Improving simulation model analysis and communication via design of experiment principles: an example from the simulation-based design of cost accounting systems

Simulation offers management accounting research many benefits, such as the ability to model and to experiment with complex and large systems. At the same time, the acceptance of this method is hampered by a feeling of complexity often associated with simulation models and their behavior, as well as with challenges in communicating the models’ results. This study shows how these challenges can be addressed via the systematic use of design of experiment (DOE) principles. The DOE process framework is applied to a simulation model of a cost accounting system that is used to quantitatively evaluate two different methods for the allocation of service costs. As a result, we not only demonstrate the potential and benefits of simulation in the field of management accounting, but also show how DOE principles can help to improve understandings of simulation model behavior and the communication of simulation results.

[1]  Robin Cooper,et al.  The design of cost management systems : text and cases , 1999 .

[2]  Anders Mårtensson,et al.  Extending Rigor and Relevance: Towards Credible, Contributory and Communicable Research , 2007, ECIS.

[3]  Maryanne M. Mowen,et al.  Cost Management: Accounting and Control , 1994 .

[4]  C.P.A. Fred Jacobs,et al.  Accuracy of Service Cost Allocations , 1999 .

[5]  John T. Mentzer,et al.  Improving the rigor of discrete‐event simulation in logistics and supply chain research , 2009 .

[6]  G. Nigel Gilbert,et al.  Simulation for the social scientist , 1999 .

[7]  Madhav V. Rajan,et al.  Cost Accounting: A Managerial Emphasis , 1972 .

[8]  Roberto Leombruni,et al.  A Common Protocol for Agent-Based Social Simulation , 2006, J. Artif. Soc. Soc. Simul..

[9]  Paul Davidsson,et al.  Agent Based Social Simulation: A Computer Science View , 2002, J. Artif. Soc. Soc. Simul..

[10]  Eva Labro,et al.  Using simulation methods in accounting research , 2015 .

[11]  Matthias Meyer,et al.  Typical Pitfalls of Simulation Modeling - Lessons Learned from Armed Forces and Business , 2012, J. Artif. Soc. Soc. Simul..

[12]  G. Nigel Gilbert,et al.  Agent-Based Models , 2007 .

[13]  Stephen Hansen,et al.  Evaluating Heuristics Used When Designing Product Costing Systems , 2011, Manag. Sci..

[14]  Björn Niehaves,et al.  A Framework for Epistemological Perspectives on Simulation , 2005, J. Artif. Soc. Soc. Simul..

[15]  Matthias Meyer,et al.  Use of simulation in controlling research: a systematic literature review for German-speaking countries , 2016 .

[16]  Mario Vanhoucke,et al.  A Simulation Analysis of Interactions Among Errors in Costing Systems , 2007 .

[17]  Matthias Meyer,et al.  Opening the ‘black box’ of simulations: increased transparency and effective communication through the systematic design of experiments , 2011, Computational and Mathematical Organization Theory.

[18]  Friederike Wall,et al.  Agent-based modeling in managerial science: an illustrative survey and study , 2014, Review of Managerial Science.

[19]  Walter A. Hendricks,et al.  The Sampling Distribution of the Coefficient of Variation , 1936 .

[20]  Ralph Stair,et al.  Managerial Decision Modeling with Spreadsheets , 2002 .

[21]  Bor-Yi Tsay,et al.  Fundamental managerial accounting concepts , 2008 .

[22]  Matthias Meyer,et al.  The Development of Social Simulation as Reflected in the First Ten Years of JASSS: a Citation and Co-Citation Analysis , 2009, J. Artif. Soc. Soc. Simul..

[23]  Jiju Antony,et al.  Design of experiments for engineers and scientists , 2003 .

[24]  Matthias Meyer,et al.  Effectively combining experimental economics and multi-agent simulation: suggestions for a procedural integration with an example from prediction markets research , 2011, Computational and Mathematical Organization Theory.

[25]  Dirk P. Kroese,et al.  Simulation and the Monte Carlo method , 1981, Wiley series in probability and mathematical statistics.

[26]  K. Sivaramakrishnan,et al.  A Critical Overview of the Use of Full‐Cost Data for Planning and Pricing , 2002 .

[27]  Maryanne M. Mowen,et al.  Cost Management: Accounting and Control, 6th Edition , 2007 .

[28]  Stewart Robinson,et al.  Simulation: The Practice of Model Development and Use , 2004 .

[29]  Patrick Grim,et al.  What kind of science is simulation? , 2007, J. Exp. Theor. Artif. Intell..

[30]  Romina Martin,et al.  Describing human decisions in agent-based models - ODD + D, an extension of the ODD protocol , 2013, Environ. Model. Softw..

[31]  Julian Reiss,et al.  A Plea for (Good) Simulations: Nudging Economics Toward an Experimental Science , 2011 .

[32]  Frank H. Selto,et al.  Cost Management: Strategies for Business Decisions , 1994 .

[33]  Stephan Leitner,et al.  A simulation analysis of interactions among intended biases in costing systems and their effects on the accuracy of decision-influencing information , 2014, Central Eur. J. Oper. Res..

[34]  Ramji Balakrishnan,et al.  Causality in the context of analytical models and numerical experiments , 2014 .

[35]  J. Gareth Polhill,et al.  The ODD protocol: A review and first update , 2010, Ecological Modelling.

[36]  Mario Vanhoucke,et al.  Diversity in Resource Consumption Patterns and Robustness of Costing Systems to Errors , 2008, Manag. Sci..

[37]  Kathleen M. Carley,et al.  Simulation modeling in organizational and management research , 2007 .

[38]  Thomas G. Calderon,et al.  How Large Corporations Use Data Mining to Create Value , 2003 .

[39]  Birgit Müller,et al.  A standard protocol for describing individual-based and agent-based models , 2006 .

[40]  J. S. Hunter,et al.  Statistics for Experimenters: Design, Innovation, and Discovery , 2006 .

[41]  Dirk P. Kroese,et al.  Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics) , 1981 .

[42]  Robert Axelrod.  Advancing the art of simulation in the social sciences , 1997 .

[43]  Charles T. Horngren.  Management and Cost Accounting , 2001 .

[44]  Kathleen M. Eisenhardt,et al.  Developing Theory Through Simulation Methods , 2006 .

[45]  Jack P. C. Kleijnen,et al.  State-of-the-Art Review: A User's Guide to the Brave New World of Designing Simulation Experiments , 2005, INFORMS J. Comput..