Date of Award

3-2025

Document Type

Thesis

Degree Name

Master of Science in Logistics and Supply Chain Management

Department

Department of Operational Sciences

First Advisor

Adam D. Reiman, PhD

Abstract

Recent advancements in 3D cargo scanning and machine learning offer solutions to improve cargo processing reliability. This study enhances a 3D cargo scanning system by addressing software crashes, connectivity failures, and image accuracy issues limiting its operational effectiveness. A systematic approach identified the primary causes of system failures. System updates were implemented, followed by 30 pre- and post-update trials to evaluate reliability improvements. Statistical t-tests showed a significant reduction in failures, although processing time slightly increased. Results indicated that targeted hardware and software updates enhanced cargo scanning efficiency and dependability. Future work will focus on refining sensor calibration, optimizing network stability, and improving overall system performance to ensure consistent cargo processing in operational environments.

AFIT Designator

AFIT-ENS-MS-25-M-175

Comments

An embargo was observed for this posting.

Approved for public release, Distribution Unlimited. PA Case Number 88ABW-2025-0276

Download

Share

COinS