A data science and open source software approach to analytics for strategic sourcing. (October 2020) - Boehmke, Brad; Hazen, Benjamin; Boone, Christopher A.; Robinson, Jessica L.
Highlights: Demonstrates how data science and open source software can be exploited to meet growing demand of data-driven decisions across organizational processes. Application of the presented data science approach proved simpler and faster; reducing required sourcing assessment time from 30 to 90 days to less than an hour. Demonstrates the efficacy of a theory driven data science method in a practical setting and then makes the tool available to other users via open source software. Abstract: Data science has emerged as a significant capability upon which firms compete. Although many data scientists and the high-performing companies that employ them seem to have developed robust methods to employ data sciences practices to achieve competitive advantages, there have been few attempts at defining and explaining how and why data science helps firms to achieve desired outcomes. In this paper, we describe how data science, which combines computer programming, domain knowledge, and analytic skillsets to scientifically extract insights from data, can be used to help meet the growing demand of analytic needs across an organization's value chain. This is done through the illustration of an applied data science initiative to a strategic sourcing problem via the use of open-source technology. In doing so, we contribute to the growing data science literature by demonstrating the application of unique data science capabilities. Moreover, the paper provides a tutorial on how to use a specific R package along with an actual case in which that package use used.