Search

Search Results

• Potential for using ai in the development of sustainable supply chains

  90-106

  Barnabás Sarkadi

  Erzsébet Buglyó-Nyakas

  Tímea Gál

  Views:

  135

  Logistics processes have an increasingly significant environmental impact, which is partly caused by a lack of knowledge or priority for green logistics. Emissions from transport, energy consumption related to production, storage and material handling, as well as packaging waste are all serious burdens. At the same time, environmentally friendly solutions can improve the image of companies. The goal of a green supply chain is not only to deliver products to the consumer, but also to reduce environmental impact while maintaining efficiency and reliability. Sustainability strategies such as reducing the carbon footprint or increasing energy efficiency are crucial. The topic was examined by systematic literature analysis based on the Web of Science database (2014–2024), using the PRISMA methodology. According to the results, from 2021 onwards, digital technologies (e.g. blockchain, Industry 4.0, artificial intelligence) have come to the fore, which will determine the future of green logistics and further research directions.

• Adaptation of automated control technologies in SMEs

  80-89

  Marcell Juhásu

  Views:

  106

  The spread of automated controlling technologies significantly impacts the operations of small and medium-sized enterprises (SMEs), especially in financial management. These technologies enable companies to manage their finances more efficiently, reduce costs, and enhance competitiveness. The benefits of automation include increased accuracy, reduced manual labor, and faster data processing, which allow managers to make more informed decisions. Through digital transformation, SMEs can access the latest technologies, such as artificial intelligence, cloud solutions, and big data analytics, further boosting the effectiveness of controlling systems. In the future, it will be crucial for SMEs to integrate new technologies like blockchain, IoT, and AI to sustainably improve their performance and competitiveness.

• The impact of digital transformation on the business model

  69-79

  László Arany

  Péter András Popovics

  Views:

  307

  The aim of this study is to examine the characteristics of the digital economy and digital business models, summarising and contextualising the milestones, tools, conditions, socio-economic impacts and areas of the emergence and development of the digital economy. Due to the interdisciplinary nature of the digital economy and its wide range of interpretations, there are no universally accepted, precise definitions and taxonomies, and the subject is delimited by a number of definitions, due to the specificities of the discipline and the approaches taken by studies in particular sub-disciplines. The digital economy is the main driver of economic growth, changing lifestyles, transforming the economy and leading to profound consequences for businesses, jobs and people. The first wave of the emergence of the digital economy can be detected in the second half of the 20th century, when it was driven primarily by the new technology itself, mainly the internet, as a widely affordable and accessible factor driving exponential growth. The future digital economy could be based on a combination of IoT (Internet of Things) and AI (Artificial Intelligence). In a general sense, the digital transformation brought about by the digital economy can be defined as the modification or adaptation of existing business models, as a result of the dramatic transformation in consumer and societal behaviour, attitudes and ways of being, as well as the dynamic pace of technological development, modernisation and innovation. Other areas of the digital economy are new digital models (digital platforms, cloud services), automation, massive data collection, data processing, data analytics, algorithm-based decision making.

• Semantic role labeling in natural texts

  164-171

  Zoltán Subecz

  Views:

  164

  The event extraction and semantic role labeling are important areas in information extraction from natural language texts. Semantic role labeling, sometimes also called shallow semantic parsing, is a task in natural language processing consisting of the detection of the semantic arguments associated with the predicate or verb of a sentence and their classification into their specific roles. We created a program for this task in Java language, in which we applied data
  mining and artificial intelligence algorithms. We introduce in this article the principles and results of this application.