Click Here for a Data Scientist: Big Data, Predictive Analytics, and Theory Development in the Era of a Maker Movement Supply Chain. Issue 4 (December 2013)

• Record Type: Journal Article
• Authors: Waller, Matthew A.; Fawcett, Stanley E.
• Is Part Of: Journal of business logistics. Volume 34:Issue 4(2013:Dec.)
• Page Start: 249
• Page End: 252
• Publication Date: 2013-12
• Subjects: Business logistics -- Periodicals; Business logistics -- United States -- Periodicals; 658.5005
• DOI: 10.1111/jbl.12024
• Languages: English
• ISSNs: 0735-3766

Abstract

<abstract abstract-type="main" id="jbl12024-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Predictive analytics is impacting many diverse areas, ranging from baseball and epidemiology to forecasting and customer relationship management. Manufacturers, retailers, software companies, and consultants are creatively discovering new applications of big data using predictive analytics in supply chain management and logistics. In practice, predictive analytics is generally atheoretical; however, we develop a 2 × 2 model to explain the role of predictive analytics in the theory development process. This 2 × 2 model shows that in our discipline we have traditionally taken one path to theory development, but that predictive analytics can be a salient component of a comprehensive theory development process. The model points to a number of research questions that need to be addressed by our research community. These questions are not just highly relevant to the academic community but also in urgent need of answers to help practitioners execute the right strategies with greater precision and efficiency. We also discuss how one disruptive trend, the maker movement, changes the nature of who the producers are in the supply chain, making big data even more valuable. As we engage in higher levels of dialogue we will be able to make meaningful progress addressing these vital research topics.</p> </abstract>