This study investigates and compare the students’ entrepreneurial mindset dimensions and intentions from EU member countries Italy, Austria, Sweden, and Greece, and an EU candidate country Bosnia and Herzegovina, which are important for fostering start-ups, economic development, and job creation. By analyzing students’ entrepreneurial mindset dimensions, demographic and academic characteristics, and availability of resources, the research aims to identify factors that impact students’ entrepreneurial intentions. Findings provide valuable insights into how these factors vary across different educational, economic, and social contexts with guidance for enhancing education to better support students’ entrepreneurial aspirations.Machine learning Random Forest was used to analyze the impact of entrepreneurial mindset dimensions, resources, and demographic and academic characteristics on students’ entrepreneurial intentions of students from EU member countries and Bosnia and Herzegovina. SHapley Additive exPlanations (SHAP) values were utilized to analyze feature importances and contributions to the model’s predictions. Statistical hypothesis tests were also conducted to compare differences of students’ entrepreneurial mindset dimensions, intentions and availability of resources between the EU member countries and Bosnia and Herzegovina.High values of entrepreneurial mindset dimensions have positive impact on entrepreneurial intentions in both EU member countries and Bosnia and Herzegovina. The availability of resources and orientation to innovations were the most impactful features for students in EU and Bosnia and Herzegovina, respectively. Gender and academic characteristics showed minimal influence. There are no significant differences in all dimensions between EU member countries and Bosnia and Herzegovina, except for confidence dimension and entrepreneurial intentions, which are significantly greater in Bosnia and Herzegovina.Findings suggest that tailored educational interventions focusing on key entrepreneurial mindset dimensions and resource access could significantly enhance entrepreneurial intentions among students. For policymakers and educators, this study provides a foundation for developing targeted strategies that align with the specific contexts of both EU member countries and Bosnia and Herzegovina. In this way higher education institutions can better support students’ entrepreneurial aspirations, contributing to broader economic development and job creation. This research offers recommendations for improving entrepreneurship education across diverse educational, economic, and social contexts and more balanced and inclusive economic development in Europe.

Cellular manufacturing represents a production system arrangement in which machines, tools, workers, and devices are grouped to produce a single product or a group of products with similar production requirements. By implementing cellular manufacturing, it is possible to significantly influence the elimination of the seven types of Lean waste: transport, inventory, unnecessary motion, waiting, overproduction, overprocessing, and defects. There are various models of a cellular manufacturing organization, and some of the most widely used and studied include the Toyota Sewing System, Bucket Brigades, Working Balance, and Rabbit Chase. This paper aims to present different types of lean cellular manufacturing organizations. By reviewing the literature, the advantages and disadvantages of individual types of cellular manufacturing will be systematized. In the practical part of the paper, the theoretical assumptions will be confirmed, and the impact and robustness of individual types of cellular manufacturing will be explored in different situations. Simulations were performed in real conditions on real products to demonstrate the efficiency of individual types of cellular manufacturing organizations depending on the duration of technological operations. The goal was also to examine the robustness of individual types of cellular organization in case of the absence of certain operators or insufficiently trained operators. The criteria used to compare different cellular models were productivity, non-conformance, WIP inventory, time to deliver the first correct piece, and flow time. Simulations were performed for the Toyota Sewing System, Bucket Brigade, Working Balance, and Rabbit Chase cellular manufacturing concepts. The simulation results indicate significant differences in the performance of individual concepts, where the difference in some criteria can reach up to 100%.  

Analyzing students’ academic performance is important for evaluating enrollment criteria which establish the standards required for pupils who finished secondary school to gain admission to a higher education institution. The aims of this research were to develop a machine learning prediction Decision Tree classifica-tion model and analyze the performance of engineering students based on their performances during second-ary school education. The performance of students was analyzed and measured as a binomial response whether students successfully finished the first and the second study years. The developed model examined general success, number of awards obtained at competitions, special awards, average grades in mathematics, physics, and one of the official state languages during secondary school as predictor variables. The number of courses transferred from the first into the second study year and students’ GPA obtained during the first study year were added as predictor variables in the analysis and development of a prediction model for the students’ performance during the second study year and their enrollment in the third study year. Developed machine learning prediction model showed that for the performance of enrolled students in the first study year general success of students during secondary school is the most important predictor variable, followed by mathematics and physics grades. However, for the performance of the students enrolled in the second study year the most important predictor variable was number of the courses transferred from the first into the second study year, followed by students’ GPA obtained during the first study year and general success. Machine learning Decision Tree classification modeling was shown to be an adequate tool for the prediction of the performance of engineering students during the first and second study years.