Building an Industry 4.0 Analytics Platform

Practical Challenges, Approaches and Future Research Directions


The ecosystem of big data technologies and advanced analytics tools has evolved rapidly in the last years offering companies new possibilities for digital transformation and data-driven solutions. Industry 4.0 represents a major application domain for big data and advanced analytics in order to exploit the huge amounts of data generated across the industrial value chain. However, building and establishing an Industry 4.0 analytics platform involves far more than tools and technology. In this paper, we report on our practical experiences when building the Bosch Industry 4.0 Analytics Platform and discuss challenges, approaches and future research directions. The analytics platform is designed for more than 270 factories as part of Bosch’s worldwide manufacturing network. We describe use cases and requirements for the analytics platform and present its architecture. On this basis, we discuss practical challenges related to analytical solution development, employee enablement, i. e., citizen data science, as well as analytics governance and present initial solution approaches. Thereby, we highlight future research directions in order to leverage advanced analytics and big data in industrial enterprises.

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  1. 1.

    Apache Software Foundation (2017) Apache hadoop. Accessed 01 Sept 2017

    Google Scholar 

  2. 2.

    Avery A, Cheek K (2015) Analytics governance: towards a definition and framework. Proceedings of the 21st Americas Conference on Information Systems (AMCIS) 2015.

    Google Scholar 

  3. 3.

    Bauernhansl T (2014) Die Vierte Industrielle Revolution – Der Weg in ein wertschaffendes Produktionsparadigma. In: Bauernhansl T, Hompel Mt, Vogel-Heuser B (eds) Industrie 4.0 in Produktion, Automatisierung und Logistik. Anwendung, Technologien, Migration. Springer, Wiesbaden, pp 5–35

    Google Scholar 

  4. 4.

    Bose R (2009) Advanced analytics: opportunities and challenges. Ind Manag Data Syst 109(2):155–172

    Article  Google Scholar 

  5. 5.

    Brettel M, Friederichsen N, Keller M et al (2014) How virtualization, decentralization and network building change the manufacturing landscape: an industry 4.0 perspective. Int J Sci Eng Technol 8(1):37–44

    Google Scholar 

  6. 6.

    Burton B, Walker MJ (2015) Gartner hype cycle for emerging technologies

    Google Scholar 

  7. 7.

    Chamoni P, Gluchowski P (2017) Business analytics – state of the art. Controll Manag Rev 61(4):8–17

    Article  Google Scholar 

  8. 8.

    Davenport TH, Harris JG (2017) Competing on analytics. The new science of winning. Harvard Business Review Press, Boston

    Google Scholar 

  9. 9.

    Dedić N, Stanier C (2017) Towards differentiating business intelligence, big data, data analytics and knowledge discovery. Proceedings of the 5th International Conference on ERP Systems, Revised Papers. Springer, Cham, pp 114–122

    Google Scholar 

  10. 10.

    Espinosa JA, Armour F (2016) The big data analytics gold rush. A research framework for coordination and governance. Proceedings of the 49th Hawaii International Conference on System Sciences (HICSS) 2016. IEEE, Piscataway, pp 1112–1121

    Google Scholar 

  11. 11.

    Gandomi A, Haider M (2015) Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manage 35(2):137–144

    Article  Google Scholar 

  12. 12.

    GE (2017) Predix platform. Accessed 01 Sept 2017

    Google Scholar 

  13. 13.

    Gölzer P, Cato P, Amberg M (2015) Data processing requirements of industry 4.0 – use cases for big data applications. Proceedings of the 23th European Conference on Information Systems (ECIS), paper 61

    Google Scholar 

  14. 14.

    Grochow J (2012) IT infrastructure to support analytics: laying the groundwork for institutional analytics. EDUCAUSE Research Bulletin

    Google Scholar 

  15. 15.

    Gröger C (2015) Advanced Manufacturing Analytics – Datengetriebene Optimierung von Fertigungsprozessen. Josef Eul, Lohmar

    Google Scholar 

  16. 16.

    Gröger C, Stach C (2014) The mobile manufacturing dashboard. Proceedings of the 2014 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, Piscataway, pp 138–140

    Google Scholar 

  17. 17.

    Gröger C, Schwarz H, Mitschang B (2014) The manufacturing knowledge repository. Consolidating knowledge to enable holistic process knowledge management in manufacturing. Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS) 2014. SciTePress, pp 39–51

    Google Scholar 

  18. 18.

    Gröger C, Kassner L, Hoos E et al (2016) The data-driven factory. Leveraging big industrial data for agile, learning and human-centric manufacturing. Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS) 2016. SciTePress, pp 40–52

    Google Scholar 

  19. 19.

    Han J, Kamber M, Pei J (2012) Data mining. Concepts and techniques, 3rd edn. Morgan Kaufmann, Waltham

    Google Scholar 

  20. 20.

    Jeschke S, Brecher C, Meisen T et al (2017) Industrial internet of things and cyber manufacturing systems. In: Jeschke S, Brecher C, Song H, al (eds) Industrial internet of things. Cybermanufacturing systems. Springer, Cham, pp 3–20

    Google Scholar 

  21. 21.

    Kart L, Linden A, Schulte WR (2013) Extend your portfolio of analytics capabilities. Gartner research note G00254653

    Google Scholar 

  22. 22.

    Kassner L, Gröger C, Mitschang B et al (2014) Product life cycle analytics – next generation data analytics on structured and unstructured data. Procedia CIRP 33:35–40

    Article  Google Scholar 

  23. 23.

    Kemper H‑G, Baars H, Mehanna W (2010) Business intelligence, 3rd edn. Vieweg+Teubner, Wiesbaden

    Google Scholar 

  24. 24.

    Kemper H‑G, Baars H, Lasi H (2013) An integrated business intelligence framework. Closing the gap between IT support for management and for production. In: Rausch P, Sheta AF, Ayesh A (eds) Business intelligence and performance management. Theory, systems and industrial applications. Springer, London, pp 13–26

    Google Scholar 

  25. 25.

    Khatri V, Brown CV (2010) Designing data governance. Commun ACM 53(1):148–152

    Article  Google Scholar 

  26. 26.

    Knime (2017) Knime analytics platform. Accessed 01 Sept 2017

    Google Scholar 

  27. 27.

    Lee J, Kao H‑A, Yang S (2014) Service innovation and smart analytics for industry 4.0 and big data environment. Procedia CIRP 16:3–8

    Article  Google Scholar 

  28. 28.

    Loshin D (2013) Big data analytics. Morgan Kaufmann, Amsterdam

    Google Scholar 

  29. 29.

    Lv Z, Song H, Basanta-Val P et al (2017) Next-generation big data analytics. State of the art, challenges, and future research topics. IEEE Trans Industr Inform 13(4):1891–1899

    Article  Google Scholar 

  30. 30.

    Marz N, Warren J (2015) Big data. Principles and best practices of scalable realtime data systems. Manning, New York

    Google Scholar 

  31. 31.

    McAfee A, Brynjolfsson E (2012) Big data: the management revolution. Harv Bus Rev 90(10):60–68

    Google Scholar 

  32. 32.

    Microsoft (2017) Power BI. Accessed 01 Sept 2017

    Google Scholar 

  33. 33.

    Morgan L (2015) Citizen data scientists: 7 ways to harness talent. Accessed 01 Sept 2017

    Google Scholar 

  34. 34.

    OECD (2015) Data-driven innovation. Big data for growth and well-being. OECD, Paris

    Google Scholar 

  35. 35.

    O’Leary DE (2013) Artificial intelligence and big data. IEEE Intell Syst 28(2):96–99

    Article  Google Scholar 

  36. 36.

    RapidMiner (2017) RapidMiner data science platform. Accessed 01 Sept 2017

    Google Scholar 

  37. 37.

    Robert Bosch GmbH (2017) Bosch group. Accessed 01 Sept 2017

    Google Scholar 

  38. 38.

    SAP (2017) SAP predictive maintenance and service. Accessed 01 Sept 2017

    Google Scholar 

  39. 39.

    Shih W, Ludwig H (2016) The biggest challenges of data-driven manufacturing. Accessed 01 Sept 2017

    Google Scholar 

  40. 40.

    Soares S (2012) Big data governance. An emerging imperative. MC Press, Boise

    Google Scholar 

  41. 41.

    Stark J (2011) Product lifecycle management. 21st century paradigm for product realisation, 2nd edn. Springer, London

    Google Scholar 

  42. 42.

    Tableau (2017) Tableau software for business intelligence and analytics. Accessed 01 Sept 2017

    Google Scholar 

  43. 43.

    Tapadinhas J (2016) How to implement a modern business intelligence and analytics platform. Gartner Research Note ID G00291781

    Google Scholar 

  44. 44.

    Thompson JK, Rogers SP (2017) Analytics. How to win with intelligence. Technics Publications, Basking Ridge

    Google Scholar 

  45. 45.

    Topchyan AR (2016) Enabling data driven projects for a modern enterprise. Proc Inst Syst Program RAS 28(3):209–230

    Article  Google Scholar 

  46. 46.

    Weber C, Königsberger J, Kassner L et al (2017) M2DDM – a maturity model for data-driven manufacturing. Procedia CIRP 63:173–178

    Article  Google Scholar 

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The author would like to thank Dieter Neumann and Thomas Müller for their valuable comments and continuous support of this work.

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Correspondence to Christoph Gröger.

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Gröger, C. Building an Industry 4.0 Analytics Platform. Datenbank Spektrum 18, 5–14 (2018).

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  • Data analytics
  • Big data
  • Platform
  • Architecture
  • Citizen data scientist
  • Analytics governance
  • Industrie 4.0