Accession Number : ADA619687
Title : Forecasting Workload for Defense Logistics Agency Distribution
Descriptive Note : MBA Professional rept.
Corporate Author : NAVAL POSTGRADUATE SCHOOL MONTEREY CA GRADUATE SCHOOL OF BUSINESS AND PUBLIC POLICY
Personal Author(s) : Chonko, Aaron W ; Heiliger, Padraic T ; Rudge, Travis W
Report Date : Dec 2014
Pagination or Media Count : 89
Abstract : The 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.
Descriptors : *LOGISTICS MANAGEMENT , *MILITARY PROCUREMENT , *PREDICTIONS , *WORKLOAD , COMPARISON , COST ANALYSIS , CYCLES , DISTRIBUTION , FORECASTING , HISTORY , MATHEMATICAL MODELS , RATES , REGRESSION ANALYSIS , SENSITIVITY , TIME SERIES ANALYSIS
Subject Categories : Statistics and Probability
Logistics, Military Facilities and Supplies
Distribution Statement : APPROVED FOR PUBLIC RELEASE