Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Smith, Brian

Committee Member

Tian, Wenmeng

Committee Member

Ma, Junfeng

Date of Degree

8-7-2020

Document Type

Graduate Thesis - Open Access

Major

Industrial Engineering

Degree Name

Master of Science

College

James Worth Bagley College of Engineering

Department

Department of Industrial and Systems Engineering

Abstract

This research builds off previous research conducted in 2009 which included a survey of healthcare professionals assessing their organization’s levels of supply chain maturity (SCM) and data standard readiness (DSR) from 1 to 5 [Smith, 2011]. With the survey data, Smith developed a 0-1 quadratic program to conserve the maximum amount of survey data while removing non-responses. This research uses the quadratic program as well as other machine learning algorithms and analysis methods to investigate what factors contribute to an organization’s SCM and DSR levels the most. No specific factors were found; however, different levels of prediction accuracy were achieved across the five different subsets and algorithms. he best accuracy prediction SCM model was linear discriminant analysis on the Reduced subset at 50.84% while the highest prediction accuracy for DSR was stepwise regression on the PCA subset at 45.00%. Most misclassifications found in this study were minimal.

URI

https://hdl.handle.net/11668/18455

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