Deck 12: Forecasting
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Deck 12: Forecasting
1
Long-range qualitative forecasts are used to determine future demand for new products, markets, and customers.
True
2
One way to deal with the bullwhip effect is to develop and share the forecasts with other supply chain members.
True
3
Explain the forecasting process, including the factors to be considered when making forecasting decisions.
The type of forecasting method to use depends on several factors, including the time frame of the forecast (i.e., how far into the future is being forecast) and demand behaviour (which may or may not follow a pattern). Three basic types of forecasting methods are: time series methods, regression methods, and qualitative methods. Forecasting is not just identifying what demand will be in the future. It is a continuing process that requires constant monitoring and adjustment. See Figure 12.3 for steps in the forecasting process.
4
Forecasting customer demand is rarely a key to providing good quality service.
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5
The type of forecasting method selected depends on time frame, demand behaviour, and causes of behaviour.
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6
The Delphi method generates forecasts based on informed judgments and opinions from knowledgeable individuals.
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7
Describe the uses of data mining and its associated tools.
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8
A gradual, long-term up or down movement of demand is referred to as a trend.
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9
Sharing demand forecasts with supply chain members has resulted in an increased bullwhip effect.
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10
Apply the different measures of forecast error.
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11
The trend toward continuous replenishment in supply chain design has shifted the need for accurate forecasts from short-term to long-term.
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12
A seasonal pattern is an oscillating movement in demand that occurs periodically over the short-run and is repetitive.
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13
Qualitative forecasts use mathematical techniques and statistical formulas.
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14
In today's competitive environment, effective supply chain management requires accurate demand forecasts.
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15
Use linear and basic multiple regression as a forecasting tool.
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16
Because of globalization of markets, managers are finding it increasingly more difficult to create accurate demand forecasts.
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17
Because of advances in technology, many service industries no longer require accurate forecasts to provide high quality service.
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18
Forecast demand with various time series models, such as exponential smoothing, and trend and seasonal adjustments, as well as use Excel.
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19
Forecasts based on mathematical formulas are referred to as qualitative forecasts.
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20
Discuss the importance of accurate forecasting in supply chain management, quality management, and strategic planning.
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21
The demand behaviour for skis is considered cyclical.
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22
Because of the development of advanced forecasting models, managers no longer track forecast error.
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23
Time series methods use historical data to predict future demand.
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24
Because of the heightened competition resulting from globalization, most companies find little strategic value in long-range forecasts.
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25
The type of forecasting method used depends entirely whether the supply chain is continuous replenishment or not.
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26
Because of ease of use and simplicity, exponential smoothing is preferred over smoothing average.
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27
One reason time series methods are popular for forecasting is that they are relatively easy to use and understand.
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28
Exponential smoothing is an averaging method for forecasting that reacts more strongly to recent changes in demand.
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29
Movements in demand that do not follow a given pattern are referred to as random variations.
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30
Time series methods assume that demand patterns in the past are a good predictor of demand in the future.
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31
The moving average method is used for creating forecasts when there is no variation in demand.
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32
A gradual, long-term up or down movement of demand is called a trend.
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33
The larger the mean absolute deviation (MAD), the more accurate the forecast.
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34
The most common type of forecasting method for long-term strategic planning is based on quantitative modelling.
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35
Short-mid-range forecasts tend to use quantitative models that forecast demand based on historical demand.
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36
Continuous replenishment systems rely heavily on extremely accurate long-term forecasts.
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37
The average, absolute difference between the forecast and demand is a popular measure of forecast error.
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38
Many companies are shifting from long-term to short-term forecast for strategic planning.
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39
The long-term strategic planning process is dependent upon qualitative forecasting methods.
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40
Forecast bias is measured by the per-period average of the sum of the forecast errors.
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41
Regression is used for forecasting when there is a relationship between the dependent variable, demand, and one or more independent (explanatory) variables.
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42
A linear regression model that relates demand to time is known as a linear trend line.
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43
A qualitative procedure used to develop a consensus forecast is known as
A) exponential smoothing.
B) regression methods.
C) the Delphi technique.
D) naïve forecasting.
A) exponential smoothing.
B) regression methods.
C) the Delphi technique.
D) naïve forecasting.
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44
Which of the following is not a type of predictable demand behaviour?
A) trend
B) random variation
C) cycle
D) seasonal pattern
A) trend
B) random variation
C) cycle
D) seasonal pattern
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45
Forecast methods based on judgment, opinion, past experiences, or best guesses are known as ___ methods.
A) quantitative
B) qualitative
C) time series
D) regression
A) quantitative
B) qualitative
C) time series
D) regression
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46
A long-range forecast would normally not be used to
A) design the supply chain.
B) implement strategic programs.
C) determine production schedules.
D) plan new products for changing markets
A) design the supply chain.
B) implement strategic programs.
C) determine production schedules.
D) plan new products for changing markets
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47
Multiple regression analysis can be used to relate demand to two or more dependent variables.
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48
The ___ method uses demand in the first period to forecast demand in the next period.
A) naïve
B) moving average
C) exponential smoothing
D) linear trend
A) naïve
B) moving average
C) exponential smoothing
D) linear trend
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49
The smoothing constant, α, in the exponential smoothing forecast
A) must always be a value greater than 1.0.
B) must always be a value less than 0.10.
C) must be a value between 0.0 and 1.0.
D) should be equal to the time frame for the forecast.
A) must always be a value greater than 1.0.
B) must always be a value less than 0.10.
C) must be a value between 0.0 and 1.0.
D) should be equal to the time frame for the forecast.
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50
A correlation coefficient is a measure of the strength of the linear relationship between an independent and a dependent variable.
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51
Linear regression relates two variables using a linear model.
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52
The closer the smoothing constant, α, is to 1.0,
A) the greater the reaction to the most recent demand.
B) the greater the dampening, or smoothing, effect.
C) the more accurate the forecast.
D) the less accurate the forecast.
A) the greater the reaction to the most recent demand.
B) the greater the dampening, or smoothing, effect.
C) the more accurate the forecast.
D) the less accurate the forecast.
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53
A ___ is an up-and-down movement in demand that repeats itself over a lengthy time period of more than a year.
A) trend
B) seasonal pattern
C) random variation
D) cycle
A) trend
B) seasonal pattern
C) random variation
D) cycle
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54
A forecast where the current period's demand is used as the next period's forecast is known as a
A) moving average forecast.
B) naïve forecast.
C) weighted moving average forecast.
D) Delphi forecast.
A) moving average forecast.
B) naïve forecast.
C) weighted moving average forecast.
D) Delphi forecast.
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55
The exponential smoothing model produces a naïve forecast when the smoothing constant, α, is equal to
A) 0.00.
B) 1.00.
C) 0.50.
D) 2.00
A) 0.00.
B) 1.00.
C) 0.50.
D) 2.00
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56
An exponential smoothing forecasting technique requires all of the following except
A) the forecast for the current period.
B) the actual demand for the current period.
C) a smoothing constant.
D) large amounts of historical demand data.
A) the forecast for the current period.
B) the actual demand for the current period.
C) a smoothing constant.
D) large amounts of historical demand data.
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57
Correlation in linear regression is a measure of the strength of the relationship between the dependent variable, demand, and an independent (explanatory) variable.
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58
Regression forecasting methods relate ___ to other factors that cause demand behaviour.
A) supply
B) demand
C) time
D) money
A) supply
B) demand
C) time
D) money
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59
The ___ forecast method consists of an exponentially smoothed forecast with a trend adjustment factor added to it.
A) exponentially smoothed
B) adjusted exponentially smoothed
C) time series
D) moving average
A) exponentially smoothed
B) adjusted exponentially smoothed
C) time series
D) moving average
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60
The sum of the weights in a weighted moving average forecast
A) must equal the number of periods being averaged.
B) must equal 1.00.
C) must be less than 1.00.
D) must be greater than 1.00.
A) must equal the number of periods being averaged.
B) must equal 1.00.
C) must be less than 1.00.
D) must be greater than 1.00.
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61
Given the following demand data for the past five months, the three-period moving average forecast for June is 
A) 103.33.
B) 99.00.
C) 95.00.
D) 92.50.

A) 103.33.
B) 99.00.
C) 95.00.
D) 92.50.
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62
Selecting the type of forecasting method to use depends on
A) the time frame of the forecast.
B) the behaviour of demand and demand patterns.
C) the causes of demand behaviour.
D) all of the above.
A) the time frame of the forecast.
B) the behaviour of demand and demand patterns.
C) the causes of demand behaviour.
D) all of the above.
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63
For the demand values and the January forecast shown in the table below, the exponential smoothing forecast for March using α = 0.40 is 
A) 1,200.
B) 1,220.
C) 1,222.
D) 1,225.

A) 1,200.
B) 1,220.
C) 1,222.
D) 1,225.
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64
The demand and forecast values are shown in the table below:
The exponential smoothing forecast for November using α = 0.35 is
A) 552.45.
B) 553.50.
C) 554.55.
D) 557.50.

A) 552.45.
B) 553.50.
C) 554.55.
D) 557.50.
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65
A forecasting model has produced the following forecasts:
The mean absolute percentage deviation (MAPD) for the end of May is
A) 0.0250.
B) 0.0583.
C) 0.5830.
D) 0.6670.

A) 0.0250.
B) 0.0583.
C) 0.5830.
D) 0.6670.
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66
If the forecast for July was 3300 and the actual demand for July was 3250, then the exponential smoothing forecast for August using α = 0.20 is
A) 3300.
B) 3290.
C) 3275.
D) 3250.
A) 3300.
B) 3290.
C) 3275.
D) 3250.
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67
A forecasting model has produced the following forecasts:
The forecast error for February is
A) 10.
B) -10.
C) -15.
D) -5

A) 10.
B) -10.
C) -15.
D) -5
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68
A forecasting model has produced the following forecasts:
The mean absolute deviation (MAD) for the end of May is
A) 7.0.
B) 7.5.
C) 10.0
D) 3.0

A) 7.0.
B) 7.5.
C) 10.0
D) 3.0
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69
Which of the following statements concerning average error is true?
A) A positive value indicates high bias, and a negative value indicates low bias.
B) A positive value indicates zero bias, and a negative value indicates low bias.
C) A negative value indicates zero bias, and a negative value indicates high bias.
D) A positive value indicates low bias, and a negative value indicates high bias.
A) A positive value indicates high bias, and a negative value indicates low bias.
B) A positive value indicates zero bias, and a negative value indicates low bias.
C) A negative value indicates zero bias, and a negative value indicates high bias.
D) A positive value indicates low bias, and a negative value indicates high bias.
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70
A forecasting model has produced the following forecasts:
At the end of May the tracking signal would be
A) 0.000.
B) 0.667.
C) 1.333.
D) 2.143.

A) 0.000.
B) 0.667.
C) 1.333.
D) 2.143.
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71
A large positive cumulative error indicates that the forecast is probably
A) higher than the actual demand.
B) lower than the actual demand.
C) unbiased.
D) biased.
A) higher than the actual demand.
B) lower than the actual demand.
C) unbiased.
D) biased.
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72
Given the following demand data for the past five months, the four-period moving average forecast for June is 
A) 96.25.
B) 99.00.
C) 110.00.
D) 93.75.

A) 96.25.
B) 99.00.
C) 110.00.
D) 93.75.
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73
For the demand values and the January forecast shown in the table below, the exponential smoothing forecast for March using α = 0.30 is 
A) 489.
B) 486.
C) 483.
D) 480.

A) 489.
B) 486.
C) 483.
D) 480.
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74
A forecasting model has produced the following forecasts:
At the end of May the average error would be
A) 7.
B) 5.
C) 3.
D) 1.

A) 7.
B) 5.
C) 3.
D) 1.
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75
The per period average of cumulative error is called
A) cumulative forecast variation.
B) absolute error.
C) average error.
D) noise.
A) cumulative forecast variation.
B) absolute error.
C) average error.
D) noise.
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76
The mean absolute percentage deviation (MAPD) measures the absolute error as a percentage of
A) all errors.
B) per period demand.
C) total demand.
D) the average error.
A) all errors.
B) per period demand.
C) total demand.
D) the average error.
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77
Given the demand and forecast values below, the naïve forecast for September is 
A) 100.6.
B) 99.0.
C) 102.0.
D) cannot be determined.

A) 100.6.
B) 99.0.
C) 102.0.
D) cannot be determined.
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78
The weighted moving average forecast for the fifth period with weights of 0.15 for period 1, 0.20 for period 2, 0.25 for period 3, and 0.40 for period 4, using the demand data shown below is 
A) 3,760.
B) 3,700.
C) 3,650.
D) 3,325.

A) 3,760.
B) 3,700.
C) 3,650.
D) 3,325.
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79
A company wants to product a weighted moving average forecast for April with the weights 0.40, 0.35, and 0.25 assigned to March, February, and January, respectively. If the company had demands of 5,000 in January, 4,750 in February, and 5,200 in March, then April's forecast is
A) 4,983.33.
B) 4,992.50.
C) 4,962.50.
D) 5,000.00.
A) 4,983.33.
B) 4,992.50.
C) 4,962.50.
D) 5,000.00.
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80
Given the demand and forecast values shown in the table below:
The three-period moving average forecast for November is
A) 516.
B) 528.
C) 524.
D) 515.

A) 516.
B) 528.
C) 524.
D) 515.
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