OPTIMIZING WORKFORCE MANAGEMENT: LEVERAGING LOGISTIC REGRESSION TO PREDICT EMPLOYEE WORK PATTERNS DURING TIME OFF

Abstract

This article explores the complexities surrounding the legal and operational implications of employees working on their scheduled days off. It emphasizes the importance of employers complying with the Fair Labor Standards Act (FLSA) and other applicable state and local laws. The article stresses the need to compensate employees for any work performed, even on unscheduled days. It also discusses the challenges posed by varying state laws regarding vacation time, scheduled shift changes, and overtime compensation. Additionally, the article introduces an innovative solution that integrates predictive analytics, logistic regression models, and advanced AI-driven time management systems. This solution aims to proactively manage employee work patterns and ensure a healthy work-life balance. It involves meticulous data collection, model development, and the deployment of an integrated system for monitoring work hours against scheduled time off. The article proposes a combination of technological advancements, policy reform, and cultural shifts within organizations to effectively address the issue. It suggests the development of an employee self-service portal, automated alerts, and approval processes, along with training and awareness programs, to facilitate a smooth transition to these new systems and practices. This comprehensive approach aims to foster a workplace environment that values both employee well-being and organizational efficiency.