Deck 7: Demand Forecasting in a Supply Chain
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Deck 7: Demand Forecasting in a Supply Chain
1
Forecasts should include both the expected value of the forecast and a measure of forecast error.
TRUE
2
Aggregate forecasts are usually more accurate than disaggregate forecasts, as they tend to have a smaller standard deviation of error relative to the mean.
TRUE
3
Long-term forecasts have a larger standard deviation of error relative to the mean than short-term forecasts.
TRUE
4
Forecasting and the accompanying managerial decisions are extremely difficult when either the supply of raw materials or the demand for the finished product is highly unpredictable.
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5
Time series forecasting methods are the most difficult methods to implement.
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6
The forecast error measures the difference between the forecast and the estimate.
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7
In adaptive forecasting, the estimates of level, trend, and seasonality are updated after each demand observation.
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8
The forecast of demand forms the basis for all strategic and planning decisions in a supply chain.
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9
Forecasting and the accompanying managerial decisions are extremely difficult when either the supply of raw materials or the demand for the finished product is highly variable.
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10
Mature products with stable demand are usually the most difficult to forecast.
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11
Leaders in many supply chains have started moving toward collaborative forecasting to improve their ability to match supply and demand.
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12
The basis for all strategic and planning decisions in a supply chain comes from
A) the forecast of demand.
B) sales targets.
C) profitability projections.
D) production efficiency goals.
A) the forecast of demand.
B) sales targets.
C) profitability projections.
D) production efficiency goals.
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13
Qualitative forecasting methods are most appropriate when there is good historical data available or when experts do not have market intelligence that is critical in making the forecast.
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14
For push processes, a manager must forecast what customer demand will be in order to
A) plan the service level.
B) plan the level of available capacity and inventory.
C) plan the level of productivity.
D) plan the level of production.
A) plan the service level.
B) plan the level of available capacity and inventory.
C) plan the level of productivity.
D) plan the level of production.
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15
The result when each stage in the supply chain makes its own separate forecast is often a match between supply and demand because these forecasts are often very different.
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16
For pull processes, a manager must forecast what customer demand will be in order to plan the level of available capacity and inventory.
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17
The moving average forecast method is used when demand has an observable trend or seasonality.
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18
Collaborative forecasting based on sales to the end customer can help enterprises further up the supply chain reduce forecast error.
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19
Causal forecasting methods find a correlation between demand and environmental factors and use estimates of what environmental factors will be to forecast future demand.
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20
The goal of any forecasting method is to predict the systematic component of demand and estimate the random component.
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21
Production can utilize forecasts to make decisions concerning
A) scheduling.
B) sales-force allocation.
C) promotions.
D) budgetary planning.
A) scheduling.
B) sales-force allocation.
C) promotions.
D) budgetary planning.
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22
The result of each stage in the supply chain making its own separate forecast is
A) an accurate forecast.
B) a more accurate forecast.
C) a match between supply and demand.
D) a mismatch between supply and demand.
A) an accurate forecast.
B) a more accurate forecast.
C) a match between supply and demand.
D) a mismatch between supply and demand.
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23
Leaders in many supply chains have started moving
A) toward independent forecasting to improve their ability to match supply and demand.
B) toward consecutive forecasting to improve their ability to match supply and demand.
C) toward sequential forecasting to improve their ability to match supply and demand.
D) toward collaborative forecasting to improve their ability to match supply and demand.
A) toward independent forecasting to improve their ability to match supply and demand.
B) toward consecutive forecasting to improve their ability to match supply and demand.
C) toward sequential forecasting to improve their ability to match supply and demand.
D) toward collaborative forecasting to improve their ability to match supply and demand.
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24
Qualitative forecasting methods are most appropriate when
A) there is good historical data available.
B) there is little historical data available.
C) experts do not have critical market intelligence.
D) forecasting demand into the near future.
A) there is good historical data available.
B) there is little historical data available.
C) experts do not have critical market intelligence.
D) forecasting demand into the near future.
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25
The resulting accuracy of a collaborative forecast enables supply chains to be
A) more responsive but less efficient in serving their customers.
B) both more responsive and more efficient in serving their customers.
C) less responsive but less efficient in serving their customers.
D) both less responsive and less efficient in serving their customers.
A) more responsive but less efficient in serving their customers.
B) both more responsive and more efficient in serving their customers.
C) less responsive but less efficient in serving their customers.
D) both less responsive and less efficient in serving their customers.
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26
When all stages of a supply chain produce a collaborative forecast, it tends to be
A) much more detailed.
B) much more complex.
C) much more accurate.
D) much more flexible.
A) much more detailed.
B) much more complex.
C) much more accurate.
D) much more flexible.
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27
In general, the further up the supply chain a company is (or the further they are from the consumer),
A) the greater the distortion of information they receive.
B) the smaller the distortion of information they receive.
C) the information they receive is more accurate.
D) the information they receive is more useful.
A) the greater the distortion of information they receive.
B) the smaller the distortion of information they receive.
C) the information they receive is more accurate.
D) the information they receive is more useful.
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28
Mature products with stable demand
A) are usually easiest to forecast.
B) are usually hardest to forecast.
C) cannot be forecast.
D) do not need to be forecast.
A) are usually easiest to forecast.
B) are usually hardest to forecast.
C) cannot be forecast.
D) do not need to be forecast.
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29
Personnel can utilize forecasts to make decisions concerning
A) scheduling.
B) promotions.
C) plant/equipment investment.
D) purchasing.
A) scheduling.
B) promotions.
C) plant/equipment investment.
D) purchasing.
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30
Forecasting methods that use historical demand to make a forecast are known as
A) qualitative forecasting methods.
B) time series forecasting methods.
C) causal forecasting methods.
D) simulation forecasting methods.
A) qualitative forecasting methods.
B) time series forecasting methods.
C) causal forecasting methods.
D) simulation forecasting methods.
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31
Forecasting methods that imitate the consumer choices that give rise to demand to arrive at a forecast are known as
A) qualitative forecasting methods.
B) time series forecasting methods.
C) causal forecasting methods.
D) simulation forecasting methods.
A) qualitative forecasting methods.
B) time series forecasting methods.
C) causal forecasting methods.
D) simulation forecasting methods.
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32
The goal of any forecasting method is to
A) predict the random component of demand and estimate the systematic component.
B) predict the systematic component of demand and estimate the random component.
C) predict the seasonal component of demand and estimate the random component.
D) predict the random component of demand and estimate the seasonal component.
A) predict the random component of demand and estimate the systematic component.
B) predict the systematic component of demand and estimate the random component.
C) predict the seasonal component of demand and estimate the random component.
D) predict the random component of demand and estimate the seasonal component.
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33
Aggregate forecasts are usually more accurate than disaggregate forecasts because
A) aggregate forecasts tend to have a larger standard deviation of error relative to the mean.
B) aggregate forecasts tend to have a smaller standard deviation of error relative to the mean.
C) disaggregate forecasts tend to have a smaller standard deviation of error relative to the mean.
D) disaggregate forecasts tend to have less standard deviation of error relative to the mean.
A) aggregate forecasts tend to have a larger standard deviation of error relative to the mean.
B) aggregate forecasts tend to have a smaller standard deviation of error relative to the mean.
C) disaggregate forecasts tend to have a smaller standard deviation of error relative to the mean.
D) disaggregate forecasts tend to have less standard deviation of error relative to the mean.
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34
Long-term forecasts are usually less accurate than short-term forecasts because
A) short-term forecasts have a larger standard deviation of error relative to the mean than long-term forecasts.
B) short-term forecasts have more standard deviation of error relative to the mean than long-term forecasts.
C) long-term forecasts have a smaller standard deviation of error relative to the mean than short-term forecasts.
D) long-term forecasts have a larger standard deviation of error relative to the mean than short-term forecasts.
A) short-term forecasts have a larger standard deviation of error relative to the mean than long-term forecasts.
B) short-term forecasts have more standard deviation of error relative to the mean than long-term forecasts.
C) long-term forecasts have a smaller standard deviation of error relative to the mean than short-term forecasts.
D) long-term forecasts have a larger standard deviation of error relative to the mean than short-term forecasts.
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35
Forecasts are always wrong and therefore
A) should include both the expected value of the forecast and a measure of forecast error.
B) should not include both the expected value of the forecast and a measure of forecast error.
C) should only be used when there are no accurate estimates.
D) should be missing the expected value of the forecast and a measure of forecast error.
A) should include both the expected value of the forecast and a measure of forecast error.
B) should not include both the expected value of the forecast and a measure of forecast error.
C) should only be used when there are no accurate estimates.
D) should be missing the expected value of the forecast and a measure of forecast error.
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36
When either the supply of raw materials or the demand for the finished product is highly variable, forecasting and the accompanying managerial decisions
A) are extremely simple.
B) are relatively straightforward.
C) are extremely difficult.
D) should not be attempted.
A) are extremely simple.
B) are relatively straightforward.
C) are extremely difficult.
D) should not be attempted.
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37
Forecasting methods that assume that the demand forecast is highly correlated with certain factors in the environment (e.g., the state of the economy, interest rates, etc.) to make a forecast are known as
A) qualitative forecasting methods.
B) time series forecasting methods.
C) causal forecasting methods.
D) simulation forecasting methods.
A) qualitative forecasting methods.
B) time series forecasting methods.
C) causal forecasting methods.
D) simulation forecasting methods.
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38
One of the characteristics of forecasts is
A) aggregate forecasts are usually less accurate than disaggregate forecasts.
B) disaggregate forecasts are usually more accurate than aggregate forecasts.
C) short-term forecasts are usually less accurate than long-term forecasts.
D) long-term forecasts are usually less accurate than short-term forecasts.
A) aggregate forecasts are usually less accurate than disaggregate forecasts.
B) disaggregate forecasts are usually more accurate than aggregate forecasts.
C) short-term forecasts are usually less accurate than long-term forecasts.
D) long-term forecasts are usually less accurate than short-term forecasts.
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39
Which forecasting methods are the simplest to implement and can serve as a good starting point for a demand forecast?
A) Qualitative forecasting methods
B) Time series forecasting methods
C) Causal forecasting methods
D) Simulation forecasting methods
A) Qualitative forecasting methods
B) Time series forecasting methods
C) Causal forecasting methods
D) Simulation forecasting methods
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40
One of the characteristics of forecasts is
A) aggregate forecasts are usually more accurate than disaggregate forecasts.
B) disaggregate forecasts are usually more accurate than aggregate forecasts.
C) short-term forecasts are usually less accurate than long-term forecasts.
D) long-term forecasts are usually more accurate than short-term forecasts.
A) aggregate forecasts are usually more accurate than disaggregate forecasts.
B) disaggregate forecasts are usually more accurate than aggregate forecasts.
C) short-term forecasts are usually less accurate than long-term forecasts.
D) long-term forecasts are usually more accurate than short-term forecasts.
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41
The multiplicative form of the systematic component of demand is shown as
A) level × trend × seasonal factor.
B) level + trend + seasonal factor.
C) (level + trend) × seasonal factor.
D) level × (trend + seasonal factor).
A) level × trend × seasonal factor.
B) level + trend + seasonal factor.
C) (level + trend) × seasonal factor.
D) level × (trend + seasonal factor).
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42
Scenario 7.1 - Marshmallow Madness
Historical demand for Peeps is as displayed in the table.

What is the level component of Holt's model for period 0?
A) -2.5
B) 10.3
C) 2.5
D) 6.4
Historical demand for Peeps is as displayed in the table.

What is the level component of Holt's model for period 0?
A) -2.5
B) 10.3
C) 2.5
D) 6.4
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43
The measure of forecast error where the amount of error of each forecast is squared and then an average is calculated is
A) mean squared error (MSE).
B) mean absolute deviation (MAD).
C) mean absolute percentage error (MAPE).
D) the tracking signal.
A) mean squared error (MSE).
B) mean absolute deviation (MAD).
C) mean absolute percentage error (MAPE).
D) the tracking signal.
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44
In adaptive forecasting,
A) there is an assumption that the estimates of level, trend, and seasonality within the systematic component do not vary as new demand is observed.
B) the estimates of level, trend, and seasonality within the systematic component are not adjusted as new demand is observed.
C) the estimates of level, trend, and seasonality are updated after each demand observation.
D) All of the above are true.
A) there is an assumption that the estimates of level, trend, and seasonality within the systematic component do not vary as new demand is observed.
B) the estimates of level, trend, and seasonality within the systematic component are not adjusted as new demand is observed.
C) the estimates of level, trend, and seasonality are updated after each demand observation.
D) All of the above are true.
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45
Scenario 7.1 - Marshmallow Madness
Historical demand for Peeps is as displayed in the table.

Use a simple moving average of three periods to forecast the demand for July. What is the forecast?
A) 67
B) 58
C) 48.5
D) 45.3
Historical demand for Peeps is as displayed in the table.

Use a simple moving average of three periods to forecast the demand for July. What is the forecast?
A) 67
B) 58
C) 48.5
D) 45.3
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46
________ forecasting methods assume that the demand forecast is highly correlated with certain factors in the environment (the state of the economy, interest rates, etc.).
A) Qualitative
B) Time-series
C) Causal
D) Simulation
A) Qualitative
B) Time-series
C) Causal
D) Simulation
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47
The measure of forecast error where the absolute amount of error of each forecast is averaged is
A) mean squared error (MSE).
B) mean absolute deviation (MAD).
C) mean absolute percentage error (MAPE).
D) bias.
A) mean squared error (MSE).
B) mean absolute deviation (MAD).
C) mean absolute percentage error (MAPE).
D) bias.
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48
A static method of forecasting
A) assumes that the estimates of level, trend, and seasonality within the systematic component do not vary as new demand is observed.
B) assumes that the estimates of level, trend, and seasonality within the systematic component vary as new demand is observed.
C) the estimates of level, trend, and seasonality are updated after each demand observation.
D) All of the above are true.
A) assumes that the estimates of level, trend, and seasonality within the systematic component do not vary as new demand is observed.
B) assumes that the estimates of level, trend, and seasonality within the systematic component vary as new demand is observed.
C) the estimates of level, trend, and seasonality are updated after each demand observation.
D) All of the above are true.
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49
The mixed form of the systematic component of demand is shown as
A) level × trend × seasonal factor.
B) level + trend + seasonal factor.
C) (level + trend) × seasonal factor.
D) level × (trend + seasonal factor).
A) level × trend × seasonal factor.
B) level + trend + seasonal factor.
C) (level + trend) × seasonal factor.
D) level × (trend + seasonal factor).
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50
Scenario 7.1 - Marshmallow Madness
Historical demand for Peeps is as displayed in the table.

Use exponential smoothing to forecast the demand for March. What is the forecast if ? = 0.7?
A) 27.5
B) 31.25
C) 28.75
D) 29.25
Historical demand for Peeps is as displayed in the table.

Use exponential smoothing to forecast the demand for March. What is the forecast if ? = 0.7?
A) 27.5
B) 31.25
C) 28.75
D) 29.25
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51
Scenario 7.1 - Marshmallow Madness
Historical demand for Peeps is as displayed in the table.

What is the level component of Holt's model for period 0?
A) -2.5
B) 10.3
C) 2.5
D) 6.4
Historical demand for Peeps is as displayed in the table.

What is the level component of Holt's model for period 0?
A) -2.5
B) 10.3
C) 2.5
D) 6.4
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52
The measure of whether a forecast method consistently over- or underestimates demand is
A) mean absolute deviation (MAD).
B) mean absolute percentage error (MAPE).
C) bias.
D) the tracking signal.
A) mean absolute deviation (MAD).
B) mean absolute percentage error (MAPE).
C) bias.
D) the tracking signal.
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53
The moving average forecast method is used when
A) demand has observable trend or seasonality.
B) demand has no observable trend or seasonality.
C) demand has observable trend and seasonality.
D) demand has no observable level or seasonality.
A) demand has observable trend or seasonality.
B) demand has no observable trend or seasonality.
C) demand has observable trend and seasonality.
D) demand has no observable level or seasonality.
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54
________ forecasting methods are primarily subjective and rely on human judgment.
A) Qualitative
B) Time-series
C) Causal
D) Simulation
A) Qualitative
B) Time-series
C) Causal
D) Simulation
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55
The additive form of the systematic component of demand is shown as
A) level × trend × seasonal factor.
B) level + trend + seasonal factor.
C) (level + trend) × seasonal factor.
D) level × (trend + seasonal factor).
A) level × trend × seasonal factor.
B) level + trend + seasonal factor.
C) (level + trend) × seasonal factor.
D) level × (trend + seasonal factor).
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56
The measure of forecast error where the average absolute error of each forecast is shown as a percentage of demand is
A) mean squared error (MSE).
B) mean absolute percentage error (MAPE).
C) bias.
D) the tracking signal.
A) mean squared error (MSE).
B) mean absolute percentage error (MAPE).
C) bias.
D) the tracking signal.
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57
The trend corrected exponential smoothing (Holt's Model) forecast method is appropriate when
A) demand has observable trend or seasonality.
B) demand has no observable trend or seasonality.
C) demand has observable trend but no seasonality.
D) demand has no observable level or seasonality.
A) demand has observable trend or seasonality.
B) demand has no observable trend or seasonality.
C) demand has observable trend but no seasonality.
D) demand has no observable level or seasonality.
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58
________ forecasting methods use historical demand to make a forecast.
A) Qualitative
B) Time-series
C) Causal
D) Simulation
A) Qualitative
B) Time-series
C) Causal
D) Simulation
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59
The simple exponential smoothing forecast method is appropriate when
A) demand has observable trend or seasonality.
B) demand has no observable trend or seasonality.
C) demand has observable trend and seasonality.
D) demand has no observable level or seasonality.
A) demand has observable trend or seasonality.
B) demand has no observable trend or seasonality.
C) demand has observable trend and seasonality.
D) demand has no observable level or seasonality.
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60
Scenario 7.1 - Marshmallow Madness
Historical demand for Peeps is as displayed in the table.

What is the trend component of Holt's model for period 0?
A) -2.5
B) 10.3
C) 2.5
D) 6.4
Historical demand for Peeps is as displayed in the table.

What is the trend component of Holt's model for period 0?
A) -2.5
B) 10.3
C) 2.5
D) 6.4
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61
Scenario 7.2

Use Solver to determine the alpha that minimizes the MAD for the ten period forecast for the data that appear in this table. Use the actual demand as the forecast for period 1 and then use exponential smoothing.
A) 0.2
B) 0.3
C) 0.4
D) 0.5

Use Solver to determine the alpha that minimizes the MAD for the ten period forecast for the data that appear in this table. Use the actual demand as the forecast for period 1 and then use exponential smoothing.

A) 0.2
B) 0.3
C) 0.4
D) 0.5
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62
Explain the basic, six-step approach to help an organization perform effective forecasting.
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63
Scenario 7.2

Develop a forecast for this data using simple exponential smoothing with an alpha of 0.66. Then calculate MAD, MSE and the tracking signal.

Develop a forecast for this data using simple exponential smoothing with an alpha of 0.66. Then calculate MAD, MSE and the tracking signal.
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64
Discuss key issues of forecasting in practice.
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65
Which of the following is a commonly used measure for measuring forecast error?
A) MDE
B) MKE
C) MAD
D) MES
A) MDE
B) MKE
C) MAD
D) MES
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66
Scenario 7.2

What is the mean absolute percentage error if the forecasts for periods 1-10 are in order, 176.6, 174.2, 176.1, 178.7, 160.4, 165.4, 177.7, 191.1, 191.0, and 175.2?
A) 5.8
B) 5.6
C) 5.4
D) 5.2

What is the mean absolute percentage error if the forecasts for periods 1-10 are in order, 176.6, 174.2, 176.1, 178.7, 160.4, 165.4, 177.7, 191.1, 191.0, and 175.2?
A) 5.8
B) 5.6
C) 5.4
D) 5.2
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67
Scenario 7.1 - Marshmallow Madness
Historical demand for Peeps is as displayed in the table.

Develop forecasts for June through October using these techniques: moving average of two period, simple exponential smoothing with an alpha of 0.8, and Holt's method. For the exponential smoothing model assume that the forecast for May is the actual demand for May. Comment on the use of these three methods to generate a forecast in this situation.
Historical demand for Peeps is as displayed in the table.

Develop forecasts for June through October using these techniques: moving average of two period, simple exponential smoothing with an alpha of 0.8, and Holt's method. For the exponential smoothing model assume that the forecast for May is the actual demand for May. Comment on the use of these three methods to generate a forecast in this situation.
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68
Describe the basic characteristics of forecasts that managers should be aware.
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69
The ________ is a good measure of forecast error when the underlying forecast has significant seasonality and demand varies considerably from one period to the next.
A) MAD
B) MSE
C) MKE
D) MAPE
A) MAD
B) MSE
C) MKE
D) MAPE
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70
The measure of how significantly a forecast method consistently over- or underestimates demand is
A) mean squared error (MSE).
B) mean absolute deviation (MAD).
C) bias.
D) the tracking signal.
A) mean squared error (MSE).
B) mean absolute deviation (MAD).
C) bias.
D) the tracking signal.
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71
Scenario 7.2

Calculate the MAD for this scenario if the forecasts for periods 1-10 are in order, 176.6, 174.2, 176.1, 178.7, 160.4, 165.4, 177.7, 191.1, 191.0, and 175.2.
A) 11.04
B) 9.52
C) 10.40
D) 12.25

Calculate the MAD for this scenario if the forecasts for periods 1-10 are in order, 176.6, 174.2, 176.1, 178.7, 160.4, 165.4, 177.7, 191.1, 191.0, and 175.2.
A) 11.04
B) 9.52
C) 10.40
D) 12.25
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72
Scenario 7.2

What is the largest value for the tracking signal (either under or overforecasting) if the forecasts for periods 1-10 are in order, 176.6, 174.2, 176.1, 178.7, 160.4, 165.4, 177.7, 191.1, 191.0, and 175.2?
A) 1.86
B) -2.07
C) 2.58
D) 3.24

What is the largest value for the tracking signal (either under or overforecasting) if the forecasts for periods 1-10 are in order, 176.6, 174.2, 176.1, 178.7, 160.4, 165.4, 177.7, 191.1, 191.0, and 175.2?
A) 1.86
B) -2.07
C) 2.58
D) 3.24
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73
Scenario 7.2

Calculate the MSE for this scenario if the forecasts for periods 1-10 are in order, 176.6, 174.2, 176.1, 178.7, 160.4, 165.4, 177.7, 191.1, 191.0, and 175.2.
A) 216.60
B) 219.80
C) 210.40
D) 221.20

Calculate the MSE for this scenario if the forecasts for periods 1-10 are in order, 176.6, 174.2, 176.1, 178.7, 160.4, 165.4, 177.7, 191.1, 191.0, and 175.2.
A) 216.60
B) 219.80
C) 210.40
D) 221.20
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