Date of Award

3-23-2017

Document Type

Thesis

Degree Name

Master of Science in Logistics and Supply Chain Management

Department

Department of Operational Sciences

First Advisor

Michael P. Kretser, PhD.

Abstract

United States Air Force (USAF) aircraft parts forecasting techniques have remained archaic despite new advancements in data analysis. This approach resulted in a 57% accuracy rate in fiscal year 2016 for USAF managed items. Those errors combine for $5.5 billion worth of inventory that could have been spent on other critical spare parts. This research effort explores advancements in condition based maintenance (CBM) and its application in the realm of forecasting. It then evaluates the applicability of CBM forecast methods within current USAF data structures. This study found large gaps in data availability that would be necessary in a robust CBM system. The Physics-Based Model was used to demonstrate a CBM like forecasting approach on B-1 spare parts, and forecast error results were compared to USAF status quo techniques. Results showed the Physics-Based Model underperformed USAF methods overall, however it outperformed USAF methods when forecasting parts with a smooth or lumpy demand pattern. Finally, it was determined that the Physics-Based Model could reduce forecasting error by 2.46% or $12.6 million worth of parts in those categories alone for the B-1 aircraft.

AFIT Designator

AFIT-ENS-MS-17-123

DTIC Accession Number

Pending

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