Nowadays, the theme of computer thinking is a common topic for educational research. The scientific literature on the subject has gradually appeared, in which psychologists emphasize the need for the development of thinking of children. Research often relates only to the development of computational thinking at elementary and high schools. Nowadays, almost everything is digitalized, so it is important to also develop the computational thinking skills of students at higher levels. In our study, we present the results of the conducted data analysis in which we examined the development of students’ efficiency. On the basis of the results, we propose a possible solution for the development of computational thinking. Using data research processes, we examined the results of the object-oriented (OO) planning and programming subject of Budapest Business School, going back 5 years. The results show that the level of particular computational thinking could be measured using the exam results, and teacher advancement (experience) improved the level of particular computational thinking. Today, education has been greatly influenced by COVID-19, challenging not only teachers but also students. The production of particular computational thinking under COVID-19 or online is much more effective than the pursuit of full computational thinking through traditional teaching.

An efficient and flexible production system can contribute to production solutions. These advantages of flexibility and efficiency are a benefit for small series productions or for individual articles. The aim of this research was to produce a genetic production system schedule similar to the sustainable production scheduling problem of a discrete product assembly plant, with more heterogeneous production lines, and controlled by one-time orders. First, we present a detailed mathematical model of the system under investigation. Then, we present the IT for a solution based on a soft calculation method. In connection with this model, a computer application was created that analyzed various versions of the model with several practical problems. The applicability of the method was analyzed with software specifically developed for this algorithm and was demonstrated on a practical example. The model handles the different products within an order, as well as their different versions. These were also considered in the solution. The solution of this model is applicable in practice, and offers solutions to better optimize production and reduce the costs of production and logistics. The developed software can not only be used for flexible production lines, but also for other problems in the supply chain that can be employed more widely (such as the problem of delivery scheduling) to which the elements of this model can be applied.

The just-in-sequence inventory strategy, as an important part of the supply chain solutions in the automotive industry, is based on feedback information from the manufacturer. The performance, reliability, availability and cost efficiency are based on the parameters of the members of the supply chain process. To increase the return on assets (ROA) of the manufacturer, the optimization of the supply process is unavoidable. Within the frame of this paper, the authors describe a flower pollination algorithm-based heuristic optimization model of just-in-sequence supply focusing on sustainability aspects, including fuel consumption and emission. After a systematic literature review, this paper introduces a mathematical model of just-in-sequence supply, including assignment and scheduling problems. The objective of the model is to determine the optimal assignment and schedule for each sequence to minimize the total purchasing cost, which allows improving cost efficiency while sustainability aspects are taken into consideration. Next, a flower pollination algorithm-based heuristic is described, whose performance is validated with different benchmark functions. The scenario analysis validates the model and evaluates its performance to increase cost-efficiency in just-in-sequence solutions.

A major contributor to climate change is the emissions from Information and Communication Technology (ICT) devices and digitalisation. Energy use, heat production, and the operation of assets all contribute to the production of harmful emissions. However, indirect emissions, such as production and disposal, also play a role. We rely mainly on the output of small and medium-sized enterprises (SMEs). This paper focuses on the emissions of SMEs. Is it certain that cloud services (remote data storage and management) leave a much smaller carbon footprint than ICT devices for own use? These two solutions lead to a paradox: using more modern devices to produce less emissions requires more energy and generates more heat. This article analyses how to resolve this paradox for SMEs.

Integrált rendszerek szükségessége kis- és középvállalkozások esetében • Necessity of Enterprise Resource Planning Systems for Small and Medium-sized Enterprises