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Use of Monte Carlo simulation: an empirical study of German, Austrian and Swiss controlling departments

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Abstract

This paper addresses current and future aspects of the use of Monte Carlo simulation in controlling departments and examines context as well as company-internal factors that may drive the intensity of its usage. To this end, we conducted an empirical survey that was completed by 445 participants from Germany, Austria and Switzerland. The results suggest a rather low adoption rate of Monte Carlo simulation in controlling, but at the same time, the quality of knowledge concerning Monte Carlo simulation within the companies is much higher. In addition, we identify a strong increase in the use of Monte Carlo simulation very recently, and its use is expected to increase threefold within the next 5 years. Furthermore, regression analyses indicate that the use of Monte Carlo simulation is mainly driven by company-internal factors such as its perceived relevance and years of usage. Contrary to our expectations, context factors such as perceived environmental uncertainty do not explain usage, and only company size and industry sector have significant effects.

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Notes

  1. It should be noted that only the first question on the intensity of usage was answered by all of the 445 respondents. Some questions could only be answered by respondents where MCS is actually in use. One main implication is that we had to test our hypotheses concerning external and internal aspects of MCS usage in two separate models: the first with N = 374 and the second with N = 66.

  2. This description is based on (Reiss 2011, p. 245), (Davis et al. 2007, p. 481) and (Evans and Olson 2002, p. 2).

  3. This also allows for ensuring that the respondents are able to answer the subsequent survey questions.

  4. Crystal Ball and @Risk are Excel-based software used for MCS. Crystal Ball is a registered trademark of Oracle, see Oracle (2010). @Risk is a registered trademark of Palisade, see Palisade (2012). Risk Solver is a registered trademark of Frontline Systems (2010).

  5. For a summary of all questions and answers submitted to the panel, see Weber et al. (2011).

  6. In this article, small companies are defined by a sales volume of less than €51 million; large companies are defined by a sales volume of more than €1 billion.

  7. High benefit is the most frequently chosen (31.8 %) level of benefit gained from MCS regarding all five items in aggregation.

  8. In this mean value, the answers for no usage are taken into account. The mean value for the intensity of usage by those who actually employ MCS is 1.7.

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Acknowledgments

An earlier version of the paper benefited from discussions at the ACMAR in Vallendar. We would like to thank Prof. Dr. Dr. h.c. Jürgen Weber and the team of the WHU-Controller Panel for the opportunity to integrate our questions in the annual WHU-Controller Panel.

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Authors and Affiliations

  1. Institute of Management Control and Accounting, Hamburg University of Technology, Schwarzenbergstraße 95 D, 21073, Hamburg, Germany

    Cathérine Grisar & Matthias Meyer

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  1. Cathérine Grisar
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  2. Matthias Meyer
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Correspondence to Matthias Meyer.

Appendix

Appendix

Explanation The following questions refer to the usage of Monte Carlo simulation. Monte Carlo simulation allows for an explicit examination of uncertainty. The input parameters are described by probability distributions instead of single values. Therefore, for example a range of possible alternative exchange rates can be analyzed instead of one single exchange rate. The outcome displays a probability distribution as well, e.g., a project’s rate of return. By means of Monte Carlo simulation, the decision-maker realizes which results are caused by a particular course of action and how high the probability for such outcomes is. Thereby, simulation is supported by Excel plug-ins.

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Questionnaire on Monte Carlo simulation

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Grisar, C., Meyer, M. Use of Monte Carlo simulation: an empirical study of German, Austrian and Swiss controlling departments. J Manag Control 26, 249–273 (2015). https://doi.org/10.1007/s00187-015-0213-2

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