STOCHASTIC AND COMPUTATIONAL MODELLING OF WORKFORCE PREDICTION: A COMPARATIVE ANALYSIS OF MARKOV, NEURAL, AND HYBRID ALGORITHMS WITHIN INDUSTRY 4.0 FRAMEWORK

Abstract

The Fourth Industrial Revolution (Industry 4.0) is revolutionising Human Resource Management (HRM), transitioning it from conventional, reactive methodologies to data-informed, predictive approaches. This paper reviews the application of various quantitative models-including classical stochastic models, advanced neural networks, and modern hybrid AI frameworks-for workforce forecasting and management. We discuss the foundational role of Markov Chain models in predicting internal labor supply and employee mobility, highlighting their ability to project future workforce states with greater precision. We then explore the capabilities of Back propagation Neural Networks (BPNNs) for forecasting complex, non-linear relationships such as employee retention and performance. A benchmark analysis demonstrates the superior performance of hybrid models, which integrate multiple machine learning techniques to enhance predictive accuracy. The practical implications of these technologies are significant, enabling proactive retention strategies, optimized workforce allocation, and data-driven decision-making. The effective execution of these models necessitates meticulous attention to ethical dilemmas, encompassing data privacy, algorithmic bias, and the imperative for human supervision. The report continues by delineating potential research avenues aimed at establishing ethical frameworks and enhancing the explain ability of intricate AI models to guarantee a sustainable and equitable future for labour management.