Skip to main content
SpringerLink
Log in

Quantitative Process Analysis

  • Chapter
Fundamentals of Business Process Management

  • 206k Accesses

Abstract

Qualitative analysis is a valuable tool to gain systematic insights into a process. However, the results obtained from qualitative analysis are sometimes not detailed enough to provide a solid basis for decision making. Think of the process owner of BuildIT’s equipment rental process wanting to make a case to the company’s COO that every site engineer should be given a tablet computer with wireless access in order to query suppliers’ catalogs and to make and modify rental requests from any construction site. The process owner will be asked to substantiate this investment in quantitative terms and specifically to estimate how much time and money would be saved by doing this investment. To make such estimates, we need to go beyond qualitative analysis.

This chapter introduces a range of techniques for analyzing business processes quantitatively, in terms of performance measures such as cycle time, total waiting time and cost. Specifically, the chapter focuses on three techniques: flow analysis, queueing analysis and simulation. All these techniques have in common that they allow us to calculate performance measures of a process, given data about the performance of individual activities and resources in the process.

It is better to be approximately right than precisely wrong.

Warren Buffett (1930–)

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

Access this chapter

Log in via an institution

Institutional subscriptions

Notes

  1. 1.

    In queueing theory, the term service time is used instead of processing time. For uniformity purposes, here we use the term processing time.

  2. 2.

    http://apps.business.ualberta.ca/aingolfsson/qtp/.

  3. 3.

    http://www.stat.auckland.ac.nz/~stats255/qsim/qsim.html.

  4. 4.

    http://www.perfdynamics.com/Tools/PDQ.html.

  5. 5.

    Note that when discussing queueing theory above, we used the term occupation rate instead of resource utilization. These two terms are synonyms.

  6. 6.

    Some simulators additionally allow one to specify that new cases are only created during certain times of the day and certain days of the week, or according to a given calendar.

References

  1. I. Adan, J. Resing, Queueing Theory (Eindhoven University of Technology, Eindhoven, 2002)

    Google Scholar 

  2. R. Anupindi, S. Chopra, S.D. Deshmukh, J.A. van Mieghem, E. Zemel, Managing Business Process Flows (Prentice Hall, New York, 1999)

    Google Scholar 

  3. S. Conger, Six sigma and business process management, in Handbook of Business Process Management, vol. 1, ed. by J. vom Brocke, M. Rosemann (Springer, Berlin, 2010), pp. 127–148

    Chapter  Google Scholar 

  4. D. Fürstenau, Process Performance Measurement (GRIN, Santa Cruz, 2008)

    Google Scholar 

  5. A. Greasley, A redesign of a road traffic accident reporting system using business process simulation. Bus. Process. Manag. J. 10(6), 635–644 (2004)

    Article  Google Scholar 

  6. P. Harmon, Business Process Change: A Guide for Business Managers and BPM and Six Sigma Professionals, 2nd edn. (Morgan Kaufmann, San Mateo, 2007)

    Google Scholar 

  7. R.S. Kaplan, D.P. Norton, The balanced scorecard—measures that drive performance. Harv. Bus. Rev. 70(1), 71–79 (1992)

    Google Scholar 

  8. W.D. Kelton, R.P. Sadowski, N.B. Swets, Simulation with Arena, 5th edn. (McGraw-Hill, New York, 2009)

    Google Scholar 

  9. M. Laguna, J. Marklund, Business Process Modeling, Simulation and Design (Prentice Hall, New York, 2004)

    Google Scholar 

  10. W.M.P. van der Aalst, M. Rosemann, M. Dumas, Deadline-based escalation in process-aware information systems. Decis. Support Syst. 43(2), 492–511 (2007)

    Article  Google Scholar 

  11. W.M.P. van der Aalst, J. Nakatumba, A. Rozinat, N. Russell, Business process simulation, in Handbook of Business Process Management, vol. 1, ed. by J. vom Brocke, M. Rosemann (Springer, Berlin, 2010), pp. 313–338

    Chapter  Google Scholar 

  12. Y. Yang, M. Dumas, L. García-Bañuelos, A. Polyvyanyy, L. Zhang, Generalized aggregate quality of service computation for composite services. J. Syst. Softw. 85(8), 1818–1830 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Marlon Dumas

  2. Queensland University of Technology and NICTA, Brisbane, Australia

    Marcello La Rosa

  3. Institute for Information Business, Vienna University of Economics and Business, Vienna, Austria

    Jan Mendling

  4. Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands

    Hajo A. Reijers

Authors
  1. Marlon Dumas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marcello La Rosa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jan Mendling
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hajo A. Reijers
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Dumas, M., La Rosa, M., Mendling, J., Reijers, H.A. (2013). Quantitative Process Analysis. In: Fundamentals of Business Process Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33143-5_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-33143-5_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33142-8

  • Online ISBN: 978-3-642-33143-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation