Datenbank-Spektrum

, Volume 18, Issue 1, pp 5–14 | Cite as

Building an Industry 4.0 Analytics Platform

Practical Challenges, Approaches and Future Research Directions
Schwerpunktbeitrag

Abstract

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.

Keywords

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

Notes

Acknowledgements

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

References

  1. 1.
    Apache Software Foundation (2017) Apache hadoop. http://hadoop.apache.org/. Accessed 01 Sept 2017Google 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 5–35CrossRefGoogle Scholar
  4. 4.
    Bose R (2009) Advanced analytics: opportunities and challenges. Ind Manag Data Syst 109(2):155–172CrossRefGoogle 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):37–44Google Scholar
  6. 6.
    Burton B, Walker MJ (2015) Gartner hype cycle for emerging technologiesGoogle Scholar
  7. 7.
    Chamoni P, Gluchowski P (2017) Business analytics – state of the art. Controll Manag Rev 61(4):8–17CrossRefGoogle Scholar
  8. 8.
    Davenport TH, Harris JG (2017) Competing on analytics. The new science of winning. Harvard Business Review Press, BostonGoogle 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 114–122Google 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 1112–1121Google Scholar
  11. 11.
    Gandomi A, Haider M (2015) Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manage 35(2):137–144CrossRefGoogle Scholar
  12. 12.
    GE (2017) Predix platform. https://www.ge.com/digital/predix/platform. Accessed 01 Sept 2017Google 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 61Google Scholar
  14. 14.
    Grochow J (2012) IT infrastructure to support analytics: laying the groundwork for institutional analytics. EDUCAUSE Research BulletinGoogle Scholar
  15. 15.
    Gröger C (2015) Advanced Manufacturing Analytics – Datengetriebene Optimierung von Fertigungsprozessen. Josef Eul, LohmarGoogle 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 138–140Google 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 39–51Google 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 40–52Google Scholar
  19. 19.
    Han J, Kamber M, Pei J (2012) Data mining. Concepts and techniques, 3rd edn. Morgan Kaufmann, WalthamMATHGoogle 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 3–20CrossRefGoogle Scholar
  21. 21.
    Kart L, Linden A, Schulte WR (2013) Extend your portfolio of analytics capabilities. Gartner research note G00254653Google 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 33:35–40CrossRefGoogle Scholar
  23. 23.
    Kemper H‑G, Baars H, Mehanna W (2010) Business intelligence, 3rd edn. Vieweg+Teubner, WiesbadenGoogle 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 13–26CrossRefGoogle Scholar
  25. 25.
    Khatri V, Brown CV (2010) Designing data governance. Commun ACM 53(1):148–152CrossRefGoogle Scholar
  26. 26.
    Knime (2017) Knime analytics platform. http://www.knime.com. Accessed 01 Sept 2017Google 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 16:3–8CrossRefGoogle Scholar
  28. 28.
    Loshin D (2013) Big data analytics. Morgan Kaufmann, AmsterdamMATHGoogle 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):1891–1899CrossRefGoogle Scholar
  30. 30.
    Marz N, Warren J (2015) Big data. Principles and best practices of scalable realtime data systems. Manning, New YorkGoogle Scholar
  31. 31.
    McAfee A, Brynjolfsson E (2012) Big data: the management revolution. Harv Bus Rev 90(10):60–68Google Scholar
  32. 32.
    Microsoft (2017) Power BI. http://powerbi.microsoft.com/. Accessed 01 Sept 2017Google Scholar
  33. 33.
  34. 34.
    OECD (2015) Data-driven innovation. Big data for growth and well-being. OECD, ParisGoogle Scholar
  35. 35.
    O’Leary DE (2013) Artificial intelligence and big data. IEEE Intell Syst 28(2):96–99CrossRefGoogle Scholar
  36. 36.
    RapidMiner (2017) RapidMiner data science platform. http://www.rapidminer.com. Accessed 01 Sept 2017Google Scholar
  37. 37.
    Robert Bosch GmbH (2017) Bosch group. http://www.bosch.com/bosch-group/. Accessed 01 Sept 2017Google Scholar
  38. 38.
    SAP (2017) SAP predictive maintenance and service. https://www.sap.com/products/predictive-maintenance.html. Accessed 01 Sept 2017Google Scholar
  39. 39.
    Shih W, Ludwig H (2016) The biggest challenges of data-driven manufacturing. https://hbr.org/2016/05/the-biggest-challenges-of-data-driven-manufacturing. Accessed 01 Sept 2017Google Scholar
  40. 40.
    Soares S (2012) Big data governance. An emerging imperative. MC Press, BoiseGoogle Scholar
  41. 41.
    Stark J (2011) Product lifecycle management. 21st century paradigm for product realisation, 2nd edn. Springer, LondonGoogle Scholar
  42. 42.
    Tableau (2017) Tableau software for business intelligence and analytics. http://www.tableau.com. Accessed 01 Sept 2017Google Scholar
  43. 43.
    Tapadinhas J (2016) How to implement a modern business intelligence and analytics platform. Gartner Research Note ID G00291781Google Scholar
  44. 44.
    Thompson JK, Rogers SP (2017) Analytics. How to win with intelligence. Technics Publications, Basking RidgeGoogle Scholar
  45. 45.
    Topchyan AR (2016) Enabling data driven projects for a modern enterprise. Proc Inst Syst Program RAS 28(3):209–230CrossRefGoogle Scholar
  46. 46.
    Weber C, Königsberger J, Kassner L et al (2017) M2DDM – a maturity model for data-driven manufacturing. Procedia CIRP 63:173–178CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature 2018

Authors and Affiliations

  1. 1.Robert Bosch GmbHGerlingen-SchillerhöheGermany

Personalised recommendations