Data-Driven Modeling of Employee Performance Using Self-Organizing Maps and Kinematic Models

Florentina-Raluca BÎLCAN, Ionica ONCIOIU, Mihai PETRESCU, Anca-Gabriela PETRESCU, Diana Andreea MÂNDRICEL

Abstract

The employee performance evaluation and optimization is a key objective for modern organizations, where decision making in human resource management must be supported by robust analytical methods. This study proposes a modeling framework based on the integration of Self-Organizing Maps (SOMs) and kinematic models (kinMods) for the analysis and dynamic simulation of the employee performance under different managerial scenarios. SOMs are employed for classifying the employees according to their skills, performance, and potential, thereby identifying latent patterns in the team structure. In addition, the kinMod enables the simulation of managerial scenarios - such as promotions, restructuring, or the departure of key employees - and the assessment of their impact on organizational cohesion and efficiency over time. The experimental results, illustrated by the correlation analysis, employee clustering outcomes, and dynamic simulations, confirm the usefulness of the proposed methodology for reducing subjectivity in human resources assessment and providing an objective decision support tool. The integration of SOMs and kinMods thus provides a transferable methodological framework with potential applications in HR analytics and organizational management.

Keywords

Adaptive topological learning, Kinematic modeling, Employee performance analysis, Human resource analytics, Data-driven decision support.

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