Week 2 Warm Up

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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Organizations that are capable of responding quickly to changing requirements can use a shorter forecast horizon and therefore benefit from more accurate forecasts.

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

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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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The shorter the forecast horizon, the more accurately the forecasts tend to track what actually happens.

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Forecasts based on an average tend to exhibit less variability than the original data.

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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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An advantage of a weighted moving average is that recent actual results can be given more importance than what occurred a while ago.

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When choosing a forecasting technique, a critical trade-off that must be considered is that between:

• cost and accuracy.
• time series and associative.
• seasonality and cyclicality.
• length and duration.
• simplicity and complexity.