A digitális forradalom időszakában a cégek eredményességéhez elengedhetetlen a megfelelő informatikai háttér, illetve technológiai tudás, ahol a digitális átalakulás sikere függ a digitális érettségtől. Ennélfogva a tanulmány célja a digitális érettség definiálása, egy modell kialakításának elméleti megalapozása, keretrendszerének bemutatása, amely segítségül szolgál a kis- és középvállalkozások (KKV-k) számára, hogy felmérhessék, hol is tartanak a digitalizációban (mennyire fejlett a digitális erőforrásrendszere, digitális szemlélete), illetve, hogy gyorsabban és hatékonyabban tudjanak reagálni a környezeti változásokra. A létrehozott modell, a digitális érettség technikai architektúrája (DÉTA) az erőforrás-alapú elméletre (dinamikus képességek elmélete), az érettségi modellekre és a KKV-k vezetésével kapcsolatos vizsgálatokra épül. A kialakított modell egy dinamikus modell, melynek célja a menedzsment támogatása stratégiai, digitális és szervezeti fejlesztések során. A modell IT- és szervezeti dimenzióra bontható, amely hat főkomponenst és 28 alkomponenst tartalmaz. A tanulmány fő célja a komponenssúlyok meghatározása egy fuzzy modell megalkotásához.

As companies try to maintain and strengthen their competitive advantage, they should be aware of the level of their digital maturity. The study aims to present a methodology that helps to determine the position of a small and medium-sized enterprise in the digital maturity life-cycle. This is performed on the basis of maturity and digital maturity models, and company growth theories. A number of studies and models have been prepared to determine digital maturity on the basis of various sectoral criteria, but these are all one-dimensional. The study therefore proposes a multi-dimensional model for determining the digital maturity life-cycle of small and medium-sized enterprises that takes into account companies’ digital maturity, the IT intensity of various sectors and their organizational characteristics. The model defines five maturity levels together with their relevant characteristics, classified into three levels in terms of data-information. It can help small and medium-sized enterprises adopt more accurate decisions regarding areas in need of development.

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 artificial intelligence, also soft computing is more and more relevant in practice and their mystification causes plenty of misunderstandings. There's a need in secondary schools as well as in non-technical/scientific higher education to introduce them in an understandable way for later use. So in our lecture and article we show the applicability of soft computing in an easy to use simulation. In today's computer science education environment deterministic algorithms play a large role, the students learn, understand and maybe even know how to implement them. It would be important to make solutions, techniques which don't rely on traditional deterministic observations known and understandable to not only students that are interested in computer science. The best solution is to reach the results of certain searching tasks. Since in our BigData based world these techniques are the core of procedures, there's a huge importance for the high-level understanding of these techniques in secondary school. There's no need to know the functioning of the core. In the first step importance of optimization tasks need to be introduced and made understood, as well as their practical problems and relevance in real life. In the next step it should be shown, that if we make concessions in the purpose of optimization (we show this through an example that this is achievable) with the help of other, less mathematically correct, approaches we get really good results. In the article we show with great detail through a case study an optimization task, show its relevance then we introduce a simulation tool which's AI based. With the use of this tool we go through all possible solution ways, and compare how two differing AI based systems behave. After the applied simulations we rate the results for the students/listeners and examine their importance. We also draft in short how to interpret the simulation results and what their efficiency means. We show as well what role running time, (speed) step number, number of parameters as well as practicable parameters play. The article shows how we can use this in education through a case study.

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.