Intelligent Workforce Management: A Predictive Analytics Approach

Abstract

Customary methods of managing the workforce are often inefficient, relying on simple forecasting methods, manual scheduling, and reactive actions to manage the expected workload․ Overstaffing, understaffing, and inefficient resource allocation are common issues․ This article explores the opportunities created by artificial intelligence and machine learning for workforce optimization in service-oriented industries. The three technical sub-areas are predictive demand forecasting approaches, clever skill-based scheduling, and the related performance, service quality and employee well-being analysis. Demand forecasting includes the modeling of seasonality using time series methods such as Long Short-Term Memory networks (LSTM) and Autoregressive Integrated Moving Average (ARIMA) models as well as the prediction of surges when unexpected demand spikes occur․ The skill-based scheduling problem gives an overview of multi-dimensional human competency management frameworks and mixed integer linear programming optimization approaches that focus on productivity, compliance and ergonomics. The recent peer-reviewed literature supports that the optimization of the workforce in a sustainable manner depends on a combination of technological capabilities and organizational culture, leadership commitment, and digital preparedness. For organizations to maximize productivity and ensure the health of their employees, human-centered design must be at the crux of AI predictions. The article concludes that AI-driven workforce optimization represents a model shift from administrative labor management to a performance-oriented labor management capability․