Main Article Content
Research background: Managerial scientists use a lot of modelling techniques for business processes. In this paper we are focused on agent-based modelling and simulations, which emerged in the last two decades as a new approach. Autonomous and interacting intelligent agents are able to model and simulate complex systems in the business sphere. With the use of agent-based modelling and simulations we are able to understand how macro level outcomes are affected by micro level processes and vice versa.
Purpose of the article: The purpose of the paper is to introduce recent development in the area of agent-based modelling and simulations focused on the business domain. Managers often have to make difficult decisions under the uncertainty and high risks. Agent-based modelling can provide powerful tools for lowering those risks through a possibility of running experiments, which is normally impossible in economics. In the second part we want to support the usefulness of agent-based simulations with our own simulations.
Methods: The method used in this article is an agent-based simulation in a multi-agent system. We use a framework called MAREA. It is a simulation environment with integrated ERP system based on REA ontology. Our simulation model is based on a retail company that sells electronics. For simplicity, in our setup we trade with computer cables.
Findings & Value added: In our simulations we experimented with the quality of sales service provided by company’s sales representatives. We investigated the impact of quality of sales service on company KPIs under the changing environment circumstances represented by disturbance agent. The quality of sales service is a part of quality of service and thus it affects the perception of brand and loyalty of customers towards the company. In our simulation setup we work with two types of customers, long-term customers and new ones. The result is that quality of sales service has mostly positive effects on company KPIs.
Alan, A. K., Kabadayi, E. T., & Yilmaz, C. (2015). Cognitive and affective constituents of the consumption experience in retail service settings: effects on store loyalty. Service Business, 10(4). doi: 10.1007/s11628-015-0288-8.
Axtell, R. L. (2007). What economic agents do: How cognition and interaction lead to emergence and complexity. Review of Austrian Economics, 20(2–3). doi: 10.1007/s11138-007-0021-5.
Behrens, D. A., Berlinger, S., & Wall, F. (2013). Phrasing and timing information dissemination in organizations: results of an agent-based simulation. Lecture Notes in Economics and Mathematical Systems Artificial Economics and Self Organization, 669. doi: 10.1007/10.1007/978-3-319-00912-4_14.
Chhatwal, J., & He, T. (2015). Economic evaluations with agent-based modelling: an introduction. PharmacoEconomics, 33(5). doi: 10.1007/s40273-015-0254-2.
Davis, J. P., Eisenhardt, K. M., & Bingham, C. B. (2007). Developing theory through simulation methods. Academy of Management Review, 32(2). doi: 10.5465/AMR.2007.24351453.
Fagiolo, G., Birchenhall, C., & Windrum, P. (2007). Empirical validation in agent-based models: introduction to the special issue. Computational Economics, 30(3). doi: 10.1007/s10614-007-9109-z.
Fatima, S. S., Wooldridge, M., & Jennings, N. R. (2004). An agenda-based framework for multi-issue negotiation. Artificial Intelligence, 152(1). doi: 10.1016/S0004-3702(03)00115-2.
Gilbert, N., Ahrweiler, P., & Pyka, A. (2007). Learning in innovation networks: some simulation experiments. Physica A: Statistical Mechanics and Its Applications, 378(1). doi: 10.1016/j.physa.2006.11.050.
Gries, M., Kulkarni, Ch., Sauer, Ch., & Keutzer, K. (2003). Comparing analytical modeling with simulation for network processors: a case study. Retrieved from https://pdfs.semanticscholar.org/83a4/bc3623b74c360512224cb8227bb1dbfd3d51.pdf (19.03.2017).
He, Z., Wang, S., & Cheng, T. C. E. (2013). Competition and evolution in multi-product supply chains: An agent-based retailer model. International Journal of Production Economics, 146(1). doi: 10.1016/j.ijpe.2013.07.019.
Heppenstall, A. J., Evans, A. J., & Birkin, M. H. (2007). Genetic algorithm optimisation of an agent-based model for simulating a retail market. Environment and Planning B: Planning and Design, 34(6). doi: 10.1068/b32068.
Izumi, K., Toriumi, F., & Matsui, H. (2009). Evaluation of automated-trading strategies using an artificial market. Neurocomputing, 72(16–18). doi: 10.1016/j.neucom.2008.07.020.
Kotler, P., & Keller, K. L. (2016). Marketing management. Boston: Pearson.
Leombruni, R., & Richiardi, M. (2005). Why are economists sceptical about agent-based simulations? Physica A: Statistical Mechanics and Its Applications, 355(1). doi: 10.1016/j.physa.2005.02.072.
Li, J., Sheng, Z., & Liu, H. (2010). Multi-agent simulation for the dominant players’ behavior in supply chains. Simulation Modelling Practice and Theory, 18(6). doi: 10.1016/j.simpat.2010.02.001.
Li, G., & Shi, J. (2012). Agent-based modeling for trading wind power with uncertainty in the day-ahead wholesale electricity markets of single-sided auctions. Applied Energy, 99. doi: 10.1016/j.apenergy.2012.04.022.
Liu, Y., & Trivedi, K. S. (2006). Survivability quantification: the analytical modeling approach. Retrieved from http://people.ee.duke.edu/~kst/surv/IoJP.pdf (19.03.2017).
Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3). doi: 10.1057/jos.2010.3.
Marks, R. E. (2007). Validating simulation models: a general framework and four applied examples. Computational Economics, 30(3). doi: 10.1007/s10614-007-9101-7.
Midgley, D., Marks, R., & Kunchamwar, D. (2007). Building and assurance of agent-based models: An example and challenge to the field. Journal of Business Research, 60(8). doi: 10.1016/j.jbusres.2007.02.004.
North, M. J., Macal, C. M., Aubin, J. S., Thimmapuram, P., Bragen, M., Hahn, J., & Hampton, D. (2010). Multiscale agent-based consumer market modeling. Complexity, 15(5). doi: 10.1002/cplx.20304.
Rand, W., & Rust, R. T. (2011). Agent-based modeling in marketing: Guidelines for rigor. International Journal of Research in Marketing, 28(3). doi: 10.1016/j.ijresmar.2011.04.002.
Ren, F., Zhang, M., & Sim, K. M. (2009). Adaptive conceding strategies for automated trading agents in dynamic, open markets. Decision Support Systems, 46(3). doi: 10.1016/j.dss.2008.11.005.
Roozmand, O., Ghasem-Aghaee, N., Hofstede, G. J., Nematbakhsh, M. A., Baraani, A., & Verwaart, T. (2011). Agent-based modeling of consumer decision making process based on power distance and personality. Knowledge-Based Systems, 24(7). doi:10.1016/j.knosys.2011.05.001.
Runje, B., Krstić Vukelja, E., & Stepanić, J. (2015). Agent-based simulation of measuring the quality of services. Technical Gazette, 22(6). doi: 10.17559/TV-20150416093602.
Sandita, A. V., & Popirlan, C. I. (2015). Developing a multi-agent system in JADE for Information management in educational competence domains. Procedia Economics and Finance, 23. doi: 10.1016/S2212-5671(15)00404-9.
Schramm, M. E., Trainor, K. J., Shanker, M., & Hu, M. Y. (2010). An agent-based diffusion model with consumer and brand agents. Decision Support Systems, 50(1). doi: 10.1016/j.dss.2010.08.004.
Siebers, P., Aickelin, U., Celia, H., & Clegg, C. (2008). A multi-agent simulation of retail management practices. SSRN Electronic Journal. doi: 10.2139/ssrn.2831284.
Sun, J., Tang, J., Fu, W., & Wu, B. (2017). Hybrid modeling and empirical analysis of automobile supply chain network. Physica A: Statistical Mechanics and Its Applications, 473. doi: 10.1016/j.physa.2017.01.036.
Šperka, R., & Vymětal, D. (2013). MAREA - an education application for trading company simulation based on REA principles. Advances in Education Research, 30.
Terano, T. (2008). Beyond the KISS principle for agent-based social simulation. Journal of Socio-Informatics, 1(1). doi: 10.14836/jsi.1.1_175.
Twomey, P., & Cadman, R. (2002). Agent-based modelling of customer behaviour in the telecoms and media markets. Info, 4(1). doi: 10.1108/146366902 10426640.
Vanhaverbeke, L., & Macharis, C. (2011). An agent-based model of consumer mobility in a retail environment. Procedia - Social and Behavioral Sciences, 20. doi: 10.1016/j.sbspro.2011.08.024.
Vymětal, D., & Ježek, F. (2014). Demand function and its role in a business simulator. Munich Personal RePEc Archive, 54716.
Vymětal, D., Spišák, M., & Šperka, R. (2012). An influence of random number generation function to multiagent systems. Agent and Multi-Agent Systems. Technologies and Applications Lecture Notes in Computer Science, 7327. doi: 10.1007/978-3-642-30947-2_38.
Wall, F. (2014). Agent-based modeling in managerial science: an illustrative survey and study. Review of Managerial Science, 10(1). doi: 10.1007/s11846-014-0139-3.
Wang, M., Wang, H., Vogel, D., Kumar, K., & Chiu, D. K. W. (2009). Agent-based negotiation and decision making for dynamic supply chain formation. Engineering Applications of Artificial Intelligence, 22(7). doi: 10.1016/j.engappai.2008.09.001.
Wong, C. L., & Tjosvold, D. (1995). Goal interdependence and quality in services marketing. Psychology and Marketing, 12(3). doi: 10.1002/mar.4220120304.
Zhang, T., & Zhang, D. (2007). Agent-based simulation of consumer purchase decision-making and the decoy effect. Journal of Business Research, 60(8). doi: 10.1016/j.jbusres.2007.02.006.
Zhou, Z., Chan, W. K., & Chow, J. H. (2007). Agent-based simulation of electricity markets: a survey of tools. Artificial Intelligence Review, 28(4). doi: 10.1007/s10462-009-9105-x.