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Data Analytics in an Industrial and Systems Engineering Curriculum

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Conference

2022 ASEE Annual Conference & Exposition

Location

Minneapolis, MN

Publication Date

August 23, 2022

Start Date

June 26, 2022

End Date

June 29, 2022

Conference Session

Industrial Engineering Division Technical Session 1

Page Count

11

Permanent URL

https://peer.asee.org/40376

Download Count

69

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Paper Authors

biography

Kathryn Abel Stevens Institute of Technology (School of Engineering and Science)

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Kathryn Abel is the Director of the Undergraduate Engineering Management (EM) and the Industrial and Systems Engineering (ISE) Programs at Stevens Institute of Technology in the School of Systems and Enterprises. She holds a Ph.D. in Technology Management and Applied Psychology. She is a Fellow in the American Society for Engineering Management. She has held several professional service positions including President (2006) and Program Chair (2005) of the Engineering Management Division of the American Society for Engineering Education and President (2007) and Vice President (2005) of Engineering Management Honor Society (Epsilon Mu Eta). Abel has been published several times including chapters in the books Eshbach's Handbook of Engineering Fundamentals and Engineering Economic Analysis by Newnan, et. al.; in journals such as the Engineering Management Journal and the Journal of Engineering Education; and several conference proceedings. She has taught courses in Total Quality Management, Engineering Economics, Logistics and Supply Chain Management, Entrepreneurial Analysis of Engineering Design, Statistics for Engineering Managers, Management of Engineering and Technology, and Senior Design. Her research areas include knowledge engineering, as well as knowledge and information management. She is a member of the Board of Advisors at West Point for the Department of Systems Engineering. She is also a member of several professional societies including ASEE, ASEM, ASME, and EMH.

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Abstract

The last two decades have seen a mass digitalization of manufacturing. Sensors and wireless monitoring within this digitation provide opportunities for vast collections of data. This data can be collected from various areas throughout the production cycle: design, assembly, quality control, maintenance, etc. Value can be extracted from this data which can benefit company’s manufacturing processes. Therefore, the need exists for analytical knowledge to explore these data sets to uncover information with the goal of improving efficiencies. Industrial engineers already have a strong statistics background as well as linear algebra. Some of the areas that traditional IE programs may be lacking are unstructured data analysis, advanced machine learning techniques, and programming skills. In response to this burgeoning need, Stevens Institute of Technology created a brand new Industrial and Systems Engineering program heavy in data analytics. The first students graduated in May 2020. A paper addressing the initiation of this topic was previously brought before ASEE IED Division in 2018 and this article is meant as a follow up. One purpose of the paper is to demonstrate the final curriculum outcome for the program. However, the global goal of this paper is to demonstrate the growing need for the topic of data analysis in Industrial Engineering curriculums.

Abel, K. (2022, August), Data Analytics in an Industrial and Systems Engineering Curriculum Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. https://peer.asee.org/40376

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