Data Science, Predictive Analytics, and Big Data: A Revolution that Will Transform Supply Chain Design and Management
Journal of Business Logistics, Vol. 34, Forthcoming
15 Pages Posted: 16 Jun 2013 Last revised: 9 Jul 2013
Date Written: 2013
We illuminate the myriad of opportunities for research where supply chain management 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 a SCM data scientist. Such knowledge is crucial to developing future supply chain leaders. We propose definitions of data science and predictive analytics as applied to supply chain management. 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 supply chain management and DPB.
Keywords: Data science, predictive analytics, big data, logistics, supply chain management, design, collaboration, integration, education
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