Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management

We illuminate the myriad of opportunities for research where supply chain management (SCM) intersects with data science, predictive analytics, and big data, collectively referred to as DPB. We show that these terms are not only becoming popular but are also relevant to supply chain research and educ...

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Vydané v:Journal of business logistics Ročník 34; číslo 2; s. 77 - 84
Hlavní autori: Waller, Matthew A., Fawcett, Stanley E.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Hoboken Blackwell Publishing Ltd 01.06.2013
Predmet:
Big Data
collaboration
Data analysis
Data science
design
education
integration
logistics
Predictive analytics
Studies
Supply chain management
ISSN:0735-3766, 2158-1592
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Shrnutí:We illuminate the myriad of opportunities for research where supply chain management (SCM) intersects with data science, predictive analytics, and big data, collectively referred to as DPB. We show that these terms are not only becoming popular but are also relevant to supply chain research and education. Data science requires both domain knowledge and a broad set of quantitative skills, but there is a dearth of literature on the topic and many questions. We call for research on skills that are needed by SCM data scientists and discuss how such skills and domain knowledge affect the effectiveness of an SCM data scientist. Such knowledge is crucial to develop future supply chain leaders. We propose definitions of data science and predictive analytics as applied to SCM. We examine possible applications of DPB in practice and provide examples of research questions from these applications, as well as examples of research questions employing DPB that stem from management theories. Finally, we propose specific steps interested researchers can take to respond to our call for research on the intersection of SCM and DPB.
Bibliografia:ArticleID:JBL12010
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SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0735-3766
2158-1592
DOI:10.1111/jbl.12010