The automotive sector is one of the key branches of the global economy. The automotive industry is also a very important sector of the Polish economy, as it generates over 8% of GDP and accounts for over 20% of the annual export value. Industry 4.0 and the effective use of modern technologies give a chance for its further dynamic development. The implementation of Industry 4.0 solutions in the business processes of automotive companies should not only take place in the area of production or logistics, as it is usually indicated, but also in their other functional areas, such as quality management, human resources management, and innovation management. The purpose of the article is to diagnose the level of maturity in the implementation of Industry 4.0 solutions in selected management areas of automotive companies operating in Poland. Using a maturity assessment tool, the authors assessed the level of maturity in six selected functional areas of an enterprise, such as production and logistics management, quality management, human resources management, social and environmental responsibility, and product innovation management. The authors also formulated conclusions and recommendations concerning actions that should be taken by automotive companies in order to achieve higher maturity levels in the implementation of Industry 4.0 solutions.
This article discusses original studies that demonstrate the relation between developed elements of the transportation network (road system density; railway system density; number of regional railway and bus connections, length of regional railway and bus connections, online accessibility to transportation services and other services related to the development of IT techniques to benefit mass transit) and the regional GNP. A new development relative to preceding studies (as quoted) is that the correlation coefficients calculated do not indicate any essential interrelations between elements of the transport system, or even the number of regional passenger transport services and regional GNP. A determination of the remaining data interrelations indicated the elements of the network which are considered essential to the development of mass transit, as resulting from a study carried out for the first time in 2015 for the Górnośląska-Zagłębioska Metropolis. Considering the fact that the number of railway connections has proven to be the most important determinant of the overall number of passenger transport services, the second part of the article presents studies that focus on the modeling of the railway network, applying the graph theory (extensively applied for ITS). Selected optimized models were analyzed and assessed in terms of possible implementability of specific improvements and the resultant growth in the number of passenger transport services. The research method applied was not novel, but the conclusions drawn from it were surprising, as they indicated that an optimized network of railway connections would not cause any significant increase in the number of passenger transport services. Successive surveys (supplementing statistical analyses) have confirmed the importance of ITS in increasing the share of mass transit in overall transit. (1) The study was carried out in Polish regions, with particular emphasis on Silesia. (2) Its conclusions emphasize the importance of data accumulated for ITS in decision-making processes aiming to ensure the sustainable development of mass/passenger transport. The article confirms a hypothesis which claims that "modeling the regional public transportation grid, applying the principles of ITS, stimulates a growth in the share of passenger transport in the overall bulk of transport, thus contributing to the sustainable development of the region".
The main purpose of the paper was the structural analysis of the connections network used by a railway carrier Koleje Dolnośląskie S.A. operating in southern Poland. The analysis used simulation methods. The analysis and simulation were based on graph theory, which is successfully used in analysing a wide variety of networks (social, biological, computer, virtual and transportation networks). The paper presents indicators which allow judging the analysed connections network according to an appropriate level of transport services. Simulation results allowed proposing some modifications for the improvement of the analysed connections network. The paper also demonstrates that graph theory and network simulations should be used as tools by transportation companies during the stage of planning a connections network.
Streszczenie. Rozwijanie innowacyjnych komponentów w modelu otwartym lub kupowanie półproduktów to dwa modele pozyskiwania nowych rozwiązań do produktu końcowego. Źródłem pozyskiwania rozwiązań są dostawcy, instytuty badawcze, a nawet konkurenci. Źródła i procesy pozyskiwania innowacji tworzą różnego rodzaju ryzyka. W artykule przedstawiono listę rodzajów ryzyka, które może pojawić się w procesie rozwoju oraz zakupu innowacji w branży motoryzacyjnej. Podano sposoby na redukowanie zidentyfikowanych rodzajów ryzyka. Opisano przykład Toyoty, której dostawcy wydają się nie stwarzać ryzyka związanego z innowacyjnością łańcucha dostaw.
The chapter presents original research showing the relationship between expenditures on developing systems (including IT) supporting innovation management of supply chains of three international automotive companies and their innovation performance. A novelty in comparison to previous (cited) studies is that the calculated correlation coefficients show the significance of the link between the expenditures on components of the suppliers' innovation management system, that is, expenditure on the improvement of innovative competences of suppliers, and the expenditure on ICT, and innovation performance of automotive companies. By establishing data interdependence, elements of the management system that contributed the most to improving the innovation performance of three international auto motive companies over the past years were selected. Analyses of data may facilitate the management of expenditure on: internal R&D activities and support for external innovators. In this chapter, the author used the results of her previous studies. To collect additional data, the following methods were used: review of documents and diagnostic survey (technique: survey, tool: questionnaire). To analyse the data obtained, a statistical method and computer simulation methods were used. The research was based on quantitative secondary data 1 and estimates (given by respondents from the automotive companies as a percentage of a specific value contained in official documents). The data for the analysis came from publications and surveys.
Celem opracowania jest wskazanie rozwiązań dla dedykowanego systemu logistyczno- transportowego usprawniającego przewóz towarów w branży motoryzacyjnej. Innowacyjne rozwiązania leżą w gestii władz regionu, potrzebę ich wdrożenie można zawrzeć w Strategii Rozwoju Transportu Województwa rozważając ich zgodność z Koncepcją Przestrzennego Zagospodarowania Kraju i Krajową Strategią Rozwoju Regionalnego. Podjęcie dedykowanych (wiodącej branży regionu) przedsięwzięć infrastrukturalnych i organizacyjnych, może przyczynić się do rozwoju gospodarczego regionu, ale to zależne będzie od możliwości organizacyjnych oraz pozyskania odpowiednich funduszy. Warto na początek zbadać czy w opinii regionalnych przedsiębiorców proponowane rozwiązania są pożądane. W artykule pokazano wyniki takich badań, potwierdzając hipotezę, że przedsiębiorcy wiodącej w regionie Śląska branży motoryzacyjnej oczekują dedykowanych rozwiązań w regionalnej sieci logistyczno-transportowej.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
334 Leonard St
Brooklyn, NY 11211
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.