Theses and Dissertations

Date of Award

8-1-2024

Document Type

Thesis

Degree Name

Master of Science in Engineering (MSE)

Department

Manufacturing Engineering

First Advisor

Hiram Moya

Second Advisor

Douglas H. Timmer

Third Advisor

Benjamin Peters

Abstract

This thesis demonstrates the critical role of decision support tools (DSTs) in both small business and large government operations, enhancing decision-making, operational efficiency, and economic benefits. By implementing Excel-based systems for small businesses and discrete event modelling for large enterprises, this research highlights the importance of adaptable and scalable solutions across various sectors. According to the U.S. Small Business Association, small businesses with 500 or fewer employees make up 99.9% of all U.S. businesses. However, 65% of new businesses fail within the first ten years, according to the Bureau of Labor Statistics. This case study focuses on the used vehicle market, in which small businesses struggle with accurate information tracking due to inadequate data-entry systems many lack proper data management, resulting in costly errors. This study aims to develop a simple, effective information tracking system using Excel, leveraging its widespread use in small businesses. My experience as an MSIPP fellow at the Savannah River National Laboratory with the Department of Energy (DOE) allowed me to address similar issues on a larger scale. Using the ExtendSim software, a robust discrete event simulation model to manage nuclear waste disposition effectively was developed. The ongoing challenge of managing nuclear waste at DOE facilities requires dynamic modelling solutions to make informed decisions on waste disposal and technological options. This research focuses on developing a robust and adaptable discrete event model using ExtendSim, targeting the dispositioning of transuranic waste at the Savannah River National Laboratory (SRNL) to the Waste Isolation Pilot Plant (WIPP). This model can be expanded to represent waste processes at other DOE facilities, providing insights into resource allocation and waste processing to stabilize productivity and reduce waste backlog. The findings can guide decision-makers at national laboratories and research entities engaged in nuclear operations. Whether for small businesses or a large nuclear waste facility, DSTs are invaluable for informed decision-making.

Comments

Copyright 2024 Alexis Andaverde. https://proquest.com/docview/3116184998

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