TCU Neeley Research Highlight – Professor of Operations Management Tyson Browning partnered with other experts to develop research insights for industry and academic leaders.
March 31, 2023
TCU Neeley school of Business professor, Tyson Browning partnered with Suzanne de Treville, and Rogelio Oliva, on empirically grounding analytics (EGA) research in operations management. Their research focuses on validating and improving mathematical models by testing them against real-world data. This approach aims to bridge gaps between theoretical research and practical application, enhancing the accuracy and usefulness of analytical tools in understanding complex systems. (Journal of Operations Management, 2023).
Abstract
Empirically grounding analytics (EGA) is an area of research that emerges at the intersection of empirical and analytical research. By “empirically grounding,” we mean both the empirical justification of model assumptions and parameters and the empirical assessment of model results and insights. EGA is a critical but largely missing aspect of operations management (OM) research. Spearman and Hopp (2021, p. 805) stated that “since empirical testing and refutation of operations models is not an accepted practice in the IE/OM research community, we are unlikely to leverage these to their full potential.” They named several “examples of overly simplistic building blocks leading to questionable representations of complex systems” (p. 805) and suggested that research using analytical tools like closed queuing network models and the Poisson model of demand processes could incorporate empirical experiments to improve understanding of where they do and do not fit reality, highlighting “the importance of making empirical tests of modeling assumptions, both to ensure the validity of the model for its proposed purpose and to identify opportunities for improving or extending our modeling capabilities. The fact that very few IE/OM papers make such empirical tests is an obstacle to progress in our field” (p. 808). They concluded that “Editors should push authors to compare mathematical models with empirical data. Showing that a result holds in one case but not another adds nuance and practicality to research results. It also provides stimulus for research progress” (p. 814). These arguments remind of Little’s (1970) observation that many potentially useful analytical models are not widely adopted in practice. Thus, EGA research can help to close two major gaps between (1) the empirical and analytical subdivisions in the OM field and (2) scholarly output and practical relevance.
