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dc.contributor.advisorDoerr, Kenneth
dc.contributor.advisorEger, Robert
dc.contributor.authorChonko, Aaron W.
dc.contributor.authorHeiliger, Padraic T.
dc.contributor.authorRudge, Travis W.
dc.dateDec-14
dc.date.accessioned2015-02-18T00:17:23Z
dc.date.available2015-02-18T00:17:23Z
dc.date.issued2014-12
dc.identifier.urihttp://hdl.handle.net/10945/44537
dc.descriptionMBA Professional Reporten_US
dc.description.abstractThe Defense Logistics Agency (DLA) predicts issue and receipt workload for its distribution agency in order to maintain adequate staffing levels and set proper rates for customers. Inaccurate forecasts lead to inaccurate staffing, subsequently leading to inaccurate pricing. DLA’s current regression forecasting model is no longer adequate for predicting future workload for DLA Distribution. We explore multiple forecasting techniques and provide a methodology for selecting a model that is a viable and accurate alternative for DLA. Our methodology encompasses best-fit determination, a comparison of predictability through back-casting, and a sensitivity exercise to see reaction and stability of our selected models’ predictions. Finally, we compare our best performing model with the current regression model to see what would have been reported if our model had been used instead of the current model for recent Program Budget Review (PBR) cycles. Our results suggest that an auto-regressive integrated moving average (ARIMA) model used with critical assessment and managerial judgment offers a viable alternative to the current model for predicting distribution workload.en_US
dc.description.urihttp://archive.org/details/forecastingworkl1094544537
dc.publisherMonterey, California: Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleForecasting workload for Defense Logistics Agency distributionen_US
dc.typeThesisen_US
dc.subject.authorDefense Logistics Agencyen_US
dc.subject.authorworkload forecastingen_US
dc.subject.authordistribution forecastingen_US
dc.subject.authorforecastingen_US
dc.subject.authorARIMAen_US
dc.subject.authordouble exponential smoothingen_US
dc.subject.authorpredicting workloaden_US
dc.subject.authorstaffingen_US
dc.subject.authorDLAen_US
dc.subject.authordistribution.en_US
dc.description.serviceCaptain, United States Armyen_US
dc.description.serviceMajor, United States Armyen_US
etd.thesisdegree.nameMaster of Business Administrationen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineBusiness Administrationen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.


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