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A Visionary Way to Novel Process Optimizations

The Marriage of the Process Domain and Deep Neuronal Networks
  • Marcus Grum
  • Norbert Gronau
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 309)

Abstract

Modern process optimization approaches do build on various qualitative and quantitative tools, but are mainly limited to simple relations in different process perspectives like cost, time or stock. In this paper, a new approach is presented which focuses on techniques of the area of Artificial Intelligence to capture complex relations within processes. Hence, a fundamental value increase is intended to be gained. Existing modeling techniques and languages serve as basic concepts and try to realize the junction of apparently contradictory approaches. This paper therefore draws a vision of promising future process optimization techniques and presents an innovative contribution.

Keywords

Process modeling Artificial Intelligence Machine learning Deep neuronal networks Knowledge Modeling Description Language KMDL Process simulation Simulation process building Process optimization 

References

  1. 1.
    Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press Inc., Oxford (1995). ISBN 0198538642zbMATHGoogle Scholar
  2. 2.
    Broomhead, D., Lowe, D.: Multivariate functional interpolation and adaptive networks. Complex Syst. 2, 321–355 (1988)zbMATHGoogle Scholar
  3. 3.
    Byrd, R.H., Lu, P., Nocedal, J., Zhu, C.Y.: A limited memory algorithm for bound constrained optimization. SIAM J. Sci. Comput. 16(6), 1190–1208 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Chambers, M., Mount-Campbell, C.A.: Process optimization via neural network metamodeling. Int. J. Prod. Econ. 79, 93–100 (2000)CrossRefGoogle Scholar
  5. 5.
    Deming, W.: Elementary Principles of the Statistical Control of Quality. JUSE, Tokyo (1950)Google Scholar
  6. 6.
    Deming, W.: Out of the Crisis. MIT Press, Cambridge (1986)Google Scholar
  7. 7.
    Deming, W.: The New Economics. MIT Press, Cambridge (1993)Google Scholar
  8. 8.
    Fahlman, S.: Faster learning variations on back-propagation: an empirical study. In: Touretszky, D., Hinton, G., Sejnowski, T. (eds.) Proceedings of the 1988 Connectionist Models Summer School, pp. 38–51. Morgan Kaufmann, San Mateo (1989)Google Scholar
  9. 9.
    Gronau, N.: Geschäftsprozessmanagement in Wirtschaft und Verwaltung., 2. Gito (2017)Google Scholar
  10. 10.
    Gronau, N., Thiem, C., Ullrich, A., Vladova, G., Weber, E.: Ein Vorschlag zur Modellierung von Wissen in wissensintensiven Geschäftsprozessen. Technical report, University of Potsdam, Department of Business Informatics, esp. Processes and Systems (2016)Google Scholar
  11. 11.
    Gronau, N.: Process Oriented Management of Knowledge: Methods and Tools for the Employment of Knowledge as a Competitive Factor in Organizations (Wissen prozessorientiert managen: Methode und Werkzeuge für die Nutzung des Wettbewerbsfaktors Wissen in Unternehmen). Oldenbourg Verlag München (2009)Google Scholar
  12. 12.
    Gronau, N.: Modeling and Analyzing Knowledge Intensive Business Processes with KMDL - Comprehensive Insights into Theory and Practice. GITO mbH Verlag, Berlin (2012)Google Scholar
  13. 13.
    Gronau, N., Grum, M., Bender, B.: Determining the optimal level of autonomy in cyber-physical production systems. In: Proceedings of the 14th International Conference on Industrial Informatics (INDIN) (2016)Google Scholar
  14. 14.
    Gronau, N., Maasdorp, C.: Modeling of Organizational Knowledge and Information: Analyzing Knowledge-Intensive Business Processes with KMDL. GITO mbH Verlag, Berlin (2016)Google Scholar
  15. 15.
    Hammer, M., Champy, J.: Reengineering the Corporation: A Manifesto for Business Revolution. Harper Business, New York (1993)Google Scholar
  16. 16.
    Hestenes, M.R., Stiefel, E.: Methods of conjugate gradients for solving linear systems. J. Res. Natl. Bur. Stand. 49(6), 409–436 (1952)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. PNAS 79(8), 2554–2558 (1982)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Imai, M.: Kaizen: The Key to Japan’s Competitive Success. McGraw-Hill/Irwin, New York City/Huntersville (1986)Google Scholar
  19. 19.
    Ishikawa, K.: What is Total Quality Control? The Japanese Way. Prentice-Hall Inc., Upper Saddle River (1985)Google Scholar
  20. 20.
    Kohonen, T.: Self-Organization and Associative Memory, 3rd edn. Springer, New York (1989).  https://doi.org/10.1007/978-3-642-88163-3. ISBN 0-387-51387-6CrossRefzbMATHGoogle Scholar
  21. 21.
    Krallmann, H., Frank, H., Gronau, N.: Systemanalyse im Unternehmen. Oldenbourg Wissenschaftsverlag (2001)Google Scholar
  22. 22.
    McCulloch, W.S., Pitts, W.: A Logical Calculus of the Ideas Immanent in Nervous Activity, pp. 15–27. MIT Press, Cambridge (1988). ISBN 0-262-01097-6zbMATHGoogle Scholar
  23. 23.
    Moen, R., Norman, C.: Evolution of the PDCA. Google Scholar (2006)Google Scholar
  24. 24.
    Nonaka, I., Takeuchi, H.: The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press, Oxford (1995)Google Scholar
  25. 25.
    Peffers, K., Tuunanen, T., Gengler, C.E., Rossi, M., Hui, W., Virtanen, V., Bragge, J.: The design science research process: a model for producing and presenting information systems research. In: 1st International Conference on Design Science in Information Systems and Technology (DESRIST), vol. 24, no. 3, pp. 83–106 (2006)Google Scholar
  26. 26.
    Peffers, K., Tuunanen, T., Rothenberger, M.A., Chatterjee, S.: A design science research methodology for information systems research. Manag. Inf. Syst. 24(3), 45–78 (2007)CrossRefGoogle Scholar
  27. 27.
    Plaut, D.C., Nowlan, S.J., Hinton, G.E.: Experiments on learning backpropagation. Technical report CMU-CS-86-126, Carnegie-Mellon University, Pittsburgh, PA (1986)Google Scholar
  28. 28.
    Remus, U.: Process-oriented knowledge management. Design and modelling. Ph.D. thesis, University of Regensburg (2002)Google Scholar
  29. 29.
    Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: Proceedings of the IEEE International Conference on Neural Networks, San Francisco, pp. 586–591 (1993)Google Scholar
  30. 30.
    Robinson, A.J., Fallside, F.: The utility driven dynamic error propagation network. Technical report CUED/F-INFENG/TR.1, Cambridge University Engineering Department (1987)Google Scholar
  31. 31.
    Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386–408 (1958)CrossRefGoogle Scholar
  32. 32.
    Rosenblatt, F.: Principles of Neurodynamics. Spartan, New York (1963)Google Scholar
  33. 33.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Internal Representations by Error Propagation, pp. 318–362. MIT Press, Cambridge (1986). ISBN 0-262-68053-XGoogle Scholar
  34. 34.
    Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)CrossRefGoogle Scholar
  35. 35.
    Shewchuk, J.R.: An introduction to the conjugate gradient method without the agonizing pain. Technical report, Carnegie Mellon University, Pittsburgh, PA, USA (1994)Google Scholar
  36. 36.
    Shewhart, W.: Statistical Method from the Viewpoint of Quality Control. Dover Publications Inc., New York (1939). Edited by W. Edwards DemingzbMATHGoogle Scholar
  37. 37.
    Sultanow, E., Zhou, X., Gronau, N., Cox, S.: Modeling of processes, systems and knowledge: a multi-dimensional comparison of 13 chosen methods. Int. Rev. Comput. Softw. (IRECOS) 6, 3309–3319 (2012)Google Scholar
  38. 38.
    Werbos, P.J.: Generalization of backpropagation with application to a recurrent gas market model. Neural Netw. 1, 339–356 (1988)CrossRefGoogle Scholar
  39. 39.
    Williams, R.J., Zipser, D.: Gradient-based learning algorithms for recurrent networks and their computational complexity. In: Chauvin, Y., Rumelhart, D.E. (eds.) Back-propagation: Theory, Architectures and Applications, pp. 433–486. Lawrence Erlbaum Publishers, Hillsdale (1995)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of PotsdamPotsdamGermany

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