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Simulation Methods Comparison in Business Process Domain

Author

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  • Roman Šperka

    (Department of Business Economics and Management, School of Business Administration, Silesian University)

Abstract

The main goal of this paper is to compare the results of an agent-based and Monte Carlo simulation experiments in business process negotiation between sellers and customers of a simple trading commodity. The motivation of the presented research is to find suitable method for predicting key performance indicators of a business company. The intention is to develop a software module in the future which might help the management of business companies to support their decisions. Microeconomic demand functions were used as a core element in the negotiation. Specifically, Marshallian demand function and Cobb-Douglas utility functions is introduced. The paper firstly presents some of the principles of agent-based and Monte Carlo simulation techniques, and demand function theory. Secondly, we present a conceptual model of a business company in terms of a simulation framework. Thirdly, a formalization of demand functions and their implementation in a seller-to-customer negotiation is introduced. Lastly, we discuss some of the simulation results in one year of selling commodities. The results obtained show that agent-based method is more suitable than Monte Carlo in the presented domain, and the demand functions could be used to predict the trading results of a company in some metrics.

Suggested Citation

  • Roman Šperka, 2015. "Simulation Methods Comparison in Business Process Domain," Working Papers 0019, Silesian University, School of Business Administration.
    • Handle: RePEc:opa:wpaper:0019
    as

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    References listed on IDEAS

    as
    1. Wilkinson, Ian & Young, Louise, 2002. "On cooperating: firms, relations and networks," Journal of Business Research, Elsevier, vol. 55(2), pages 123-132, February.
    2. Robert A. Pollak, 1969. "Conditional Demand Functions and Consumption Theory," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 83(1), pages 60-78.
    3. Hazhir Rahmandad & John Sterman, 2008. "Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models," Management Science, INFORMS, vol. 54(5), pages 998-1014, May.
    4. Brian Heath & Raymond Hill & Frank Ciarallo, 2009. "A Survey of Agent-Based Modeling Practices (January 1998 to July 2008)," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(4), pages 1-9.
    5. David Collings & A. A. Reeder & Iqbal Adjali & P. Crocker & M. H. Lyons, 1999. "Agent Based Customer Modelling," Computing in Economics and Finance 1999 1352, Society for Computational Economics.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    simulation; agent-based; Monte Carlo; trading; price negotiation; commodity; key performance indicators.;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C99 - Mathematical and Quantitative Methods - - Design of Experiments - - - Other
    • L21 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Business Objectives of the Firm
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics

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