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A Big Data system supporting Bosch Braga Industry 4.0 strategy

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  • Santos, Maribel Yasmina
  • Oliveira e Sá, Jorge
  • Andrade, Carina
  • Vale Lima, Francisca
  • Costa, Eduarda
  • Costa, Carlos
  • Martinho, Bruno
  • Galvão, João

Abstract

People, devices, infrastructures and sensors can constantly communicate exchanging data and generating new data that trace many of these exchanges. This leads to vast volumes of data collected at ever increasing velocities and of different variety, a phenomenon currently known as Big Data. In particular, recent developments in Information and Communications Technologies are pushing the fourth industrial revolution, Industry 4.0, being data generated by several sources like machine controllers, sensors, manufacturing systems, among others. Joining volume, variety and velocity of data, with Industry 4.0, makes the opportunity to enhance sustainable innovation in the Factories of the Future. In this, the collection, integration, storage, processing and analysis of data is a key challenge, being Big Data systems needed to link all the entities and data needs of the factory. Thereby, this paper addresses this key challenge, proposing and implementing a Big Data Analytics architecture, using a multinational organisation (Bosch Car Multimedia – Braga) as a case study. In this work, all the data lifecycle, from collection to analysis, is handled, taking into consideration the different data processing speeds that can exist in the real environment of a factory (batch or stream).

Suggested Citation

  • Santos, Maribel Yasmina & Oliveira e Sá, Jorge & Andrade, Carina & Vale Lima, Francisca & Costa, Eduarda & Costa, Carlos & Martinho, Bruno & Galvão, João, 2017. "A Big Data system supporting Bosch Braga Industry 4.0 strategy," International Journal of Information Management, Elsevier, vol. 37(6), pages 750-760.
    • Handle: RePEc:eee:ininma:v:37:y:2017:i:6:p:750-760
    DOI: 10.1016/j.ijinfomgt.2017.07.012
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    References listed on IDEAS

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    1. Henning Kagermann, 2015. "Change Through Digitization—Value Creation in the Age of Industry 4.0," Springer Books, in: Horst Albach & Heribert Meffert & Andreas Pinkwart & Ralf Reichwald (ed.), Management of Permanent Change, edition 127, chapter 2, pages 23-45, Springer.
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    1. Acharya, Abhilash & Singh, Sanjay Kumar & Pereira, Vijay & Singh, Poonam, 2018. "Big data, knowledge co-creation and decision making in fashion industry," International Journal of Information Management, Elsevier, vol. 42(C), pages 90-101.
    2. Gupta, Shivam & Kar, Arpan Kumar & Baabdullah, Abdullah & Al-Khowaiter, Wassan A.A., 2018. "Big data with cognitive computing: A review for the future," International Journal of Information Management, Elsevier, vol. 42(C), pages 78-89.
    3. Tseng, Hsiao-Ting, 2023. "Customer-centered data power: Sensing and responding capability in big data analytics," Journal of Business Research, Elsevier, vol. 158(C).
    4. Monshizadeh, Fatemeh & Sadeghi Moghadam, Mohammad Reza & Mansouri, Taha & Kumar, Maneesh, 2023. "Developing an industry 4.0 readiness model using fuzzy cognitive maps approach," International Journal of Production Economics, Elsevier, vol. 255(C).
    5. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    6. Brodny, Jarosław & Tutak, Magdalena, 2023. "Assessing the level of digital maturity in the Three Seas Initiative countries," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    7. Sonntag, Martin & Mehmann, Jens & Teuteberg, Frank, 2021. "Application of Industry 4.0 in the automotive sector," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 151-182, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    8. Fernandes, Marta & Canito, Alda & Bolón-Canedo, Verónica & Conceição, Luís & Praça, Isabel & Marreiros, Goreti, 2019. "Data analysis and feature selection for predictive maintenance: A case-study in the metallurgic industry," International Journal of Information Management, Elsevier, vol. 46(C), pages 252-262.

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