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Prescriptive Control of Business Processes

New Potentials Through Predictive Analytics of Big Data in the Process Manufacturing Industry

Business & Information Systems Engineering volume 58, pages 261–280 (2016)Cite this article

Abstract

This paper proposes a concept for a prescriptive control of business processes by using event-based process predictions. In this regard, it explores new potentials through the application of predictive analytics to big data while focusing on production planning and control in the context of the process manufacturing industry. This type of industry is an adequate application domain for the conceived concept, since it features several characteristics that are opposed to conventional industries such as assembling ones. These specifics include divergent and cyclic material flows, high diversity in end products’ qualities, as well as non-linear production processes that are not fully controllable. Based on a case study of a German steel producing company – a typical example of the process industry – the work at hand outlines which data becomes available when using state-of-the-art sensor technology and thus providing the required basis to realize the proposed concept. However, a consideration of the data size reveals that dedicated methods of big data analytics are required to tap the full potential of this data. Consequently, the paper derives seven requirements that need to be addressed for a successful implementation of the concept. Additionally, the paper proposes a generic architecture of prescriptive enterprise systems. This architecture comprises five building blocks of a system that is capable to detect complex event patterns within a multi-sensor environment, to correlate them with historical data and to calculate predictions that are finally used to recommend the best course of action during process execution in order to minimize or maximize certain key performance indicators.

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References

  • Aggarwal C (2012) An introduction to sensor data analytics. In: Aggarwal C (ed) Managing and mining sensor data. Springer, New York, pp 1–8

    Google Scholar 

  • Akerkar B (2013) Advanced data analytics for business. In: Akerkar B (ed) Big data computing. CRC Press, Boca Raton, pp 373–397

    Chapter  Google Scholar 

  • Allwood J, Cullen J (2012) Sustainable materials with both eyes open. UIT Cambridge, Cambridge

    Google Scholar 

  • Benbasat I, Goldstein D, Mead M (1987) The case research strategy in studies of information systems. MIS Q 11(3):369–386

    Article  Google Scholar 

  • BITKOM (2012) Big Data im Praxiseinsatz – Szenarien, Beispiele. Effekte, BITKOM, Berlin

    Google Scholar 

  • Bruns R, Dunkel J (2010) Event-Driven Architecture. Springer, Berlin

    Book  Google Scholar 

  • Buhl H, Röglinger M, Moser F, Heidemann J (2013) Big data. A fashionable topic with(out) sustainable relevance for research and practice? Bus Inf. Syst Eng 5(2):65–69

    Google Scholar 

  • Camacho E, Bordons C (2007) Model predictive control. Springer, London

    Book  Google Scholar 

  • Chan KH, Dozal-Mejorada EJ, Cheng X, Kephart R, Ydstie BE (2014) Predictive control with adaptive model maintenance: application to power plants. Comput Chem Eng 70(1):91–103

    Article  Google Scholar 

  • Darke P, Shanks G, Broadbent M (1998) Successfully completing case study research: combining rigour, relevance and pragmatism. Inf Syst J 8(4):273–289

    Article  Google Scholar 

  • Dhar V (2013) Data science and prediction. Commun ACM 56(12):64–73

    Article  Google Scholar 

  • Dhar V, Jarke M, Laartz J (2014) Big data. Bus Inf Syst Eng 6(5):257–259

    Article  Google Scholar 

  • Dixon J, Jones T (2011) Hype cycle for business process management, 2011. https://www.gartner.com/doc/1751119. Accessed 28 May 2015

  • Eckert M, Bry F (2009) Complex event processing (CEP). Informatik-Spektrum 32(2):163–167

    Article  Google Scholar 

  • Elghoneimy E, Gruver W (2011) Intelligent decision support and agent-based techniques applied to wood manufacturing. In: International symposium on distributed computing and artificial intelligence 91(1):85–88

  • Engel Y, Etzion O (2011) Towards proactive event-driven computing. In: Proceedings of the 5th ACM international conference on distributed event-based system. New York

  • Engel Y, Etzion O, Feldman Z (2012) A basic model for proactive event-driven computing. In: Proceedings of the 6th ACM international conference on distributed event-based systems, Berlin

  • Etzion O, Niblett P (2011) Event processing in action. Manning Publications, Stamford

    Google Scholar 

  • Evans JR, Lindner CH (2012) Business analytics. Decis Line 43(2):4–6

    Google Scholar 

  • Fischer K, Jacobi S, Diehl C, Theis C (2004) Multiagent technologies for steel production and control. In: Proceedings of the IEEE/WIC/ACM international conference on intelligent agent technology, Beijing

  • Fülöp L, Beszédes Á, Tóth G, Demeter H, Vidács L, Farkas L (2012) Predictive complex event processing. In: Proceedings of the 5th Balkan conference in informatics, Novi Sad

  • Goode K, Moore J, Roylance B (2000) Plant machinery working life prediction method utilizing reliability and condition-monitoring data. J Process Mech Eng 214(2):109–122

    Article  Google Scholar 

  • Graichen K, Egretzberger M, Kugi A (2010) Ein suboptimaler Ansatz zur schnellen modellprädiktiven Regelung nichtlinearer Systeme. Automatisierungstechnik 58(8):447–456

    Article  Google Scholar 

  • Gröger C, Schwarz H, Mitschang B (2014) Prescriptive analytics for recommendation-based business process optimization. In: Abramowicz W, Kokkinaki A (eds) Business information systems. Springer, Heidelberg, pp 25–37

    Chapter  Google Scholar 

  • Hahn D, Lassmann G (1999) Produktionswirtschaft – Controlling industrieller Produktion. Physica-Verlag, Heidelberg

    Book  Google Scholar 

  • Harjunkoski I, Maravelias CT, Bongers P, Castro PM, Engell S, Grossmann IE, Hooker J, Méndez C, Sand G, Wassick J (2014) Scope for industrial applications of production scheduling models and solution methods. Comput Chem Eng 62(1):161–193

    Article  Google Scholar 

  • Heng A, Tan ACC, Mathew J, Montgomery N, Banjevic D, Jardine AKS (2009) Intelligent condition-based prediction of machinery reliability. Mech Syst Signal Process 23(5):1600–1614

    Article  Google Scholar 

  • Hevner AR, March ST, Park J, Ram S (2004) Design science in information systems research. MIS Q 28(1):75–105

    Google Scholar 

  • International Electrotechnical Commission (2013) IEC 62264-1:2013 – enterprise-control system integration. IEC, Geneva

    Google Scholar 

  • Jacobi S, León-Soto E, Madrigal-Mora C, Fischer K (2007) MasDISPO: a multiagent decision support system for steel production and control. In: Proceedings of the 19th National conference on innovative applications of artificial intelligence, Vancouver, pp 1707–1714

  • Jahangirian M, Eldabi T, Naseer A, Stergioulas LK, Young T (2010) Simulation in manufacturing and business: a review. Eur J Oper Res 203(1):1–13

    Article  Google Scholar 

  • Janiesch C, Matzner M, Müller O (2012) Beyond process monitoring: a proof-of-concept of event-driven business activity management. Bus Process Manag J 18(4):625–643

    Article  Google Scholar 

  • Jardine A, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20(7):1483–1510

    Article  Google Scholar 

  • Jarke M (2014) Interview with Michael Feindt on “Prescriptive big data analytics”. Bus Inf Syst Eng 6(5):301–302

    Article  Google Scholar 

  • Kagermann H, Lukas W, Wahlster W (2011) Industrie 4.0: Mit dem Internet der Dinge auf dem Weg zur 4. industriellen Revolution. In: VDI Nachrichten 13. VDI Verlag, Düsseldorf

  • Kittisupakorn P, Thitiyasook P, Hussain M, Daosud W (2009) Neural network based model predictive control for a steel pickling process. J Process Control 19(4):579–590

    Article  Google Scholar 

  • Konrad B, Lieber D, Deuse J (2012) Striving for zero defect production: intelligent manufacturing control through data mining in continuous rolling mill processes. In: Windt K (ed) Robust manufacturing control. Springer, Heidelberg, pp 215–229

    Google Scholar 

  • Kowalczyk M, Buxmann P (2014) Big Data and information processing in organizational decision processes. A multiple case study. Bus Inf Syst Eng 6(5):267–278

    Article  Google Scholar 

  • Krumeich J, Jacobi S, Werth D, Loos P (2014a) Big data analytics for predictive manufacturing control – a case study from process industry. In: Proceedings of the IEEE 3rd International congress on big data, Anchorage, pp 530–537

  • Krumeich J, Jacobi S, Werth D, Loos P (2014b) Towards planning and control of business processes based on event-based predictions. In: Abramowicz W, Kokkinaki A (eds) Business information systems. Springer, Heidelberg, pp 38–49

    Chapter  Google Scholar 

  • Krumeich J, Schimmelpfennig J, Werth D, Loos P (2014c) Realizing the predictive enterprise through intelligent process predictions based on big data analytics: a case study and architecture proposal. Köllen, Bonn, pp 1253–1264

    Google Scholar 

  • Krumeich J, Weis B, Werth D, Loos P (2014d) Event-driven business process management: where are we now? Bus Process Manag J 20(4):615–633

    Article  Google Scholar 

  • Krumeich J, Werth D, Loos P, Schimmelpfennig J, Jacobi S (2014e) Advanced planning and control of manufacturing processes in steel industry through big data analytics: case study and architecture proposal. In: Proceedings of the 2014 IEEE international conference on big data, Washington, pp 16–24

  • Krumeich J, Werth D, Loos P (2015a) Enhancing organizational performance through event-based process predictions. In: Proceedings of the 21st Americas conference on information systems, Puerto Rico, pp 1–12

  • Krumeich J, Mehdiyev N, Werth D, Loos P (2015b) Towards an extended metamodel of event-driven process chains to model complex event patterns. In: Jeusfeld M, Karlapalem K (eds) Advances in conceptual modeling. Springer, Heidelberg, pp 119–130

  • Kurbel K (2005) Produktionsplanung und -steuerung im Enterprise Resource Planning und Supply Chain Management, 6th edn. Oldenbourg, München

    Book  Google Scholar 

  • Lasi H, Fettke P, Kemper H-G, Feld T, Hoffman M (2014) Industry 4.0. Bus Inf. Syst Eng 6(4):239–242

    Google Scholar 

  • Loos P (1997) Produktionslogistik in der chemischen Industrie. Gabler, Wiesbaden

    Google Scholar 

  • Loos P, Allweyer T (1998) Application of production planning and scheduling in the process industries. Comput Ind 36(3):199–208

    Article  Google Scholar 

  • Luckham D (2002) The power of events. Addison-Wesley, Boston

    Google Scholar 

  • Luckham D (2012) Event processing for business. Wiley, Hoboken

    Book  Google Scholar 

  • Lundberg A (2006) Leverage complex event processing to improve operational performance. Bus Intell J 11(1):55–65

    Google Scholar 

  • Margara A, Cugola G, Tamburrelli G (2014) Learning from the past: automated rule generation for complex event processing. In: Proceedings of the 8th ACM international conference on distributed event-based systems, Mumbai, pp 47–58

  • May C (1996) PPS mit Neuronalen Netzen. Deutscher Universitäts-Verlag, Wiesbaden

    Book  Google Scholar 

  • Mehdiyev N, Krumeich J, Werth D, Loos P (2015a) Sensor event mining with hybrid ensemble learning and evolutionary feature subset selection model. In: Proceedings of the 2015 IEEE international conference on big data, Santa Clara

  • Mehdiyev N, Krumeich J, Enke D, Werth D, Loos P (2015b) Determination of rule patterns in complex event processing using machine learning techniques. Procedia Comput Sci 61(1):395–401

    Article  Google Scholar 

  • Mehdiyev N, Krumeich J, Werth D, Loos P (2016) Determination of event patterns for complex event processing using unordered fuzzy rule induction with multi-objective evolutionary feature subset selection. In: Proceedings of the 49th Hawaii international conference on system sciences. Kauai

  • Metz D, Karadgi S, Müller U, Grauer M (2012) Self-learning monitoring and control of manufacturing processes based on rule induction and event processing. In: Proceedings of the 4th international conference on information, process, and knowledge management. Valencia, pp 88–92

  • Minelli M, Chambers M, Dhiraj A (2013) Big data, big analytics. Emerging business intelligence and analytic trends for today’s businesses. Wiley, Hoboken

    Book  Google Scholar 

  • Niamsuwan S, Kittisupakorn P, Mujtaba I (2014) Control of milk pasteurization process using model predictive approach. Comput Chem Eng 66(1):2–11

    Article  Google Scholar 

  • Pettey C, Goasduff L (2011) Gartner says between now and year-end 2014, overlooked but easily detectable business process defects will topple 10 global 2000 companies. http://www.gartner.com/newsroom/id/1530114. Accessed 28 May 2015

  • Rainer C (2013) Anwendung von Data-Mining zur Abbildung von Planungsregeln in der flexibilitätsorientierten Prozessindustrie. In: Zsifkovits H, Altendorfer S (eds) Logistics Systems Engineering. Hampp, Mering, pp 45–58

    Google Scholar 

  • Rapp W (2002) Information technology strategies. Oxford University Press, Oxford

    Book  Google Scholar 

  • Redlich D, Gilani W (2012) Event-driven process centric performance prediction via simulation. Springer, Berlin, pp 473–478

    Google Scholar 

  • Riebel P (1963) Industrielle Erzeugungsverfahren in betriebswirtschaftlicher Sicht. Gabler, Wiesbaden

    Book  Google Scholar 

  • Scheer A-W (1998) Wirtschaftsinformatik: Referenzmodelle für industrielle Geschäftsprozesse, 2nd edn. Springer, Berlin

    Google Scholar 

  • Schlegel K, Sallam R, Yuen D, Tapadinhas J (2013) Magic quadrant for business intelligence and analytics platforms. https://www.gartner.com/doc/2326815/magic-quadrant-business-intelligence-analytics. Accessed 28 May 2015

  • Schwegmann B, Matzner M, Janiesch C (2013) A method and tool for predictive event-driven process analytics. In: Proceedings of the 11th International Conference on Wirtschaftsinformatik, vol 1, Leipzig, pp 721–736

  • Shobrys D, White D (2002) Planning, scheduling and control systems: why cannot they work together. Comput Chem Eng 26(2):149–160

    Article  Google Scholar 

  • Statista (2015) Stahlproduzenten nach Produktionsmenge in Deutschland 2014. http://de.statista.com/statistik/daten/studie/153022/umfrage/die-groessten-stahlproduzenten-nach-produktionsmenge-in-deutschland/. Accessed 29 May 2015

  • United Nations (2008) International standard industrial classification of all economic activities, rev 4. United Nations Publications, New York

    Google Scholar 

  • Unni K (2012) Steel manufacturing could use more sensing and analysis. http://www.sensorsmag.com/process-industries/steel-manufacturing-could-use-more-sensing-and-analysis-10249. Accessed 28 May 2015

  • van der Aalst et al. (2012) Process mining manifesto. In: Daniel F et al (eds) Business process management workshops. Springer, Heidelberg, pp 169–194

  • Veldman J, Klingenberg W, Wortmann H (2011) Managing condition-based maintenance technology. J Qual Maint Eng 17(1):40–62

    Article  Google Scholar 

  • Viaene S (2013) Data scientists aren’t domain experts. IT Professional 15(6):12–17

    Article  Google Scholar 

  • Vidačković K (2014) Eine Methode zur Entwicklung dynamischer Geschäftsprozesse auf Basis von Ereignisverarbeitung, PhD thesis. Universität Stuttgart, Stuttgart

  • von Ammon R, Ertlmaier T, Etzion O, Kofman A, Paulus P (2010) Integrating complex events for collaborating and dynamically changing business processes. In: Dan A et al (eds) Service-oriented computing. ICSOC/ServiceWave 2009 workshops. Springer, Heidelberg, pp 370–384

  • Widder A, von Ammon R, Schaeffer P, Wolff C (2007) Identification of suspicious, unknown event patterns in an event cloud. In: Proceedings of the 2007 Inaugural international conference on distributed event-based systems. Toronto, pp 164–170

  • Wortmann F, Flüchter K (2015) Internet of things. Technology and value added. Bus Inf Syst Eng 57(3):221–224

    Article  Google Scholar 

  • Yonder Blue (2013) Industrial Big Data: Die Zukunft kennen – Prozesse automatisieren. Blue Yonder, Karlsruhe

    Google Scholar 

  • Yam R, Tse P, Li L, Tu P (2001) Intelligent predictive decision support system for condition-based maintenance. Int J Adv Manuf Technol 17(5):383–391

    Article  Google Scholar 

  • Zhu D, Zheng Z, Gao X (2010) Intelligent optimization-based production planning and simulation analysis for steelmaking and continuous casting process. J Iron Steel Res Int 17(9):19–30

    Article  Google Scholar 

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Acknowledgments

This research was funded in part by the German Federal Ministry of Education and Research under grant numbers 01IS12050 (project IDENTIFY) and 01IS14004A (project iPRODICT).

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Authors and Affiliations

  1. Institute for Information Systems (IWi), German Research Center for Artificial Intelligence (DFKI GmbH), Saarland University, Campus Bld. D3 2, Stuhlsatzenhausweg 3, 66123, Saarbrücken, Germany

    Julian Krumeich,  Dirk Werth &  Peter Loos

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  1. Julian Krumeich
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  2. Dirk Werth
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  3. Peter Loos
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Correspondence to Julian Krumeich.

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Accepted after two revisions by Prof. Jarke.

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Krumeich, J., Werth, D. & Loos, P. Prescriptive Control of Business Processes. Bus Inf Syst Eng 58, 261–280 (2016). https://doi.org/10.1007/s12599-015-0412-2

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