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    Wyświetlanie 1-2 z 2
    Tytuł:
    Implementation of Industry 4.0 Techniques in Lean Production Technology: A Literature Review
    Autorzy:
    Lucantoni, Laura
    Antomarioni, Sara
    Ciarapica, Filippo Emanuele
    Bevilacqua, Maurizio
    Powiązania:
    https://bibliotekanauki.pl/articles/2172180.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    industry 4.0
    lean management
    total productive maintenance
    big data analytics
    VOSviewer
    Opis:
    Lean thinking and Industry 4.0 have been broadly investigated in recent years in intelligent manufacturing. Lean Production is still one of the most efficient industrial solutions in business and research, despite being implemented for a long time. On the other hand, Industry 4.0 has been introduced referring to the fourth industrial revolution. This study aims to analyze the combination of both Industry 4.0 and Lean production practices through a systematic literature review from a Lean Automation perspective. In this field, 189 articles are examined using VOSviewer for cluster analysis. Then, a more detailed analysis is provided to explore how Industry 4.0 and Lean techniques are integrated from a practical perspective. Results highlighted Big Data Analysis and Value Stream Mapping as the most common techniques, also emphasizing a growing trend toward new publications. Nevertheless, few practical applications are identified in the literature highlighting six gaps in the correlation of LA practices.
    Źródło:
    Management and Production Engineering Review; 2022, 13, 3; 83--93
    2080-8208
    2082-1344
    Pojawia się w:
    Management and Production Engineering Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Big data-driven framework for viral churn prevention: a case study
    Autorzy:
    Lucantoni, Laura
    Antomarioni, Sara
    Bevilacqua, Maurizio
    Ciarapica, Filippo E.
    Powiązania:
    https://bibliotekanauki.pl/articles/406954.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Big Data Analytics
    Machine Learning
    Probability Estimation Trees
    Customer Value Management
    ICT sector
    Opis:
    The application of churn prevention represents an important step for mobile communication companies aiming at increasing customer loyalty. In a machine learning perspective, Customer Value Management departments require automated methods and processes to create marketing campaigns able to identify the most appropriate churn prevention approach. Moving towards a big data-driven environment, a deeper understanding of data provided by churn processes and client operations is needed. In this context, a procedure aiming at reducing the number of churners by planning a customized marketing campaign is deployed through a data-driven approach. Decision Tree methodology is applied to drow up a list of clients with churn propensity: in this way, customer analysis is detailed, as well as the development of a marketing campaign, integrating the individual churn model with viral churn perspective. The first step of the proposed procedure requires the evaluation of churn probability for each customer, based on the influence of his social links. Then, the customer profiling is performed considering (a) individual variables, (b) variables describing customer-company interactions, (c) external variables. The main contribution of this work is the development of a versatile procedure for viral churn prevention, applying Decision Tree techniques in the telecommunication sector, and integrating a direct campaign from the Customer Value Management marketing department to each customer with significant churn risk. A case study of a mobile communication company is also presented to explain the proposed procedure, as well as to analyze its real performance and results.
    Źródło:
    Management and Production Engineering Review; 2020, 11, 3; 38-47
    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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