The Data-driven Factory - Leveraging Big Industrial Data for Agile, Learning and Human-centric Manufacturing

Christoph Gröger, Laura Kassner, Eva Hoos, Jan Königsberger, Cornelia Kiefer, Stefan Silcher, Bernhard Mitschang


Global competition in the manufacturing industry is characterized by ever shorter product life cycles, increasing complexity and a turbulent environment. High product quality, continuously improved processes as well as changeable organizational structures constitute central success factors for manufacturing companies. With the rise of the internet of things and Industrie 4.0, the increasing use of cyber-physical systems as well as the digitalization of manufacturing operations lead to massive amounts of heterogeneous industrial data across the product life cycle. In order to leverage these big industrial data for competitive advantages, we present the concept of the data-driven factory. The data-driven factory enables agile, learning and human-centric manufacturing and makes use of a novel IT architecture, the Stuttgart IT Architecture for Manufacturing (SITAM), overcoming the insufficiencies of the traditional information pyramid of manufacturing. We introduce the SITAM architecture and discuss its conceptual components with respect to service-oriented integration, advanced analytics and mobile information provisioning in manufacturing. Moreover, for evaluation purposes, we present a prototypical implementation of the SITAM architecture as well as a real-world application scenario from the automotive industry to demonstrate the benefits of the data-driven factory.


  1. Aggarwal, C.C. and Zhai, C.X. (2012), “An Introduction to Text Mining”, in Aggarwal, C.C. and Zhai, C.X. (Eds.), Mining Text Data, Springer, Boston, pp. 1-10.
  2. Bracht, U., Hackenberg, W. and Bierwirth, T. (2011), “A monitoring approach for the operative CKD logistics”, Werkstattstechnik, Vol. 101 No. 3, pp. 122-127.
  3. Brettel, M., Friederichsen, N., Keller, M. and Rosenberg, M. (2014), “How Virtualization, Decentralization and Network Building Change the Manufacturing Landscape: An Industry 4.0 Perspective”, International Journal of Science, Engineering and Technology, Vol. 8 No. 1, pp. 37-44.
  4. Clevenger, N.C. (2011), iPad in the enterprise. Developing and deploying business applications, Wiley, Indianapolis.
  5. Daniel, F. and Matera, M. (2014), Mashups - Concepts, Models and Architectures. Data-Centric Systems and Applications, Springer, Heidelberg.
  6. Davis, J., Edgar, T., Porter, J., Bernaden, J. and Sarli, M.S. (2012), “Smart Manufacturing, Manufacturing Intelligence and Demand- Dynamic Performance”, Computers & Chemical Engineering, Vol. 47, pp. 145-156.
  7. Erl, T. (2008), SOA. Principles of service design, The Prentice Hall service-oriented computing series from Thomas Erl, Prentice Hall, Upper Saddle River.
  8. Evans, J.R. and Lindner, C.H. (2012), “Business Analytics: The Next Frontier for Decision Sciences”, Decision Line, Vol. 43 No. 2, pp. 4-6.
  9. Ferrucci, D. and Lally, D. (2004), “UIMA. An architectural approach to unstructured information processing in the corporate research environment”, Natural Language Engineering, Vol. 10 No. 3-4, pp. 327-348.
  10. Francese, R., Risi, M., Tortora, G. and Tucci, M. (2015), “Visual Mobile Computing for Mobile End-Users”, IEEE Transactions on Mobile Computing, to appear.
  11. Gölzer, P., Cato, P. and Amberg, M. (2015), “Data Processing Requirements of Industry 4.0 - Use Cases for Big Data Applications”, in Proceedings of the European Conference on Information Systems (ECIS) 2015, Paper 61.
  12. Gröger, C., Schwarz, H. and Mitschang, B. (2014a), “Prescriptive Analytics for Recommendation-Based Business Process Optimization”, in Proceedings of the International Conference on Business Information Systems (BIS) 2014, Springer, Cham, pp. 25-37.
  13. Gröger, C., Schwarz, H. and Mitschang, B. (2014b), “The Manufacturing Knowledge Repository. Consolidating Knowledge to Enable Holistic Process Knowledge Management in Manufacturing”, in Proceedings of the International Conference on Enterprise Information Systems (ICEIS) 2014, SciTePress, pp. 39-51.
  14. Groover, M.P. (2008), Automation, production systems, and computer-integrated manufacturing, 3rd ed., Prentice Hall, Upper Saddle River.
  15. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I.H. (2009), “The WEKA Data Mining Software: an Update”, SIGKDD Explorations, Vol. 11 No. 1, pp. 10-18.
  16. Hjelmervik, O.R. and Wang, K. (2006), “Knowledge Management in Manufacturing: The Soft Side of Knowledge Systems”, in Wang, K., Kovacs, G.L., Wozny, M. and Fang, M. (Eds.), Knowledge Enterprise: Intelligent Strategies in Product Design, Manufacturing, and Management, Vol. 207, Springer, pp. 89-94.
  17. Holtewert, P., Wutzke, R., Seidelmann, J. and Bauernhansl, T. (2013), “Virtual Fort Knox - Federative, secure and cloud-based platform for manufacturing”, in Proceedings of the CIRP Conference on Manufacturing Systems (CMS) 2013, Elsevier, pp. 527-532.
  18. Hoos, E., Gröger, C. and Mitschang, B. (2014), “Mobile Apps in Engineering: A Process-Driven Analysis of Business Potentials and Technical Challenges”, in Proceedings of the CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME) 2014, Procedia CIRP Vol. 33, Elsevier, pp. 17-22.
  19. ISA (2000), Enterprise-Control System Integration No. ISA-95.
  20. Kassner, L., Gröger, C., Mitschang, B. and Westkämper, E. (2014), “Product Life Cycle Analytics - Next Generation Data Analytics on Structured and Unstructured Data”, in Proceedings of the CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME) 2014, Procedia CIRP Vol. 33, Elsevier, p. 35-40.
  21. Kassner, L. and Mitschang, B. (2016), “Exploring Text Classification for Messy Data: An Industry Use Case for Domain-Specific Analytics”, in Proceedings of the International Conference on Extending Database Technology (EDBT) 2016,, to appear.
  22. Kemper, H.-G., Baars, H. and Lasi, H. (2013), “An Integrated Business Intelligence Framework. Closing the Gap Between IT Support for Management and for Production”, in Rausch, P., Sheta, A.F. and Ayesh, A. (Eds.), Business Intelligence and Performance Management. Theory, Systems and Industrial Applications, Advanced Information and Knowledge Processing, Springer, London, pp. 13-26.
  23. Königsberger, J., Silcher, S. and Mitschang, B. (2014), “SOA-GovMM: A meta model for a comprehensive SOA governance repository”, in Proceedings of the IEEE International Conference on Information Reuse and Integration (IRI) 2014, IEEE, pp. 187-194.
  24. MacDougall, W. (2014), “Industrie 4.0 - Smart Manufacturing for the Future”, available at: GTAI/Content/EN/Invest/_SharedDocs/Downloads/G TAI/Brochures/Industries/industrie4.0-smart-manufact uring-for-the-future-en.pdf (accessed 29.10.15).
  25. Meehan, M. (2014), “SOA adoption marked by broad failure and wild success”, available at: http://searchsoa.tec (accessed 28.10.15).
  26. Minguez, J., Lucke, D., Jakob, M., Constantinescu, C. and Mitschang, B. (2010), “Introducing SOA into Production Environments - The Manufacturing Service Bus”, in Proceedings of the 43rd CIRP International Conference on Manufacturing Systems (CMS), Neuer Wissenschaftlicher Verlag, Wien, pp. 1117-1124.
  27. NHTSA (2014), “NHTSA Data”, available at: (accessed 28.10.15).
  28. Ogren, P.V. and Bethard, S.J. (2009), “Building test suites for UIMA components”, in Proceedings of the Workshop on Software Engineering, Testing, and Quality Assurance for Natural Language Processing (SETQANLP), ACM, pp. 1-4.
  29. Papazoglou, M.P., Heuvel, W.-J.v. and Mascolo, J.E. (2015), “A Reference Architecture and KnowledgeBased Structures for Smart Manufacturing Networks”, IEEE Software, Vol. 32 No. 3, pp. 61-69.
  30. Sebastian-Coleman, L. (2013), Measuring data quality for ongoing improvement, Elsevier, Burlington.
  31. Shi, J., Wan, J., Yan, H. and Suo, H. (2011), “A survey of Cyber-Physical Systems”, in Proceedings of the International Conference on Wireless Communications and Signal Processing (WCSP), IEEE, Piscataway, pp. 1-6.
  32. Silcher, S., Dinkelmann, M., Minguez, J. and Mitschang, B. (2013), “Advanced Product Lifecycle Management by Introducing Domain-Specific Service Buses”, in Cordeiro, J., Maciaszek, L.A. and Filipe, J. (Eds.), Enterprise Information Systems (ICEIS) 2013. Revised Selected Papers, Lecture Notes in Business Information Processing, Vol. 141, Springer Berlin, pp. 92-107.
  33. Vogel-Heuser, B., Kegel, G., Bender, K. and Wucherer, K. (2009), “Global information architecture for industrial automation”, Automatisierungstechnische Praxis, Vol. 51 No. 01-02, pp. 108-115.
  34. Wang, R.Y. and Strong, D.M. (1996), “Beyond accuracy: what data quality means to data consumers”, Journal of Management Information Systems, Vol. 12 No. 4, pp. 5-33.
  35. Westkämper, E. (2014), Towards the Re-Industrialization of Europe. A concept for manufacturing for 2030, Springer, Berlin.
  36. Whitman, M.E. and Mattord, H.J. (2007), Principles of information security, 3rd ed., Thomson Course Technology, Boston.
  37. Zuehlke, D. (2010), “SmartFactory - Towards a factory-ofthings”, Annual Reviews in Control, Vol. 34 No. 1, pp. 129-138.
  38. ZVEI (2015), “The Reference Architectural Model Industrie 4.0 (RAMI 4.0)”, available at: dustrie-40-RAMI-40-English.pdf (accessed 28.09.15).

Paper Citation

in Harvard Style

Gröger C., Kassner L., Hoos E., Königsberger J., Kiefer C., Silcher S. and Mitschang B. (2016). The Data-driven Factory - Leveraging Big Industrial Data for Agile, Learning and Human-centric Manufacturing . In Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-187-8, pages 40-52. DOI: 10.5220/0005831500400052

in Bibtex Style

author={Christoph Gröger and Laura Kassner and Eva Hoos and Jan Königsberger and Cornelia Kiefer and Stefan Silcher and Bernhard Mitschang},
title={The Data-driven Factory - Leveraging Big Industrial Data for Agile, Learning and Human-centric Manufacturing},
booktitle={Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},

in EndNote Style

JO - Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - The Data-driven Factory - Leveraging Big Industrial Data for Agile, Learning and Human-centric Manufacturing
SN - 978-989-758-187-8
AU - Gröger C.
AU - Kassner L.
AU - Hoos E.
AU - Königsberger J.
AU - Kiefer C.
AU - Silcher S.
AU - Mitschang B.
PY - 2016
SP - 40
EP - 52
DO - 10.5220/0005831500400052