ORCID Identifier(s)

0000-0002-6337-2755

Graduation Semester and Year

2015

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Industrial Engineering

Department

Industrial and Manufacturing Systems Engineering

First Advisor

John Priest

Abstract

The need to determine knowledge from increasing amounts of information and raw data is a current and ongoing goal [1]. As technology continues to advance in the ability to collect and save more and more data, companies today must assimilate and understand this information as a valuable resource [2]. Companies in marketing and sales have found the use of a data warehouse and automated data analysis to be valuable resources [2]. This automated analysis of data has also been implemented in disciplines including scientific and medical research [2]. However, there continue to be other lower tech industrial areas of activity such as mineral extraction that have the capacity to generate and save large amounts of data that do not use automated knowledge discovery. Wyoming-based mineral extraction companies are collecting and storing an increasing number of data points. While the data and information exists and is available, there may be lost opportunity for insight into company activities. This may result in the loss of valuable improved efficiency. This research project provides detailed insights for an accepted comprehensive implementation process to explore automated data analysis in low-tech mineral extraction companies without previous experience using automated data analysis tools.

Keywords

Implementation process, Data analysis

Disciplines

Engineering | Operations Research, Systems Engineering and Industrial Engineering

Comments

Degree granted by The University of Texas at Arlington

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