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Operations Management Study Set 5

Statistics

Quiz 3 :

Forecasting

Quiz 3 :

Forecasting

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Forecasts based on time-series (historical)data are referred to as associative forecasts.
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True False
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False

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A consumer survey is an easy and sure way to obtain accurate input from future customers since most people enjoy participating in surveys.
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True False
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False

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Forecasting techniques that are based on time-series data assume that future values of the series will duplicate past values.
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True False
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False

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The Delphi approach involves the use of a series of questionnaires to achieve a consensus forecast.
True False
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The naive forecast can serve as a quick and easy standard of comparison against which to judge the cost and accuracy of other techniques.
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Once accepted by managers, forecasts should be held firm regardless of new input since many plans have been made using the original forecast.
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When new products or services are introduced, focus forecasting models are an attractive option.
True False
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For new products in a strong growth mode, a low alpha will minimize forecast errors when using exponential smoothing techniques.
True False
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Organizations that are capable of responding quickly to changing requirements can use a shorter forecast horizon and therefore benefit from more accurate forecasts.
True False
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The purpose of the forecast should be established first so that the level of detail, amount of resources, and accuracy level can be understood.
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The shorter the forecast period, the more accurately the forecasts tend to track what actually happens.
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The naive forecast is limited in its application to series that reflect no trend or seasonality.
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Forecasts based on an average tend to exhibit less variability than the original data.
True False
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Trend-adjusted exponential smoothing uses double smoothing to add twice the forecast error to last period's actual demand.
True False
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Forecasts help managers both to plan the system itself and to provide valuable information for using the system.
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Exponential smoothing adds a percentage (called alpha)of the last period's forecast to estimate the next period's demand.
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The naive approach to forecasting requires a linear trend line.
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Time-series techniques involve the identification of explanatory variables that can be used to predict future demand.
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Forecasting techniques generally assume an existing causal system that will continue to exist in the future.
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Forecasts for groups of items tend to be less accurate than forecasts for individual items because forecasts for individual items don't include as many influencing factors.
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