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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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