Year: 2025
Type: editorial
Abstract: Empirically Grounding Analytics (EGA) in operations and supply chain management is a research area at the intersection of empirical and analytical studies.Spearman and Hopp (2021) identified it as an underserved research area with great opportunity for input.To clarify what EGA is, we use a quote provided in the JOM editorial on the subject (see de Treville et al. ( 2023)) as a definition: "an EGA paper combines mathematical, stochastic, and/or economic modeling with empirical data….Empirically grounding an analytic model creates knowledge by linking analytical insights to what has been observed using empirical methods (such as case studies, action research, field experiments, interviews, or analysis of secondary data) to establish a theoretically and empirically relevant question."De Treville et al. (2023) propose a framework for discussing EGA research approaches and assessing contributions, summarized in Figure 1 of their editorial.We will refer to this framework rather extensively in our discussion of work in this Special Issue.We provide a "deconstructed" version of this figure, with some added details, in Figure 1.Research in "empirical grounding" of analytical models can be conceptually viewed as offering two different ways to drive research and lead to impactful contributions.The "left side" approach has as its end goal to establish analytical models verifiably linked to data and observations reflecting the real operational setting.This approach contributes a "calibrated fit" of the model to the operational decision reality.It requires careful empirical justification of modeling assumptions and parameters.The calibration of model parameters involves collecting representative data from the realistic setting, with any remaining model assumptions and approximations well justified for the real situation.The expectation of these grounded models is a high quality of solutions for the approximated real decision problem.The "right side" approach pursues empirical assessment of model results, solution quality, and applicability of insights in addressing issues encountered in real practice.It carefully verifies that (a) an effective implementation of the model reasonably and accurately depicts the operational setting and decision situation; (b) the obtained solutions lead to improved performance; and (c) incorporating analytical insights and tools leads to improved managerial practice for this setting.In most cases, "left side" research leads to well-calibrated models with strong hints for improved solution quality and useful insights to be further tested in the real setting and actual practice."Right side" research carefully tests and confirms the wisdom of new insights and tools, leading to improved practice in the operational setting.However, such testing and analytical insights may reveal irregularities and complexities not effectively depicted in the models, thus driving the need for new "left side" research and subsequent "right side" research for further validation and testing.After a few such healthy iterations, we expect this EGA research paradigm to lead to the development of well-grounded knowledge and well-tested, impactful theories enhancing operations and supply chain management practice.This systematic iteration, "left side ⇌ right side," is what we refer to as the "close-the-loop" approach.In this editorial thought piece, we demonstrate how the above approaches ("left side", "right side", and "close-the-loop")
Source: Journal of Operations Management
DOI: 10.1002/joom.1373
Authors: Arnd Huchzermeier, Panos Kouvelis
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,520
OpenAlex ID: W4410137308