Big Data and Supply Chain Analytics: Implications for Teaching
Jason Woldt
University of Wisconsin-Whitewater, USA
Sameer Prasad
University of Wisconsin-Whitewater, USA
Ceyhun Ozgur
Valparaiso University, Valparaiso, USA
Volume 14: 2020, pp. 155-176; ABSTRACT
In environments characterized by turbulence, economic turmoil and uncertainty along with dramatic advancements in available technology and information availability, individuals with big data and supply chain analytics capability will continue to be in high demand. To reflect this new reality, universities will need to modify course content and pedagogy to meet industry needs. Based on industry data and an extensive literature review, we derive a course mapping model for big data and supply chain analytics. Specifically, we synthesize the literature on big data, structural elements of analytics, and outcomes. Using this model, we gathered 116 different supply chain analytics job descriptions posted on the world’s largest job database (indeed.com). Based on these findings, we offer recommendations to align academic programs with the needs of the industry.
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