MQM 227 Actual Questions and Correct Answers
Q1
Regarding types of time-series components, which of the following statements is incorrect?
Answer: B. Seasonality is represented by an irregular pattern of demand.
Q2
What is the August forecasted demand using a 4-month moving average for the following actual
demand values: January - 300 units, February - 200 units, March - 400 units, April - 500 units,
May - 600 units, June - 600 units, July - 700 units. (Round answer to nearest whole number.)
Answer: A. 4 month = 600
Q3
What is the August forecasted demand using a 4-month weighted moving average for the
following past actual demand: January - 300 units, February - 200 units, March - 400 units, April
- 500 units, May 600 units, June - 600 units, July 700 units. P1 = 0.4, P2 = 0.3, P3 = 0.2, P4 =
0.1 (Round answer to nearest whole number.)
Answer: 630
Q4
What is the August forecasted demand using a 6-month as well as a 6-month moving average
for the following actual demand values: January - 300 units, February - 200 units, March - 400
units, April - 500 units, May 600 units, June - 600 units, July 700 units. (Round answer to nearest
whole number.)
Answer: B. 6 month = 500
Q5
The qualitative type of forecasting utilizes time-series and causal approaches. There is no
universal forecasting method for all situations. In organizations, managers are usually most
interested in predicting future demand. In CPFR, a long-term, mutually beneficial, collaborative
relationship is needed. In a regular moving average calculation, all of the months included in
the calculation are weighted equally.
Answer: A. The qualitative type of forecasting utilizes time-series and causal
approaches.
, Q6
Short-range forecasts are good for procuring materials and work scheduling. Short-range
forecasts are usually less than 6 months in the future. Casual forecasting methods are great for
predicting turning points in demand. Medium-range forecasts are usually 6 months to two years
in the future. Causal forecasting methods are usually more accurate than time-series models for
medium-to long-range forecasts.
Answer: C. Casual forecasting methods are great for predicting turning points in
demand.
Q7
An essential forecast number is the forecasting error (e.g., standard deviation or range).
Weighted moving average is the simplest method of time-series forecasting. For the weighted
moving average, the sum of coefficients always equals to 1.0. Qualitative forecasting methods
rely on managerial judgment. In a weighted moving average calculation, demand in more
recent months is often given a higher coefficient so the moving average calculation is more
responsive to more recent changes in demand.
Answer: B. Weighted moving average is the simplest method of time-series
forecasting.
Q8
Long-range forecasts are good for planning facilities and processes. Quantitative forecasting
generally assumes that past data and data patterns are reliable predictors of the future.
Time-series forecasting is used to make general analyses of past demand patterns over time.
Time-series forecasting uses these demand patterns to predict demand in the future. In CPFR,
all parties must be willing to share sensitive information about demand data, future sales
promotions, potential orders, new products, and lead times.
Answer: C. Time-series forecasting is used to make general analyses of past demand
patterns over time.
Q9
In a weighted moving average, some of the months in the calculation are considered more
important and are assigned a higher coefficient. Exponential smoothing is a special form of the
weighted moving average. Quantitative forecasting methods use an implicit ("best guess")
forecasting model. In CPFR, sufficient time and resources must be provided for it to succeed. An
essential forecast number is the best estimate of demand (e.g., mean, median, mode).
Answer: C. Quantitative forecasting methods use an implicit ("best guess") forecasting
model.
Q1
Regarding types of time-series components, which of the following statements is incorrect?
Answer: B. Seasonality is represented by an irregular pattern of demand.
Q2
What is the August forecasted demand using a 4-month moving average for the following actual
demand values: January - 300 units, February - 200 units, March - 400 units, April - 500 units,
May - 600 units, June - 600 units, July - 700 units. (Round answer to nearest whole number.)
Answer: A. 4 month = 600
Q3
What is the August forecasted demand using a 4-month weighted moving average for the
following past actual demand: January - 300 units, February - 200 units, March - 400 units, April
- 500 units, May 600 units, June - 600 units, July 700 units. P1 = 0.4, P2 = 0.3, P3 = 0.2, P4 =
0.1 (Round answer to nearest whole number.)
Answer: 630
Q4
What is the August forecasted demand using a 6-month as well as a 6-month moving average
for the following actual demand values: January - 300 units, February - 200 units, March - 400
units, April - 500 units, May 600 units, June - 600 units, July 700 units. (Round answer to nearest
whole number.)
Answer: B. 6 month = 500
Q5
The qualitative type of forecasting utilizes time-series and causal approaches. There is no
universal forecasting method for all situations. In organizations, managers are usually most
interested in predicting future demand. In CPFR, a long-term, mutually beneficial, collaborative
relationship is needed. In a regular moving average calculation, all of the months included in
the calculation are weighted equally.
Answer: A. The qualitative type of forecasting utilizes time-series and causal
approaches.
, Q6
Short-range forecasts are good for procuring materials and work scheduling. Short-range
forecasts are usually less than 6 months in the future. Casual forecasting methods are great for
predicting turning points in demand. Medium-range forecasts are usually 6 months to two years
in the future. Causal forecasting methods are usually more accurate than time-series models for
medium-to long-range forecasts.
Answer: C. Casual forecasting methods are great for predicting turning points in
demand.
Q7
An essential forecast number is the forecasting error (e.g., standard deviation or range).
Weighted moving average is the simplest method of time-series forecasting. For the weighted
moving average, the sum of coefficients always equals to 1.0. Qualitative forecasting methods
rely on managerial judgment. In a weighted moving average calculation, demand in more
recent months is often given a higher coefficient so the moving average calculation is more
responsive to more recent changes in demand.
Answer: B. Weighted moving average is the simplest method of time-series
forecasting.
Q8
Long-range forecasts are good for planning facilities and processes. Quantitative forecasting
generally assumes that past data and data patterns are reliable predictors of the future.
Time-series forecasting is used to make general analyses of past demand patterns over time.
Time-series forecasting uses these demand patterns to predict demand in the future. In CPFR,
all parties must be willing to share sensitive information about demand data, future sales
promotions, potential orders, new products, and lead times.
Answer: C. Time-series forecasting is used to make general analyses of past demand
patterns over time.
Q9
In a weighted moving average, some of the months in the calculation are considered more
important and are assigned a higher coefficient. Exponential smoothing is a special form of the
weighted moving average. Quantitative forecasting methods use an implicit ("best guess")
forecasting model. In CPFR, sufficient time and resources must be provided for it to succeed. An
essential forecast number is the best estimate of demand (e.g., mean, median, mode).
Answer: C. Quantitative forecasting methods use an implicit ("best guess") forecasting
model.
