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Data Science, Predictive Analytics, and Big Data in Supply Chain Management: Current State and Future Potential
Author(s) -
Schoenherr Tobias,
SpeierPero Cheri
Publication year - 2015
Publication title -
journal of business logistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.611
H-Index - 79
eISSN - 2158-1592
pISSN - 0735-3766
DOI - 10.1111/jbl.12082
Subject(s) - predictive analytics , big data , data science , analytics , computer science , scale (ratio) , field (mathematics) , data analysis , business analytics , curriculum , supply chain , knowledge management , business , marketing , psychology , data mining , pedagogy , physics , mathematics , business model , quantum mechanics , pure mathematics , business analysis
While data science, predictive analytics, and big data have been frequently used buzzwords, rigorous academic investigations into these areas are just emerging. In this forward thinking article, we discuss the results of a recent large‐scale survey on these topics among supply chain management ( SCM ) professionals, complemented with our experiences in developing, implementing, and administering one of the first master's degree programs in predictive analytics. As such, we effectively provide an assessment of the current state of the field via a large‐scale survey, and offer insight into its future potential via the discussion of how a research university is training next‐generation data scientists. Specifically, we report on the current use of predictive analytics in SCM and the underlying motivations, as well as perceived benefits and barriers. In addition, we highlight skills desired for successful data scientists, and provide illustrations of how predictive analytics can be implemented in the curriculum. Relying on one of the largest data sets of predictive analytics users in SCM collected to date and our experiences with one of the first master's degree programs in predictive analytics, it is our intent to provide a timely assessment of the field, illustrate its future potential, and motivate additional research and pedagogical advancements in this domain.