Designing a digital twin of an employee

The development of a digital twin of an employee aims to reduce risks such as unpredictable or undesirable behavior, burnout, declining engagement in work processes, lower productivity, and the emergence of destructive conflicts. The paper outlines approaches to designing digital twins and proposes organizational and methodological support for their development as socio-economic systems characterized by goal-setting, dynamism, reflection, and bounded rationality. The prototype of an employee’s digital twin is based on a hybrid model that integrates mathematical models, which enable computer simulation of physical processes, with data-driven models incorporating data mining and reinforcement learning methods to identify patterns in data that are useful for decision-making. Such intelligently managed digital twin merges these two classes of models and is closely tied to the concept of individual human capital, understood as the sum of professional, intellectual, and social resources that define a worker’s productivity and income. The digital twin incorporates both a human capital assessment model and a management model that supports the creation of personalized professional development trajectories. The management model applies reinforcement learning algorithms to generate an optimal management regime, consisting of measures and decisions aimed at employee development while accounting for dynamic personal characteristics such as health, professional and other competencies, and motivation. The implementation of an employee’s digital twin has practical significance, as it helps to mitigate potential risks, enhance employee productivity, and improve the overall efficiency of the enterprise.