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    Wyświetlanie 1-2 z 2
    Tytuł:
    Comparison of Selected ERP Systems Supporting the Production Planning and Control on the Example of Automotive Industry
    Autorzy:
    Sika, Robert
    Wojtala, Oliwia
    Hajkowski, Jakub
    Powiązania:
    https://bibliotekanauki.pl/articles/2172187.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    information technology
    enterprise management system
    automotive industry
    Opis:
    In many companies, along with the economic development, the use of integrated management systems is becoming more and more common, which are subject to evolution in terms of, inter alia, offered functions and new user requirements. The main purpose of this paper is to compare selected ERP (Enterprise Resource Planning) systems in the field of production planning and control on the example of the automotive industry. The paper presents the contemporary functioning of the automotive industry against the background of issues related to the integrated management systems used in them. The research part presents the proprietary methodology for the assessment of IT systems used in the automotive industry, which included a user survey. The obtained score allowed to indicate the optimal ERP class system supporting production planning and control.
    Źródło:
    Management and Production Engineering Review; 2022, 13, 3; 39--51
    2080-8208
    2082-1344
    Pojawia się w:
    Management and Production Engineering Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of instance-based learning for cast iron casting defects prediction
    Autorzy:
    Sika, Robert
    Szajewski, Damian
    Hajkowski, Jakub
    Popielarski, Paweł
    Powiązania:
    https://bibliotekanauki.pl/articles/407199.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    soft modelling
    instance-based learning
    k-nearest neighbours algorithm
    cast iron casting defects
    computer application
    Opis:
    The paper presents an example of Instance-Based Learning using a supervised classification method of predicting selected ductile cast iron castings defects. The test used the algorithm of k-nearest neighbours, which was implemented in the authors’ computer application. To ensure its proper work it is necessary to have historical data of casting parameter values registered during casting processes in a foundry (mould sand, pouring process, chemical composition) as well as the percentage share of defective castings (unrepairable casting defects). The result of an algorithm is a report with five most possible scenarios in terms of occurrence of a cast iron casting defects and their quantity and occurrence percentage in the casts series. During the algorithm testing, weights were adjusted for independent variables involved in the dependent variables learning process. The algorithms used to process numerous data sets should be characterized by high efficiency, which should be a priority when designing applications to be implemented in industry. As it turns out in the presented mathematical instance-based learning, the best quality of fit occurs for specific values of accepted weights (set #5) for number k = 5 nearest neighbours and taking into account the search criterion according to “product index”.
    Źródło:
    Management and Production Engineering Review; 2019, 10, 4; 101-107
    2080-8208
    2082-1344
    Pojawia się w:
    Management and Production Engineering Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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