Forecasting demand for airlift of sustainment cargo is an important function for logistics planners. For the civil reserve air fleet participants (CRAF), having a useful long-range forecast enables them to make business decisions to maximize profit and manage their fleets. Because the DoD relies on CRAF for much of its steady-state and wartime surge requirements, it is important for these civilian enablers to stay financially healthy in what has become a difficult market. In addition to the CRAF airlines, DoD schedulers also benefit from somewhat shorter-term forecasts of demand, as accurate forecasts help them allocate aircraft type and determine route frequency for airlift of sustainment cargo. Time series forecasting is a method applied in many circumstances, to include forecasting of aviation service demand. It does not require the modeler to attribute causation, but rather uses historical data of a univariate series to predict future values. This paper applies a variety of time-series techniques to historical sustainment demand data from Iraq and Afghanistan AORs, ultimately choosing a single technique to develop into a prediction model for future demand in each AOR. The resulting models show excellent goodness-of-fit values and are successfully validated against a reserved portion of data.


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    Title :

    Time Series Forecasting of Airlift Sustainment Cargo Demand


    Contributors:

    Publication date :

    2012


    Size :

    95 pages


    Type of media :

    Report


    Type of material :

    No indication


    Language :

    English