Forecasting workload for Defense Logistics Agency distribution
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Authors
Chonko, Aaron W.
Heiliger, Padraic T.
Rudge, Travis W.
Subjects
Defense Logistics Agency
workload forecasting
distribution forecasting
forecasting
ARIMA
double exponential smoothing
predicting workload
staffing
DLA
distribution.
workload forecasting
distribution forecasting
forecasting
ARIMA
double exponential smoothing
predicting workload
staffing
DLA
distribution.
Advisors
Doerr, Kenneth
Eger, Robert
Date of Issue
2014-12
Date
Dec-14
Publisher
Monterey, California: Naval Postgraduate School
Language
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.
Type
Thesis
Description
MBA Professional Report
Series/Report No
Department
Organization
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NPS Report Number
Sponsors
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Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This 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.