Workforce Scheduling Optimization Using Machine Learning in High-Turnover U.S. Service Operations: Evidence from the Hotel Industry

Abstract

This paper examines the relationship between machine learning scheduling tool adoption as the independent variable and labor cost efficiency and employee retention rate as the dependent variables in high-turnover United States service operations, with an evidence base drawn from the hotel industry. The United States hotel industry presents the most acute and well-documented context for this inquiry: hotel employment stands at approximately 2.15 million workers, labor costs represent 32.4 percent of total hotel revenue, 65 percent of hotels report persistent staffing shortages, and the industry records one of the highest attrition rates in the United States service sector at 4.28 percent. Drawing on Human Capital Theory and the Technology-Organization-Environment framework, this paper conducts a systematic narrative literature review of peer-reviewed research on machine learning scheduling, workforce management, and high-turnover service operations. Four machine learning technique categories applied to scheduling optimization are reviewed and compared: supervised learning approaches, reinforcement learning, neural network demand forecasting, and constraint satisfaction hybrid methods. Barriers to effective adoption are mapped across the three framework dimensions and found to be concentrated in the organizational and environmental dimensions. The paper proposes a three-level reskilling framework for hotel operations managers and a four-stage implementation model with cost parameters and success metrics. A proposed mixed-methods empirical validation design specifies the research approach for primary data confirmation. The paper contributes the first integrated framework for machine learning scheduling adoption in high-turnover hotel operations that simultaneously addresses technology adoption barriers, workforce capability development, and labor cost efficiency measurement.