A forecasting method has produced the following over the past five months. What is the mean absolute deviation?