There is still considerable doubt and even anxiety among simulation modelers as to what the methodologically correct guidelines or procedures for validating simulation models should be. Epistemically, the approaches one finds in the simulation literature run the gamut from objectivist to relativist with shades in between. At present in the philosophy of science, there appears to be a convergence toward a nonalgorithmic but discursive and nonrelativistic view of the argumentation involved in warranting scientific theorizing. The present paper attempts to give a description of the various philosophical positions as well as to summarize their problems and the kinds of evidentiary arguments they would each allow in arriving at defensible simulation models. From the debate, we attempt to set out a perspective that frees the practioner to pursue a varied set of approaches to validation with a diminished burden of methodological anxiety. Reciprocally this perspective does not let the modeler off of the hook but rather converts the validation problem into an ethical problem in which the practitioner must responsibly and professionally argue for the warrant of the model.Simulation, Validation, Philosophy of Science, Hermeneutics

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Organizational knowledge is a critical source of competitive advantage for professional service firms. Learning from experience and sustaining past knowledge are critical to the success of such knowledge-driven firms. We use learning curve theory to evaluate learning and depreciation in professional services. Our results, based on seven years of project data collected from an architectural engineering (A/E) firm, show that (a) professional services exhibit learning curves, (b) there is virtually no depreciation of knowledge and, (c) the rate of learning accelerates with experience.learning, depreciation, professional services

Sustainability has become a global corporate mandate with implementation impacted by two key trends. The first is recognition that global supply chains have a profound impact on sustainability which requires “greening” the entire supply chain. The second is technology—digitization, artificial intelligence (AI), and “big data”—which have become ubiquitous. These technologies are impacting every aspect of how companies organize and manage their supply chains and have a powerful impact on sustainability. In this essay, we synthesize current dominant themes in research on sustainable supply chains in the age of digitization. We also highlight potential new research opportunities and challenges and showcase the papers in our STF.

This study examines the relationship between organizational experience and productivity in a professional service organization. The research addresses a gap in the existing literature with respect to organizational experience models in service organizations. Our findings confirm a significant, positive relationship between organizational experience and productivity. In addition, we investigate the effect of information technology on the relationship between organizational experience and productivity. The findings indicate that information technology which becomes a part of the production process is associated with productivity improvements, while information technology which merely documents or collects information is not.

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In this issue, Cui et al. () show how the quantity and quality of user‐generated Facebook data can be used to enhance product forecasts. The intent of this note is to show how another type of user‐generated content—customer search data, specifically one obtained from Google Trends—can be used to reduce out‐of‐sample forecast errors. Based on our work with an online retailer, we bolster Cui et al. () result by showing that adding customer search data to time series models improves out‐of‐sample forecast errors.