Exam 15: Forecasting

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Because of the development of advanced forecasting models,managers no longer track forecast error.

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The demand behaviour for skis is considered cyclical.

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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 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

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A correlation coefficient is a measure of the strength of the linear relationship between an independent and a dependent variable.

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The ___ forecast method consists of an exponentially smoothed forecast with a trend adjustment factor added to it.

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Forecast bias is measured by the per-period average of the sum of the forecast errors.

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Which of the following is a reason why a forecast can go "out of control?"

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The type of forecasting method selected depends on time frame,demand behaviour,and causes of behaviour.

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Short-mid-range forecasts tend to use quantitative models that forecast demand based on historical demand.

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Given the demand and forecast values below,the naïve forecast for September is Given the demand and forecast values below,the naïve forecast for September is

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In today's competitive environment,effective supply chain management requires accurate demand forecasts.

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The sum of the weights in a weighted moving average forecast

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Linear regression relates two variables using a linear model.

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Many companies are shifting from long-term to short-term forecast for strategic planning.

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Correlation is a measure of the strength of the

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A tracking signal is computed by

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The Delphi method generates forecasts based on informed judgments and opinions from knowledgeable individuals.

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Which of the following is not a type of predictable demand behaviour?

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Sharing demand forecasts with supply chain members has resulted in an increased bullwhip effect.

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Time series methods use historical data to predict future demand.

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