chapter 12
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
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
Which of the following can be used to monitor a forecast to see if it is biased high or low? a. a tracking signal b. the mean absolute deviation (MAD) c. the mean absolute percentage deviation (MAPD) d. a linear trend line model
a
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.
b
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.
b
Forecast methods based on judgment, opinion, past experiences, or best guesses are known as ___________ methods. a. quantitative b. qualitative c. time series d. regression
b
If forecast errors are normally distributed then a. 1 MAD = 1σ b. 1 MAD ≈ 0.8 σ c. 0.8 MAD ≈ 1σ d. 1 MAD ≈ 1.96 σ
b
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.
b
Regression forecasting methods relate _________to other factors that cause demand behavior. a. Supply b. Demand c. Time d. Money e. Efficiency
b
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
b
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
b
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.
b
Which of the following is not a type of predictable demand behavior? a. trend b. random variation c. cycle d. seasonal pattern
b
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
c
A mathematical technique for forecasting that relates the dependent variable to an independent variable is a. correlation analysis. b. exponential smoothing. c. linear regression. d. weighted moving average.
c
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.
c
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.
c
The per period average of cumulative error is called a. cumulative forecast variation. b. absolute error. c. average error. d. noise.
c
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.
c
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
d
A tracking signal is computed by a. multiplying the cumulative error by MAD b. multiplying the absolute error by MAD c. dividing MAD by the cumulative absolute error d. dividing the cumulative error by MAD
d
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.
d
Correlation is a measure of the strength of the a. nonlinear relationship between two dependent variables. b. nonlinear relationship between a dependent and independent variable. c. linear relationship between two dependent variables. d. linear relationship between a dependent and independent variable.
d
Selecting the type of forecasting method to use depends on a. the time frame of the forecast. b. the behavior of demand and demand patterns. c. the causes of demand behavior. d. all of the above.
d
Which of the following is a reason why a forecast can go "out of control?" a. a change in trend b. an irregular variation such as unseasonable weather c. a promotional campaign d. all of the above
d
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
d
Because of advances in technology, many service industries no longer require accurate forecasts to provide high quality service.
f
Because of the development of advanced forecasting models managers no longer track forecast error.
f
Many companies are shifting from long-term to short-term forecast for strategic planning.
f
The trend toward continuous replenishment in supply chain design has shifted the need for accurate forecasts from short-term to long-term.
f
t or f Continuous replenishment systems rely heavily on extremely accurate long-term forecasts.
f
t or f Forecasting customer demand is rarely a key to providing good quality service.
f
t or f Forecasts based on mathematical formulas are referred to as qualitative forecasts
f
t or f The type of forecasting method used depends entirely whether the supply chain is continuous replenishment or not.
f
Sharing demand forecasts with supply chain members has resulted in an increased bullwhip effect.
f - decreased
The most common type of forecasting method for long-term strategic planning is based on quantitative modeling
f - qualitative
Qualitative forecasts use mathematical techniques and statistical formulas.
f - quantitative
Multiple regression analysis can be used to relate demand to two or more dependent variables.
f - relationship of demand to two or more independent variables
The demand behavior for skis is considered cyclical.
f - seasonal
Because of ease of use and simplicity, exponential smoothing is preferred over smoothing average.
f - smoothing average is preferred over exponential smoothing
The larger the mean absolute deviation (MAD) the more accurate the forecast.
f - the smaller the MAD the more accurate the forecast
The moving average method is used for creating forecasts when there is no variation in demand.
f - there is variation in demand
A correlation coefficient is a measure of the strength of the linear relationship between an independent and a dependent variable.
t
A gradual, long-term up or down movement of demand is called a trend.
t
A linear regression model that relates demand to time is known as a linear trend line.
t
Because of globalization of markets, managers are finding it increasingly more difficult to create accurate demand forecasts.
t
Because of the heightened competition resulting from globalization most companies find little strategic value in long-range forecast.
t
Correlation in linear regression is a measure of the strength of the relationship between the dependent variable, demand, and an independent (explanatory) variable.
t
Exponential smoothing is an averaging method for forecasting that reacts more strongly to recent changes in demand.
t
Forecast bias is measured by the per-period average of the sum of the forecast errors.
t
In today competitive environment, effective supply chain management requires accurate demand forecasts.
t
Linear regression relates two variables using a linear model.
t
Long-range qualitative forecasts are used to determine future demand for new products, markets and customers.
t
Movements in demand that do not follow a given pattern are referred to as random variations.
t
One reason time series methods are popular for forecasting is that they are relatively easy to use and understand.
t
Regression is used for forecasting when there is a relationship between the dependent variable, demand, and one or more independent (explanatory) variables.
t
Short-midrange forecasts tend to use quantitative models that forecast demand based on historical demand.
t
The Delphi method generates forecasts based on informed judgments and opinions from knowledgeable individuals.
t
The average, absolute difference between the forecast and demand is a popular measure of forecast error.
t
The long-term strategic planning process is dependent upon qualitative forecasting methods.
t
The type of forecasting method selected depends on time frame, demand behavior and causes of behavior.
t
Time series methods assume that demand patterns in the past is a good predictor of demand in the future.
t
t or f A gradual, long-term up or down movement of demand is referred to as a trend.
t
t or f A seasonal pattern is an oscillating movement in demand that occurs periodically over the short-run and is repetitive.
t
t or f One way to deal with the bullwhip effect is to develop and share the forecasts with other supply chain members.
t
t or f Time series methods use historical data to predict future demand.
t
