Title: Bringing Data Science to the Management of Workforces
Abstract: The talk will cover several, real-world collaborations relating to the operational management of workforces. The main part of the talk will focus on a research project with the Apple Worker Exit Study using extensive data on staffing, productivity, and pay from within a consumer electronics supply chain producing tens of billions in USD revenue quarterly. We study how firms should manage the problem of worker turnover, including its surprising impact on low-skilled workforces and the implications for production, wage, and inventory decisions. Despite the lack of skills, we find that worker turnover impedes coordination between assembly line coworkers by weakening knowledge sharing and relationships. We structurally estimate a dynamic equilibrium model of workers’ endogenous turnover decisions and the firm’s dynamic production and staffing decisions, and we apply reinforcement learning to evaluate managerial alternatives. A less turnover-prone, hence more productive, workforce reduces the firm’s variable production costs by 4.5%, or an estimated $928 million for the studied product. Such benefits justify paying higher efficiency wages even to less skilled workforces; furthermore, interestingly, rational inventory management policies incentivize self-interested firms to reduce rather than tolerate turnover. We also cover more recent research that develops learning algorithms to address the problem of worker stress and burnout for highly skilled workforces (ICU nurses and fighter jet pilots). In particular, we equip the nurses staffing three highly sophisticated ICUs with physiological sensors to identify and prevent exceptionally stressful workflows; and use physiological sensors placed on jet pilots to better train them against fatigue.
Bio: Ken Moon is an Assistant Professor and Claude Marion Endowed Faculty Scholar of Operations, Information and Decisions at the Wharton School, University of Pennsylvania. He received his Ph.D. at the Stanford Graduate School of Business, his J.D. from the Harvard Law School, and his dual bachelor's in Mathematics and Economics from Stanford University. His research interests center on workforces and marketplaces, and he applies data science methods spanning structural estimation, machine learning, and econometrics. His work has received the POMS Operational Excellence Best Paper Award (2020) and through supervising doctoral students the MSOM Best Student Paper Award (2021), the POMS Product Innovation and Technology Best Student Paper Award (2020), and the IBM Service Science Best Student Paper Award (2017).
