Lean management and related ideas have had a significant impact on organizations throughout North America and the world. Despite its popularity and impact, I-O psychologists have largely neglected Lean as a research topic and few I-O psychologists engage in applied practice in the area. In this focal article, we provide a working definition of Lean and present examples of Lean’s influence. Next, we outline possible reasons to explain I-O psychologists’ indifference to Lean. Finally, we provide some topic areas that I-O psychologists can use to contribute to the Lean literature. By using I-O psychologists’ skill in measurement and evaluation, along with our considerable organizational theory, we believe that I-O psychology can improve Lean and broaden their impact. We hope this focal article will inspire I-O psychologists to reconsider a research and practice area that they have previously ignored. In addition, we hope that this article causes I-O psychologists to reflect on their role to play in addressing popular management trends.

Attrition modeling is a direct application of extant turnover research that can favorably impact workforce planning and action planning. However, while academic research enables practitioners insights into understanding turnover phenomena, there is no single document that comprehensively translates this work to give guidance as to the many practical decisions that must be made when modeling turnover, as well as how to apply psychological research to messier operational data. This focal article introduces and provides guidance on attrition modeling by outlining early considerations when planning a study, describing how to mesh theory with operational considerations when identifying turnover predictors within organizational settings, highlighting analytical strategies to model turnover, and considering how to appropriately share results. Collectively, this article serves as a guide to conducting attrition modeling within organizations and offers suggestions for future research to inform best practices.