An Integrated Business Intelligence Framework

Information Technology (IT) support in the manufacturing sector has reached a watershed with digital components beginning to permeate all products and processes. The classical divide between “technical” IT and “business” IT begins to blend more and more. Data from design, manufacturing, product use, service, and support is made available across the complete product lifecycle and supply chain. This goes hand in hand with the diffusion of sensor and identification technology and the availability of relevant information streams on the customer side—leading to unprecedented amounts of data. The challenge is to purposefully apply emerging BI concepts for a comprehensive decision support that integrates product and shop floor design phases, the steering and design of operational industrial processes, as well as big and unstructured data sources. This chapter brings those pieces together in order to derive an integrated framework for management and decision support in the manufacturing sector.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic €32.70 /Month

Buy Now

Price includes VAT (France)

eBook EUR 85.59 Price includes VAT (France)

Softcover Book EUR 116.04 Price includes VAT (France)

Hardcover Book EUR 105.49 Price includes VAT (France)

Tax calculation will be finalised at checkout

Purchases are for personal use only

Similar content being viewed by others

Towards an Integrated Decision-Support Framework for the New Generation of Manufacturing Systems

Chapter © 2021

Big Data Analysis to Ease Interconnectivity in Industry 4.0—A Smart Factory Perspective

Chapter © 2017

Developing Real-Time Smart Industrial Analytics for Industry 4.0 Applications

Chapter © 2020

References

  1. ActionPlanT: ICT for manufacturing—the ActionPlanT roadmap for manufacturing 2.0 (2012). Available via http://www.actionplant-project.eu/public/documents/roadmap.pdf. Accessed 25th July 2012
  2. Alter, A.: Business intelligence—are your BI systems making you smarter? CIO Insight 05/2003, 77–85 (2003) Google Scholar
  3. Baars, H., Kemper, H.G.: Management support with structured and unstructured data—an integrated business intelligence framework. Inf. Syst. Manag. 25(2), 132–148 (2008) ArticleGoogle Scholar
  4. Baars, H., Kemper, H.G.: Ubiquitous computing—an application domain for business intelligence in the cloud? In: Proceedings of the 17th Americas Conference on Information Systems (AMCIS), USA (2011) Google Scholar
  5. Baars, H., Qie, L.: BI in the Cloud – Die Cloud als neuer Ansatz zur Erhöhung der BI-Agilität? BI Spektrum 7(2), 26–29 (2012) Google Scholar
  6. Baars, H., Sun, X.: Multidimensional analysis of RFID data in logistics. In: Proceedings of the 42th Hawaii International Conference on System Sciences (HICSS-42), USA (2009) Google Scholar
  7. Baars, H., Gille, D., Strüker, J.: Evaluation of RFID applications for logistics: a framework for identifying, forecasting and assessing benefits. Eur. J. Inf. Syst. (EJIS) 18(6), 578–591 (2009) ArticleGoogle Scholar
  8. Baars, H., Kemper, H.G., Lasi, H., Siegel, M.: Combining RFID technology and business intelligence for supply chain optimization—scenarios for retail logistics. In: Proceedings of the 41th Hawaii International Conference on System Sciences (HICSS-41), USA (2008) Google Scholar
  9. Bottani, E., Bertolini, M., Montanari, R., Volpi, A.: RFID-enabled business intelligence modules for supply chain optimization. Int. J. Technol.: Res. Appl. 1(4), 253–278 (2009) Google Scholar
  10. Brinkmann, A., Effert, S., Heidebuer, M., Vodisek, M., Baars, H.: An integrated architecture for business intelligence support from application down to storage. In: Proceedings of the 3rd International Workshop on Storage Network Architecture and Parallel I/Os, Saint Louis, USA (2005) Google Scholar
  11. Bucher, T., Gericke, A.: Process-centric business intelligence. Bus. Process. Manag. J. 15(3), 408–429 (2009) ArticleGoogle Scholar
  12. Cattell, R.: Scalable SQL and NoSQL data stores. SIGMOD Rec. 39(4), 12–27 (2010) ArticleGoogle Scholar
  13. Cho, D.Y.: Ubiquitous data warehouse—integrating RFID with multidimensional online analysis. In: Proceedings of the San Diego International Systems Conference, San Diego (2005) Google Scholar
  14. Chow, H.K.H., Choy, K.L., Lee, W.B., Chan, F.T.S.: Design of a knowledge-based logistics strategy system. Expert Syst. Appl. 29, 272–290 (2005) ArticleGoogle Scholar
  15. Curtin, J., Kauffman, R.J., Riggins, F.J.: Making the ‘Most’ out of RFID technology: a research agenda for the study of the adoption, usage and impact of RFID. Inf. Technol. Manag. 8(2), 87–110 (2007) ArticleGoogle Scholar
  16. Dayal, U., Hsu, M., Ladin, R.: Business process coordination: state of the art, trends, and open issues. In: Proceedings of the 27th International Conference on Very Large Data Bases (VLDB), Italy (2001) Google Scholar
  17. Eckerson, W.E.: Best practices in operational BI—converging analytical and operational processes. In: TDWI best practice report, 3rd quarter 2007 (2007) Google Scholar
  18. Ehrlenspiel, K.: Integrierte Produktentwicklung, 3rd edn. Hanser, München (2007) Google Scholar
  19. Eigner, M., Stelzer, R.: Product Lifecycle Management, 2nd edn. Springer, Heidelberg (2009) BookGoogle Scholar
  20. Fleisch, E.: What is the internet of things? An economic perspective. In: Auto-ID Labs white paper (WP-BIZAPP-053), Auto-ID Labs, St. Gallen (2010) Google Scholar
  21. Giannakakis, T., Vosniakos, G.C.: Sheet metal cutting and piercing operations planning and tools configuration by an expert system. Int. J. Adv. Manuf. Technol. 36(7–8), 658–670 (2008) ArticleGoogle Scholar
  22. Golfarelli, M., Rizzi, S., Cella, I.: Beyond data warehousing: what’s next in business intelligence? In: Proceedings of the 7th ACM International Workshop on Data Warehousing and OLAP, USA (2004) Google Scholar
  23. Grigoria, D., Casatib, F., Castellanosb, M., Dayalb, U., Sayalb, M., Shan, S.C.: Business process intelligence. Comput. Ind. 53, 321–343 (2004) ArticleGoogle Scholar
  24. GS1: EPCglobal standards (2012). Available via http://www.gs1.org/gsmp/kc/epcglobal. Accessed 25 July 2012
  25. Günther, O., Kletti, W., Kurbach, U.: RFID in Manufacturing. Springer, Heidelberg (2008) Google Scholar
  26. Jacobs, A.: The pathologies of big data. Commun. ACM 52(8), 36–44 (2009) ArticleGoogle Scholar
  27. Kemper, H.G., Baars, H.: From data warehouses to transformation hubs—a conceptual architecture. In: Proceedings of the 17th European Conference on Information Systems (ECIS), Italy (2009) Google Scholar
  28. Kemper, H.G., Baars, H., Mehanna, W.: Business Intelligence – Grundlagen und praktische Anwendungen, 3rd edn. Vieweg, Wiesbaden (2010) BookGoogle Scholar
  29. Kendal, S.: An Introduction to Knowledge Engineering. Springer, London (2007) MATHGoogle Scholar
  30. Klawans, B.: Embedded or conventional BI—determining the right combination of BI for your business. Bus. Intell. J. 13(1), 30–36 (2008) Google Scholar
  31. Kletti, J.: Manufacturing Execution System: MES. Springer, Heidelberg (2007) BookGoogle Scholar
  32. Koch, M., Baars, H., Lasi, H., Kemper, H.G.: Manufacturing execution systems and business intelligence for production environments. In: Proceedings of the 16th Americas Conference on Information Systems, Peru (2010) Google Scholar
  33. Lasi, H.: Industrial intelligence—a BI-based approach to enhance manufacturing engineering in industrial companies. In: Proceedings of the 8th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME), Italy (2012) Google Scholar
  34. Lasi, H.: Decision support within knowledge-based engineering—a business intelligence-based concept. In: Proceedings of the 18th Americas Conference on Information Systems (AMCIS), USA (2012) Google Scholar
  35. Lasi, H., Hollstein, P., Kemper, H.G.: Heterogeneous IT landscapes in innovation processes—an empirical analyses of integration approaches. In: Proceedings of the International Conference Information Systems (IADIS), Portugal (2010) Google Scholar
  36. Lyytinnen, K., Yoo, Y.: Issues and challenges in ubiquitous computing. Commun. ACM 45(12), 42–65 (2002) ArticleGoogle Scholar
  37. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big Data: the Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute (2011) Google Scholar
  38. Marjanovic, O.: The next stage of operational business intelligence: creating new challenges for business process management. In: Proceedings of the 40th Annual Hawaii International Conference on System Sciences. IEEE Comput. Soc., New York (2007) Google Scholar
  39. Mell, P., Grance, T.: The NIST definition of cloud computing. National Institute of Standards and Technology, Special Publication 800-145 (2011) Google Scholar
  40. Platter, H.: A common database approach for OLTP and OLAP using an in-memory column database. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, Providence, USA (2009) Google Scholar
  41. Plattner, H., Zeier, A.: In-Memory Data Management: An Inflection Point for Enterprise Applications. Springer, Heidelberg (2011) Google Scholar
  42. Pourshahid, A., Chen, P., Amyot, D., Weiss, M., Forster, A.J.: Business process monitoring and alignment: an approach based on the user requirements notation and business intelligence tools. In: Proceedings of the 10th Workshop of Requirement Engineering, Canada, pp. 149–159 (2007) Google Scholar
  43. Röhner, S., Breitsprecher, T., Wartzack, S.: Acquisition of design-relevant knowledge within the development of sheet-bulk metal forming. In: Proceedings of the International Conference on Engineering Design (ICED11), Denmark (2011) Google Scholar
  44. Song, M., van der Aalst, W.M.P.: Towards comprehensive support for organizational mining. Decis. Support Syst. 46(11), 300–317 (2008) ArticleGoogle Scholar
  45. Strauch, C.: NoSQL databases (2011). Available via http://www.christof-strauch.de/nosqldbs.pdf. Accessed 25 July 2012
  46. Thomson, W.J.J., van der Walt, J.S.: Business intelligence in the cloud. South African J. Inf. Manag. 12(1), 1–5 (2010) Google Scholar
  47. van der Aalst, W.M.P., Weijters, J.M.M.: Process mining: a research agenda. Comput. Ind. 53(3), 231–244 (2004) ArticleGoogle Scholar
  48. Weber, P.: Digital Mock-up im Maschinenbau. Shaker, Aachen (2003) Google Scholar
  49. Weiser, M.: Ubiquitous computing (1996). Available via: http://sandbox.xerox.com/ubicomp. Accessed 25th July 2012
  50. zur Mühlen, M.: Process-driven management information systems—combining data warehouses and workflow technology. In: Proceedings of the 4th International Conference on Electronic Commerce Research (ICECR-4), USA, pp. 550–566 (2001) Google Scholar

Author information

Authors and Affiliations

  1. Chair of Information Systems I, University of Stuttgart, Stuttgart, Germany Hans-Georg Kemper, Henning Baars & Heiner Lasi
  1. Hans-Georg Kemper