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Use of simulation in controlling research: a systematic literature review for German-speaking countries

Management Review Quarterly volume 66, pages 117–157 (2016)Cite this article

Abstract

This paper provides a systematic literature review of the use of simulation in the field of “controlling”, a term synonymously used for management accounting and control in German-speaking countries. The review starts from a total of 12,102 articles published in leading, controlling-related business journals in German-speaking countries (Germany, Austria, and Switzerland) between 1980 and 2009. Thereof, 47 articles specifically refer to the use of simulation in controlling. This set of articles is analyzed along the following dimensions: development of publication volume over time, important authors, controlling tasks and instruments supported by simulation or fulfillment of minimum quality criteria concerning simulation modeling and analysis. The results indicate an increasing interest in employing simulation within controlling and its particular relevance in practice. Two areas emerge as the main application arena: planning and risk management. Despite some progress the review also shows that simulation is not yet an established branch of research on its own in controlling. A detailed analysis of the articles suggests that more transparency and standards in the application of simulations are needed to further advance this method.

Zusammenfassung

Dieser Artikel führt eine systematische Literaturanalyse der Nutzung von Simulation im Bereich Controlling durch. Von insgesamt 12.102 Artikeln, die zwischen 1980 und 2009 in führenden, für das Controlling relevanten betriebswirtschaftlichen Zeitschriften im deutschsprachigen Raum (Deutschland, Österreich und Schweiz) veröffentlicht wurden, können 47 Simulationsartikel mit Controllingbezug identifiziert werden. Diese Artikel werden hinsichtlich verschiedener Dimensionen untersucht, wie etwa die Entwicklung der Anzahl der Publikationen, wichtigste Autoren, Aufgaben und Instrumente des Controllings die mit Simulation unterstützt werden oder die Erfüllung gewisser Minimumstandards bezüglich der Anwendung von Simulation. Die Analyse zeigt ein steigendes Interesse an Simulation im Bereich Controlling, insbesondere auf Seite der Praxis. Dabei stehen vor allem die Themengebiete Planung und Risikomanagement im Vordergrund. Jedoch verdeutlichen die Ergebnisse auch, dass Simulation noch kein eigenständiger etablierter Forschungszweig innerhalb des Controllings ist. Eine Detailanalyse der Artikel zeigt Defizite in den Bereichen Transparenz und Standards bei der Anwendung von Simulationen auf.

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Fig. 1

Notes

  1. For a more detailed discussion see Sect. 2.

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

  3. The use of simulation in corporate controlling practice has been examined in Grisar and Meyer (2015).

  4. The term “German-speaking countries” refers to Germany, Austria, and Switzerland in the following. We are aware of the fact that Lichtenstein is also a German-speaking country. However, as there is no controlling-related journal originated from Lichtenstein, we refrain from further explicitly mentioning the country in our article.

  5. The journal “Management Science” sticks out with an extreme value of 23.4 %.

  6. Richiardi et al. analyzed top economic research regarding agent-based simulation and yield a share of less than 0.03 % agent-based simulation in economics (Richiardi et al. 2006:1.3).

  7. In this empirical study, cost accounting is denoted as task and instrument at the same time; however, we are abstain from this perspective and denote cost accounting only as an controlling instrument in the following.

  8. For different classifications, see Harrison et al. (2007), Davis et al. (2007) and Macy and Willer (2002).

  9. Using digital computers one can strictly speak only of “quasi-continuous” simulations.

  10. For example Deckert and Klein distinguish in their review between three categories of simulation: discrete simulation, continuous simulation and Monte Carlo simulation (Deckert and Klein 2010:93). Based on what has been described here, discrete simulation and continuous simulation should already fully describe how the change of state variables can be modeled. The last category therefore does not fit.

  11. Both Crystal Ball and @Risk are Excel-based software. The name @Risk indicates its specific application for the calculation of “at risk” figures such as Value at Risk (VaR). Crystal Ball is a registered trademark of Oracle, see Oracle (2010). @Risk is a registered trademark of Palisade, see Palisade (2012).

  12. For a comparison of different software programs, see Klein (2010) and Sugiyama (2008).

  13. For an overview of different classifications, see Schäffer and Steiners (2004).

  14. For the relevance of the conceptual use of controlling information in general, see Heine (2008).

  15. We would like to mention that simulation articles possibly relevant for Controlling may also be published in journals located in the sub-rankings Business Information Systems and Operations Research, such as the journals Wirtschaftsinformatik (WI) and OR Spectrum. To keep our study and its results comparable to the previous studies about Controlling mentioned above, we decided not to include these journals. Still, we screened these journals for possibly relevant contents and will reflect on possible biases and implications of this decision in the last section of this paper.

  16. In 2013, this journal was relaunched and appears now as “Journal of Business Economics”.

  17. In 2012, this journal obtained an English name in addition: “Business Administration Review”.

  18. In 2014, this journal was relaunched and appears now as “Management Review Quarterly”.

  19. The extended version of the ranking does not affect the selection of the journals as only some additional journals are included. The ranking position of the selected journals remains the same, see JQ2.1 (VHB 2011).

  20. In 2011, this journal was relaunched and appears now as “Journal of Management Control”.

  21. It is the only journal in the sample that is currently included in the Social Science Citation Index (Thomson Reuters 2012).

  22. In 2013, this journal was renamed again and appears now as “Controlling Management Review”.

  23. It should be noted that in a different version of the JQ2 ranking, krp has an independent index of 4.54 (VHB 2008).

  24. See Schäffer and Binder (2008:62). Their citation analysis is based on academic publications.

  25. We are aware of the fact that simulation-based controlling articles may also be published in other journals than the introduced ones. However, with our selection, we rely on those journals that have been used in other studies and that are perceived at most related to controlling.

  26. The establishment of practice journals may have triggered the rise of the share of articles in these journals compared to the share of academic articles in the 1990s. Between 1980 and 1989, the share of academic articles is 83.4 %; between 1990 and 2009, the share decreases to 69.0 %.

  27. It should be noted that this had to be done by hand for 66.8 % of the articles as they were not included in electronic databases.

  28. Throughout the paper, the sample of simulation articles in controlling is referred to as “simulation articles” or “reviewed articles”. The single term “articles” refers to all published articles within the defined journals (universe of articles).

  29. We set the number of simulation articles in controlling in relation to the overall number of articles published in each journal between 1980 and 2009.

  30. In case of joint work, the article counts for both authors.

  31. In addition, one reviewed article provided a survey on simulation in controlling but did not refer to specific tasks.

  32. In this review, the term “risk analysis” denotes every practice to model risk and to make it manageable. It abstracts from the narrow view that risk analysis itself denotes a simulation approach which is sometimes found in the literature (Hertz 1964).

  33. Heine and Kunz (2003) is a review article and has been excluded as well.

  34. We would like to remind of the problematic character of these self-descriptions discussed in Sect. 2, where we also discussed issues with the term “Monte Carlo simulation”.

  35. Maple 11 is now available in its 16th edition. It is a registered trademark of Waterloo Maple Inc., see Maplesoft (2012).

  36. For the following analysis of the simulation quality, we excluded articles that do not contain a simulation model for examination. Articles dealing with simulation in a wider sense such as the mere examination of alternatives and business games were excluded as well. Therefore, 31 out of 47 articles were included in our analysis.

  37. It is worth noting that these results may be different for journals of other disciplines, e.g., business informatics or operations research that also happen to contain simulation-based controlling articles. Due to a greater focus of simulation in these journals, the simulation quality is likely to be higher.

  38. This problem is also found and discussed in a study by Fontana who analyzed the diffusion of simulation in economics (Fontana 2006:4.8 and 5.2).

  39. Wolf identified the methodological and conceptual gap and describes in detail how to implement the simulation (Wolf 2003). A general introduction to simulation within the reviewed articles is given by Deckert and Klein (2010).

  40. For detailed information on experimental design, see (Law 2007:619–668) and Kleijnen et al. (2005).

  41. It should be mentioned, deciding what articles in these journals can be classified as controlling articles can become a quite challenging task with high differences in interrater reliability. For a good discussion of the relationships of the disciplines of the fields of controlling and operations research see Küpper (2007).

References

  • The reviewed articles are marked with an asterisk

  • Albers S (1998) Regeln für die Allokation eines Marketing-Budgets auf Produkte oder Marktsegmente. Zeitschrift für betriebswirtschaftliche Forschung 50(3):211–235*

  • Anderson DR, Sweeney DJ, Williams TA, Martin K (2011) An introduction to management science: quantitative approaches to decision making. South-Western Cengage Learning, Mason

    Google Scholar 

  • Axelrod RK (1997) Advancing the art of simulation in the social sciences. In: Conte R, Hegselmann R, Terna P (eds) Simulating social phenomena. Springer, Berlin, pp 21–40

    Chapter  Google Scholar 

  • Barth R, Meyer M, Spitzner J (2012) Typical pitfalls of simulation modeling: Lessons learned from armed forces and business. J Artif Soc Soc Simul 15(2)

  • Bleuel H (2006) Monte-Carlo-Analysen im Risikomanagement mittels Software-Erweiterungen zu MS-Excel: Dargestellt am Fallbeispiel der Unternehmensplanung. Controlling 18(7):371–378*

  • Brink A (1989) Der Einsatz der Simulationstechnik in der Betriebswirtschaft. Das Wirtschaftsstudium 18(12):679–685

    Google Scholar 

  • Brockhoff K (1987) Budgetierungsstrategien für Forschung und Entwicklung. Zeitschrift für Betriebswirtschaft 57(9):846–869*

  • Buxmann P (1999) Der Einfluss von Entwicklungen in der Informations- und Kommunikationstechnik auf betriebliche Entscheidungssysteme. Zeitschrift für betriebswirtschaftliche Forschung 51(7/8):714–729*

  • Charnes JM (2007) Financial modeling with Crystal Ball and Excel. Wiley, Hoboken

    Google Scholar 

  • Christensen J (2010) Accounting errors and errors of accounting. Account Rev 85(6):1827–1838

    Article  Google Scholar 

  • Colsman B (2007) Erfolgsfaktoren und Verbesserungspotenziale in der praktischen Umsetzung des Planungsprozesses. Zeitschrift für Controlling und Management 51(3):194–199

    Article  Google Scholar 

  • Cook TM, Russell RA (1993) Introduction to management science, 5th edn. Prentice Hall, Englewood Cliffs

    Google Scholar 

  • Dannenberg H (2009) Investitionsentscheidungen unter Berücksichtigung von Risikotragfähigkeitsrestriktionen. Zeitschrift für Controlling und Management 53(4):248–253*

  • Davies P (2004) Systematic reviews and the Campbell Collaboration. In: Thomas G, Pring R (eds) Evidence-based practice in education. Open University Press, Maidenhead, pp 21–33

    Google Scholar 

  • Davis JS, Pesch HL (2012) Fraud dynamics and controls in organizations. Account Organ Soc 38(6–7):469–483

    Google Scholar 

  • Davis JP, Eisenhardt KM, Bingham CB (2007) Developing theory through simulation methods. Acad Manag Rev 32(2):480–499

    Article  Google Scholar 

  • Deckert A, Klein R (2010) Agentenbasierte Simulation zur Analyse und Lösung betriebswirtschaftlicher Entscheidungsprobleme. Journal für Betriebswirtschaft 60(2):89–125

    Article  Google Scholar 

  • Dhebar A (1993) Managing the quality of quantitative analysis. Sloan Manag Rev 34(2):69–75

    Google Scholar 

  • Djanani C (1992) Quantitatives Modell zur Simulierung der finanziellen Konsequenzen strategischer Entscheidungen: Dargestellt an einem Vergleich der Rentabilitäts- und Liquiditätswirkungen von Inlands- und Auslandsinvestitionen. Journal für Betriebswirtschaft 42(5):203–215*

  • Drexl A (1990) Nutzungsdauerentscheidungen bei Sicherheit und Risiko. Zeitschrift für betriebswirtschaftliche Forschung 42(1):50–66*

  • Ehrmann T, Scheinker E (2009) Ex-Post-Adaptionen von Vertragsregelungen: Eine simulationsbasierte Analyse auf Grundlage der NK-Modelle. Zeitschrift für Betriebswirtschaft 79(1):93–123*

  • Evans JR, Olson DL (2002) Introduction to simulation and risk analysis. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Fink A (2009) Conducting research literature reviews: from the Internet to paper, 3rd edn. Sage Publications, Thousand Oaks

    Google Scholar 

  • Fontana M (2006) Simulation in economics: evidence on diffusion and communication. J Artif Soc Soc Simul 9(2)

  • Forrester J (1961) Industrial dynamics. MIT Press, Cambridge

    Google Scholar 

  • Franke A (1997) Risiko-Controlling bei Projekten des Industrieanlagenbaus. Controlling 9(3):170–179*

  • Friedemann D (2004) Integrierte Unternehmensplanng: Eine Utopie für den Mittelstand? Zeitschrift für Controlling und Management 48(1):11–14

    Article  Google Scholar 

  • Gilbert GN (2008) Agent-based models. Quantitative applications in the social sciences. Sage Publications, London

    Google Scholar 

  • Gilbert GN, Troitzsch KG (2005) Simulation for the social scientist, 2nd edn. Open University Press, Maidenhead

    Google Scholar 

  • Gleißner W (2004) Die Aggregation von Risiken im Kontext der Unternehmensplanung. Zeitschrift für Controlling und Management 48(5):350–359*

  • Gleißner W (2008) Erwartungstreue Planung und Planungssicherheit. Controlling 20(2):81–87*

  • Gleißner W, Grundmann T (2003) Stochastische Planung: Auf dem Weg zu einem chancen- und risikoorientierten Controlling. Controlling 15(9):459–466*

  • Gleißner W, Grundmann T (2008) Risiko-Benchmark-Werte für das Risikocontrolling deutscher Unternehmen. Zeitschrift für Controlling und Management 52(5):314–319*

  • Gleißner W, Romeike F (2005) Anforderungen an die Softwareunterstützung für das Risikomanagement. Zeitschrift für Controlling und Management 49(2):154–164*

  • Grimm V, Berger U, Bastiansen F, Eliassen S, Ginot V, Giske J, Goss-Custard J, Grand T, Heinz SK, Huse G, Huth A, Jepsen JU, Jørgensen C, Mooij WM, Müller B, Pe’er G, Piou C, Railsback SF, Robbins AM, Robbins MM, Rossmanith E, Rüger N, Strand E, Souissi S, Stillman RA, Vabø R, Visser U, DeAngelis DL (2006) A standard protocol for describing individual-based and agent-based models. Ecol Model 198(1–2):115–126

    Article  Google Scholar 

  • Grisar C, Meyer M (2015) Use of Monte Carlo simulation: an empirical study of German, Austrian and Swiss controlling departments. J Manag Control 26(2):249–273

    Article  Google Scholar 

  • Grob HL, Mrzyk A (1998) Risiko-Chancen-Analyse in der Investitionsrechnung: Integration von VOFI und Crystal Ball. Controlling 10(2):120–129*

  • Günther TW (2013) Conceptualisations of ‘controlling’ in German-speaking countries: analysis and comparison with Anglo-American management control frameworks. J Manag Control 23(1):269–290

    Article  Google Scholar 

  • Günther TW, Smirska K, Schiemann F, Weber S (2009) Optimierung des Risikomanagementsystems am Beispiel der R. Stahl Technologiegruppe. Controlling 21(1):48–56*

  • Harrison JR, Lin Z, Carroll GR, Carley KM (2007) Simulation modeling in organizational and management research. Acad Manag Rev 32(4):1229–1245

    Article  Google Scholar 

  • Heath B, Hill R, Ciarello F (2009) A survey of agent-based modeling practices (January 1998 to July 2008). J Artif Soc Soc Simul 12(4)

  • Heine B-O (2008) Konzeptionelle Nutzung von Controllinginformationen: Ein modelltheoretischer Ansatz. Gabler Verlag, Wiesbaden

    Google Scholar 

  • Heine B-O, Kunz J (2003) Die Computersimulation als Methode in der betriebswirtschaftlichen Forschung. Zeitschrift für Controlling und Management 47(6):374–378*

  • Heinen KC, Hoffjan A (2004) Zur strategischen Relevanz wettbewerberbezogener Kosteninformationen: Eine experimentelle Untersuchung zum Competitor Accounting. Die Betriebswirtschaft 64(2):211–227*

  • Hertz DB (1964) Risk analysis in capital investment. Harv Bus Rev 42(1):95–106

    Google Scholar 

  • Hess SW, Quigley HA (1963) Analysis of risk in investments using Monte Carlo techniques. Chem Eng Prog Symp Ser 59(42):55–63

    Google Scholar 

  • Hess T, Weber J, Hirnle C, Hirsch B, Strangfeld O (2005) Themenschwerpunkte und Tendenzen in der deutschsprachigen Controllingforschung: Eine empirische Analyse. In: Weber J, Meyer M (eds) Internationalisierung des Controllings. Standortbestimmung und Option. Deutscher Universitäts-Verlag, Wiesbaden, pp 29–47

    Chapter  Google Scholar 

  • Hibbeln M, Viemann K (2009) Unternehmensplanung in Venture-Capital-Gesellschaften. Die Betriebswirtschaft 69(1):7–30*

  • Hoitsch H, Backes M (1992) Die ökonomische Bewertung strategischer Investitionen im CIM-Bereich. Journal für Betriebswirtschaft 42(1):41–56*

  • Hoitsch H, Winter P (2004) Die Cash Flow at Risk-Methode als Instrument eines integriert-holistischen Risikomanagements. Zeitschrift für Controlling und Management 48(4):235–246*

  • Homburg C, Stephan J (2004) Kennzahlenbasiertes Risikocontrolling in Industrie- und Handelsunternehmen. Zeitschrift für Controlling und Management 48(5):313–325*

  • Kehrel U, Schmitting W (2008) Jenseits der Grenzen der klassischen Investitionsrechnung: Integration von vollständigen Finanzplänen, flexibler Planung und Simulation. Zeitschrift für Planung und Unternehmenssteuerung 19(1):59–83*

  • Keppe H, Weber M (1993) Risikoanalyse bei partieller Wahrscheinlichkeitsinformation. Die Betriebswirtschaft 53(1):49–56*

  • Kesaraju VS, Ciarallo FW (2012) Integrated simulation combining process-driven and event-driven models. J Simul 6:9–20

    Article  Google Scholar 

  • Kieninger M, Sommerfeldt H (1992) Prozesskostenmanagement mit dem PC: Aufbau, Ablauf und Auswertungen der Prozesskostenrechnugn unter Einsatz des PROZESSMANAGERS. Controlling 4(1):38–45*

  • Kleijnen JPC, Sanchez SM, Lucas TW, Cioppa TM (2005) A user’s guide to the brave new world of designing simulation experiments. INFORMS J Comput 17(3):263–289

    Article  Google Scholar 

  • Klein M (2010) Add-In basierte Softwaretools zur stochastischen Unternehmensbewertung. Working Papers in Accounting Valuation Auditing No 2010-7, Nürnberg

  • Koller H (1966) Simulation als Methode in der Betriebswirtschaft. Zeitschrift für Betriebswirtschaft 36(2):95–110

    Google Scholar 

  • Krapp M (2000) Relative Leistungsbewertung im dynamischen Kontext: Eine Analyse der Kollusionsproblematik bei wiederholter Delegation. Zeitschrift für betriebswirtschaftliche Forschung 52(5):257–277*

  • Kruschwitz L (1980) Bemerkungen zur Risikoanalyse aus theoretischer Sicht. Zeitschrift für Betriebswirtschaft 50(7):800–808*

  • Kühnberger M, Eckstein P, Woithe M (1996) Die Diskriminanzanalyse als ein Instrument zur Früherkennung negativer Unternehmensentwicklungen: Eine empirische Studie auf der Basis simulierter Daten. Zeitschrift für Betriebswirtschaft 66(12):1449–1464*

  • Kunz J (2005) Aspekte simulationsbasierter Forschung. Zeitschrift für Controlling und Management 49(1):13–14

    Article  Google Scholar 

  • Küpper H-U (2007) Controlling und Operations Research: Der Beitrag quantitativer Theorie zur Selbstfindung und Akzeptanz einer praxisorientierten Disziplin. Zeitschrift für Betriebswirtschaft 77(7/8):735–758

    Article  Google Scholar 

  • Küpper H-U, Mattessich R (2005) Twentieth century accounting research in the German language area. Account Bus Financ Hist 15(3):345–410

    Article  Google Scholar 

  • Küppers G, Lenhard J, Shinn T (2006) Computer simulation: practice, epistemology, and social dynamics. In: Lenhard J, Küppers G, Shinn T (eds) Simulation. Pragmatic construction of reality. Springer, Dordrecht, pp 3–22

    Google Scholar 

  • Labro E, Vanhoucke M (2007) A simulation analysis of interactions among errors in costing systems. Account Rev 82(4):939–962

    Article  Google Scholar 

  • Labro E, Vanhoucke M (2008) Diversity in resource consumption patterns and robustness of costing systems to errors. Manag Sci 54(10):1715–1730

    Article  Google Scholar 

  • Lange C (1990) Transparenz und Flexibilität: Erfolgsfaktoren für Investitionsentscheidungen. Controlling 2(3):134–142*

  • Laux H, Gillenkirch RM, Schenk-Mathes HY (2012) Entscheidungstheorie, 8th edn. Springer, Berlin

    Book  Google Scholar 

  • Law AM (2007) Simulation modeling and analysis, 4th edn. McGraw-Hill, Boston

    Google Scholar 

  • Law AM (2009) How to build valid and credible simulation models. In: Rossetti MD, Hill RR, Johansson B, Dunkin A, Ingalls RG (eds) Proceedings of the 2009 winter simulation conference, Austin, pp 24–33

  • Lee JY (2003) Cost system research perspectives. Adv Manag Account 11:39–57

    Article  Google Scholar 

  • Lehtinen A, Kuorikoski J (2007) Computing the perfect model: why do economists shun simulation? Philos Sci 74(3):304–329

    Article  Google Scholar 

  • Lesch MS (2009) Nutzung von Investitionsrechenverfahren zur Berücksichtigung und zur Bewertung von Handlungsoptionen: Eine empirische Untersuchung der Einflussfaktoren und Wirkungen. Kovac, Hamburg

    Google Scholar 

  • Lorscheid I, Heine B-O, Meyer M (2012) Opening the ‘black box’ of simulations: increased transparency and effective communication through the systematic design of experiments. Comput Math Organ Theory 18(1):22–62

    Article  Google Scholar 

  • Macy MW, Willer R (2002) From factors to actors: computational sociology and agent-based modeling. Annu Rev Sociol 28:143–166

    Article  Google Scholar 

  • Manuj I, Mentzer JT, Bowers MR (2009) Improving the rigor of discrete-event simulation in logistics and supply chain research. Int J Phys Distrib Logist Manag 39(3):172–201

    Article  Google Scholar 

  • Maplesoft (2012) Maple 11. http://www.maplesoft.com/products/maple/history/pastversions_maple11.aspx?L=G. Accessed 8 May 2012

  • MARGA (2011) Das General Management Planspiel. http://www.marga.de/. Accessed 8 May 2012

  • Messner M, Becker A, Schäffer U, Binder C (2008) Legitimacy and identity in Germanic management accounting research. Eur Account Rev 17(1):129–159

    Article  Google Scholar 

  • Meyer M, Lorscheid I, Troitzsch KG (2009) The development of social simulation as reflected in the first ten years of JASSS: a citation and co-citation analysis. J Artif Soc Soc Simul 12(4)

  • Meyer M, Grisar C, Kuhnert F (2011) The impact of biases on simulation-based risk aggregation: modeling cognitive influences on risk assessment. J Manag Control 22(1):79–105

    Article  Google Scholar 

  • Nawrocki D (2001) The problems with Monte Carlo simulation. J Financ Plan 14(11):92–106

    Google Scholar 

  • Nevries P, Strauß E (2008) Aufgaben des Controllings im Rahmen des Risikomanagementprozesses: Eine empirische Untersuchung in deutschen Großkonzernen. Zeitschrift für Controlling und Management 52(2):106–111

    Article  Google Scholar 

  • Oracle (2010) Oracle Crystal Ball. http://www.oracle.com/crystalball/index.html. Accessed 12 May 2012

  • Oxford Dictionary of English (2010) In: Stevenson A (ed) Oxford Dictionary of English, 3rd edn. Oxford University Press, Oxford

  • Paetzmann K (2008) Corporate Governance: Strategische Marktrisiken, Controlling, Überwachung. Springer, Berlin

    Google Scholar 

  • Palisade (2012) @Risk: Ein neuer Standard für die Risikoanalyse. http://www.palisade.com/risk/de/. Accessed 12 May 2012

  • Pelz DC (1978) Some expanded perspectives on use of social science in public policy. In: Yinger JM, Cutler SJ (eds) Major social issues: a multidisciplinary view. New York, pp 346–357

  • Petticrew M, Roberts H (2006) Systematic reviews in the social sciences. Blackwell, Oxford

    Book  Google Scholar 

  • Powell SG, Baker KR (2004) The art of modeling with spreadsheets: management science, spreadsheet engineering, and modeling craft. Wiley, Hoboken

    Google Scholar 

  • Prokop J (2008) Sensitivitätsanalyse und Value at Risk als Instrumente des Marktpreisrisiko-Reporting nach IFRS 7. Betriebswirtschaftliche Forschung und Praxis 60(5):464–480*

  • Reichmann T, Haiber T, Fröhling O (1992) Open System Simulation: Konzept für ein flexibles Strategien-Controlling. Controlling 4(6):305–311*

  • Reiss J (2011) A plea for (good) simulations: nudging economics toward an experimental science. Simul Gaming 42(2):243–264

    Article  Google Scholar 

  • Rich RF (1977) Use of social science information by federal bureaucrats: knowledge for action versus knowledge for understanding. In: Weiss CH (ed) Using social research in public policy making. Lexington Books, Lexington, pp 199–211

    Google Scholar 

  • Richiardi M, Leombruni R, Saam N, Sonnessa M (2006) A common protocol for agent-based social simulation. J Artif Soc Soc Simul 9(1)

  • Sargent RG (2009) Verification and validation of simulation models. In: Rossetti MD, Hill RR, Johansson B, Dunkin A, Ingalls RG (eds) Proceedings of the 2009 winter simulation conference. Austin, pp 162–176

  • Schäfer-Kunz J (1997) Perspektiven der simulationsgestützten Kostenrechnung. Kostenrechnungspraxis 41(5):277–280*

  • Schäffer U, Binder C (2008) “Controlling” as an academic discipline: the development of management accounting and management control research in German-speaking countries between 1970 and 2003. Account Hist 13(1):33–74

    Article  Google Scholar 

  • Schäffer U, Steiners D (2004) Zur Nutzung von Controllinginformationen. Zeitschrift für Planung und Unternehmenssteuerung 15(4):377–404

    Google Scholar 

  • Schäffer U, Binder C, Gmür M (2006) Struktur und Entwicklung der Controllingforschung: Eine Zitations- und Kozitationsanalyse von Controllingbeiträgen in deutschsprachigen wissenschaftlichen Zeitschriften von 1970 bis 2003. Zeitschrift für Betriebswirtschaft 76(4):395–440

    Article  Google Scholar 

  • Schäffer U, Weber J, Matthias Mahlendorf (2012) Controlling in Zahlen. Stand und Entwicklungen des Controllings in den D-A-CH-Staaten: Ergebnisse aus fünf Jahren WHU-Controllerpanel. WHU Otto-Beisheim School of Management, Vallendar

  • Schmolke A, Thorbek P, DeAngelis DL, Grimm V (2010) Ecological models supporting environmental decision making: a strategy for the future. Trends Ecol Evol 25(8):479–486

    Article  Google Scholar 

  • Schnabl H (1985) Computersimulation und Modellbindung in der Ökonomie. Wirtschaftswissenschaftliches Studium 14(9):453–460

    Google Scholar 

  • Schneeweiß C, Alscher J (1987) Zur Disposition von Mehrprodukt-Lägern unter Verwendung der klassischen Losgrößenformel. Zeitschrift für Betriebswirtschaft 57(5/6):483–502*

  • Schoute M (2009) Antecedents and consequences of cost system design choices. Vrije Universiteit, Amsterdam

    Google Scholar 

  • Schrader U, Hennig-Thurau T (2009) VHB-Jourqual2: methods, results and implications of the German academic association for business research’s journal ranking. Bus Res 2(2):180–204

    Article  Google Scholar 

  • Selto FH, Widener SK (2004) New directions in management accounting research: insights from practice. Adv Manag Account 12:1–35

    Article  Google Scholar 

  • Steinle C, Harmening S (1991) Strategische Planung mit dem PC: Grundüberlegungen und praktische Lösungshinweise. Zeitschrift für Planung und Unternehmenssteuerung 2(2):119–139*

  • Strugalla R (1997) Prozessorientierte Kostensimulation: Zur besseren Entscheidungsfindung bei der Planung von Produktionssystemen. Kostenrechnungspraxis 41(3):151–155*

  • Sugiyama S (2008) Monte Carlo simulation/risk analysis on a spreadsheet: review of three software packages. Foresight 9:36–42

    Google Scholar 

  • Sulistio A, Yeo CS, Buyya R (2004) A taxonomy of computer-based simulations and its mapping to parallel and distributed systems simulation tools. Softw: Pract Exp 34(7):653–673

  • Thomson Reuters (2012) Social Science Citation. Index. http://science.thomsonreuters.com/mjl/publist_ssci.pdf. Accessed 20 March 2012

  • Tranfield D, Denyer D, Smart P (2003) Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br J Manag 14(3):207–222

    Article  Google Scholar 

  • Troßmann E, Baumeister A (2008) Gestaltungsaspekte einer risikoorientierten Lebenszyklusrechnung im Maschinenbau. Zeitschrift für Controlling und Management 52(2):99–105*

  • VHB (2008) VHB-JOURQUAL 2. http://vhbonline.org/service/jourqual/jq2/. Accessed 27 May 2008

  • VHB (2011) VHB-JOURQUAL 2.1. http://vhbonline.org/service/jourqual/vhb-jourqual-21-2011/. Accessed 16 May 2012

  • Viemann K (2005) Risikoadjustierte Performancemaße. Zeitschrift für Planung und Unternehmenssteuerung 16(3):373–380*

  • Wagenhofer A (2006) Management accounting research in German-speaking countries. J Manag Account Res 18(1):1–19

    Article  Google Scholar 

  • Warnick B (1989a) Implementierung einer PC-gestützten Nutzschwellenanalyse. Kostenrechnungspraxis 33(1):33–36*

  • Warnick B (1989b) Implementierung einer PC-gestützten Nutzschwellenanalyse (Teil II). Kostenrechnungspraxis 33(2):83–86*

  • Warnick B (1991) Simulation langfristiger Entscheidungswirkungen auf Basis prozessorientierter Ergebnisrechnungen. Kostenrechnungspraxis 35(6):319–323*

  • Weber J, Schäffer U (1998) Controlling-Entwicklung im Spiegel von Stellenanzeigen 1990–1994. Kostenrechnungspraxis 42(4):227–233

    Google Scholar 

  • Weber J, Schäffer U (2008) Einführung in das Controlling, 12th edn. Schäffer-Poeschel, Stuttgart

    Google Scholar 

  • Weber J, Weißenberger BE, Liekweg A (1999) Risk tracking and reporting: Unternehmerisches Chancen- und Risikomanagement nach dem KonTraG. Advanced Controlling. Wiley-VCH, Weinheim

    Google Scholar 

  • Weißenberger BE (2007) IFRS für Controller: Einführung, Anwendung, Fallbeispiele. Haufe, Freiburg

    Google Scholar 

  • Weißenberger BE, Löhr B (2007) Planung und Unternehmenserfolg: Stylized Facts aus der empirischen Controllingforschung im deutschsprachigen Raum von 1990–2007. Zeitschrift für Planung und Unternehmenssteuerung 18(4):335–363

    Article  Google Scholar 

  • Wilde KD (1982) Langfristige Prognosen in der strategischen Planung: Methodik und Verfahrensvergleich anhand praktischer Beispiele. Zeitschrift für Betriebswirtschaft 52(10):923–941*

  • Willeke A (1998) Risikoanalyse in der Energiewirtschaft. Zeitschrift für betriebswirtschaftliche Forschung 50(12):1146–1164*

  • Winkler H, Slamanig M (2008) Konzeption eines aktivitätsorientierten Instruments zur Anlaufkostenplanung. Zeitschrift für Planung und Unternehmenssteuerung 19(1):85–106*

  • Wolf K (2003) Risikoaggregation anhand der Monte-Carlo-Simulation. Controlling 15(10):565–572*

  • Wolf K (2009) Monte-Carlo-Simulation: Einsatz im Rahmen der Unternehmensplanung. Controlling 21(10):545–552*

  • Wunderlich J (2002) Rechnerbasierte Kostensimulation für komplexe Produktionssysteme. Kostenrechnungspraxis 46(4):255–261*

  • Zander D (1998) Break-Even-Analyse unter Unsicherheit. Kostenrechnungspraxis 42(3):165–172

    Google Scholar 

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Acknowledgments

An earlier version of the paper benefited from discussions at the EAA Annual Congress in Rome. We also would like to thank Iris Lorscheid, Klaus G. Troitzsch and the two anonymous reviewers for their helpful comments. Finally, we would like to thank Jonas Hauke for his support in screening the business informatics and operations research literature.

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  1. Institute of Management Control and Simulation, 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

See Tables 11 and 12.

Table 11 Data extraction form (part 1 of 2)
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Table 12 Data extraction form (part 2 of 2)
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Grisar, C., Meyer, M. Use of simulation in controlling research: a systematic literature review for German-speaking countries. Manag Rev Q 66, 117–157 (2016). https://doi.org/10.1007/s11301-015-0117-0

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