Understanding Entropy Generation during the Execution of Business Process Instantiations: An Illustration from Cost Accounting

  • Peter De Bruyn
  • Philip Huysmans
  • Herwig Mannaert
  • Jan Verelst
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 146)


The instantiation and execution of business processes typically generates an enormous set of data, including financial- and accounting-related information, based on different aggregation levels. As a result, it can be very complex to draw conclusions from this data, such as which steps in a business process are causing delays or, in an accounting context, which tasks are causing high costs. In this paper, we relate this complexity generated through business process execution to the concept of entropy, as defined in thermodynamics. More specifically, we show how information aggregation seems to be at the core of this phenomenon. We discuss six types of information aggregation dimensions which tend to increase entropy (and hence, complexity) in a cost accounting context. As entropy is generally controlled by adding structure to the considered system, we propose a set of preliminary guidelines to control this entropy based on insights from the Normalized Systems (NS) theory rationale.


Entropy Business process execution Information aggregation Cost accounting Normalized Systems 


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  1. 1.
    Kaplan, R.S., Anderson, S.R.: Time-driven activity-based costing. Harvard Business Review 82(11), 131–138 (2004)Google Scholar
  2. 2.
    Drury, C.: Management and Cost Accounting. South-Western (2007)Google Scholar
  3. 3.
    Lev, B.: The aggregation problem in financial statements: An informational approach. Journal of Accounting Research 6(2), 247–261 (1968)CrossRefGoogle Scholar
  4. 4.
    Ronen, J., Falk, G.: Accounting aggregation and the entropy measure: An experimental approach. The Accounting Review 48(4), 696–717 (1973)Google Scholar
  5. 5.
    Abdel-Khalik, A.R.: The entropy law, accounting data, and relevance to decision-making. The Accounting Review 49(2), 271–283 (1974)Google Scholar
  6. 6.
    Boltzmann, L.: Lectures on Gas Theory. Dover Publications (1995)Google Scholar
  7. 7.
    Wikipedia: Entropy (2013),
  8. 8.
    Jung, J.Y., Chin, C.H., Cardoso, J.: An entropy-based uncertainty measure of process models. Information Processing Letters 111(3), 135–141 (2011)CrossRefGoogle Scholar
  9. 9.
    De Bruyn, P., Huysmans, P., Oorts, G., Mannaert, H.: On the applicability of the notion of entropy for business process analysis. In: Proceedings of the Second International Symposium on Business Modeling and Sofware Design (BMSD), pp. 93–99 (2012)Google Scholar
  10. 10.
    De Bruyn, P., Mannaert, H.: On the generalization of normalized systems concepts to the analysis and design of modules in systems and enterprise engineering. International Journal on Advances in Systems and Measurements 5(3&4), 216–232 (2012)Google Scholar
  11. 11.
    Mannaert, H., De Bruyn, P., Verelst, J.: Exploring entropy in software systems: towards a precise definition and design rules. In: Proceedings of the Seventh International Conference on Systems (ICONS), pp. 93–99 (2012)Google Scholar
  12. 12.
    Van Nuffel, D.: Towards designing modular and evolvable business processes. PhD thesis, University of Antwerp (2011)Google Scholar
  13. 13.
    Atkinson, A., Banker, R., Kaplan, R.: Management Accounting. The Robert S. Kaplan Series in Management Accounting. Prentice Hall (2001)Google Scholar
  14. 14.
    Kaplan, R.S., Bruns, W.: Accounting and Management: A Field Study Perspective. Harvard Business School Press (1987)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Peter De Bruyn
    • 1
  • Philip Huysmans
    • 1
  • Herwig Mannaert
    • 1
  • Jan Verelst
    • 1
  1. 1.Normalized Systems Institute (NSI), Department of Management Information SystemsUniversity of AntwerpAntwerpBelgium

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