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The employees’ knowledge level is crucial because it ensures the long-term social, environmental, and financial sustainability of a company’s operations on the global market. In doing so, there is a problem of how to measure the employees’ knowledge level within the segment, and improve it based on performed measures. In our research, we proposed the use of real-world problem solving for the purpose of determining and improving the employees’ knowledge level potential. In the study, we present a numerical modeling approach to evaluate real-world problem-solving capabilities and find out how the captured data influence the important parameters for determining the employee’s knowledge level. We have measured seven parameters (number of solutions, technological feasibility of the proposed solutions, team thinking activities, age, prior knowledge about sustainable production methods, and education level). By using linear regression, we have defined general forms of functional dependencies that enable the individual company to calculate and, if necessary, increase the employees’ knowledge level for their team. The results of the research show the correlation dependence of individual parameters, in which we found a close relationship between the employees’ knowledge level and age, as well as the technological feasibility of proposed solutions and educational level. In the final phase of the research, we conducted an experiment with mixed groups consisting of participants from different age groups, with the results showing a high degree of intergenerational integration importance.

The Importance of Employees’ Knowledge in Sustainable, Green Manufacturing: Numerical Modeling Approach