Abstract
Models to understand the impact of management practices on retail performance are often simplistic and assume low levels of noise and linearity. Of course, in real life, retail operations are dynamic, nonlinear and complex. To overcome these limitations, we investigate discrete-event and agent-based modeling and simulation approaches. The joint application of both approaches allows us to develop simulation models that are heterogeneous and more life-like, though poses a new research question: When developing such simulation models one still has to abstract from the real world, however, ideally in such a way that the ‘essence’ of the system is still captured. The question is how much detail is needed to capture this essence, as simulation models can be developed at different levels of abstraction. In the literature the appropriate level of abstraction for a particular case study is often more of an art than a science. In this paper, we aim to study this question more systematically by using a retail branch simulation model to investigate which level of model accuracy obtains meaningful results for practitioners. Our results show the effects of adding different levels of detail and we conclude that this type of study is very valuable to gain insight into what is really important in a model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bakken D (2007). Visualise it: Agent-based simulations may help you make better marketing decisions. Market Research 19(4): 22–29.
Baxter N, Collings D and Adjali I (2003). Agent-based modelling—Intelligent customer relationship management. BT Technol J 21(2): 126–132.
Baydar C (2003). Agent-based modelling and simulation of store performance for personalised pricing. In: Chick SE, Sanchez PJ, Ferrin DM and Morrice DJ (eds). Proceedings of the 2003 Winter Simulation Conference. New Orleans, Louisiana, IEEE: Piscataway, NJ, pp 1759–1764.
Berman O and Larson RC (2004). A queuing control model for retail services having back room operations and cross-trained workers. Comput Opns Res 31: 201–222.
Birdi K et al (2008). The impact of human resource and operational management practices on company productivity: A longitudinal study. Pers Psychol 61(3): 467–501.
Boero R and Squazzoni F (2005). Does empirical embeddedness matter? Methodological issues on agent-based models for analytical social science. JASSS 8(4)6. http://jasss.soc.surrey.ac.uk/8/4/6.html.
Bonabeau E (2002). Agent-based modeling: Methods and techniques for simulating human systems. PNAS 99(Suppl 3): 7280–7287.
Cao J (1999). Evaluation of advertising effectiveness using agent-based modeling and simulation. Proceedings of 2nd UK Workshop of SIG on Multi-Agent Systems. Hewlett-Packard Laboratories: Bristol, UK.
Clegg CW et al (2002). An international survey of the use and effectiveness of modern manufacturing practices. Hum Factor Ergon Man 12: 171–191.
Csik B (2003). Simulation of competitive market situations using intelligent agents. Periodica Polytechnica Social and Management Sciences 11: 83–93.
Darley V, von Tessin P and Sanders D (2004). An agent-based model of a corrugated-box factory: The trade-off between finished goods stock and on-time-in-full delivery. In: Coelho H and Espinasse B (eds). Proceedings of the 5th Workshop on Agent-Based Simulation. Lisbon, Portugal. SCS Publishing House: Erlangen, San Diego.
Delbridge R et al (2006). The Organisation of Productivity: Re-thinking Skills and Work Organisation. Advanced Institute of Management Research (AIM): UK.
Department of Trade and Industry (2003). UK Productivity and Competitiveness Indicators DTI Economics Paper No 6, UK.
Dubiel B and Tsimhoni O (2005). Integrating agent based modeling into a discrete event simulation. In: Kuhl ME, Steiger NM, Armstrong FB and Joines JA (eds). Proceedings of the 2005 Winter Simulation Conference. Orlando, FL, IEEE: Piscataway, NJ, pp 1029–1037.
Fornell C et al (1996). The American customer satisfaction index: Nature, purpose, and findings. J Marketing 60: 7–18.
Garcia R (2005). Uses of agent-based modeling in innovation/new product development research. J Prod Innovat Mngt 22: 380–398.
Greasley A (2005). Using DEA and simulation in guiding operating units to improved performance. J Opl Res Soc 56: 727–731.
Hillier FS and Lieberman GJ (2005). Introduction to Operations Research 8th edn. McGraw-Hill: New York.
Jager W (2007). The four P’s in social simulation, a perspective on how marketing could benefit from the use of social simulation. J Bus Res 60: 868–875.
Jager W et al (2000). Behaviour in commons dilemmas: Homo Economicus and Homo Psychologicus in an ecological-economic model. Ecol Econ 35(3): 357–379.
Janssen MA and Jager W (2001). Fashions, habits and changing preferences: Simulation of psychological factors affecting market dynamics. J Econ Psychol 22(6): 745–772.
Janssen MA and Jager W (2002). Simulating diffusion of green products: Co-evolution between firms and consumers. J Evol Econ 12: 283–306.
Janssen MA and Ostrom E (2006). Empirically based, agent-based models. Ecol Soc 11(2): 37.
Kitazawa K and Batty M (2004). Pedestrian behaviour modelling: An application to retail movements using a genetic algorithm. In: Proceedings of the 7th International Conference on Design and Decision Support Systems in Architecture and Urban Planning. St Michelsgestel, The Netherlands.
Koritarov V (2004). Real-world market representation with agents: Modeling the electricity market as a complex adaptive system with an agent-based approach. IEEE Power and Energy Magazine 2(4): 39–46.
Kotz S and van Dorp JR (2004). Beyond Beta: Other Continuous Families of Distributions with Bounded Support and Applications. World Scientific Publishing Company: Singapore.
Law AM and Kelton WD (1991). Simulation Modeling and Analysis. 2nd edn. McGraw-Hill: New York.
Moss S and Edmonds B (2005). Sociology and simulation: Statistical and qualitative cross-validation. Am J Sociol 110: 1095–1131.
Nicholson M, Clarke I and Blakemore M (2002). One brand, three ways to shop’: Situational variables and multichannel consumer behaviour. International Review of Retail, Distribution and Consumer Research 12: 131–148.
Parunak HVD, Savit HR and Riolo RL (1998). Agent-based modeling vs. equation-based modeling: A case study and users’ guide. In: Sichman JS, Conte R and Gilbert N (eds). Proceedings of Multi-Agent Systems and Agent-Based Simulation. Lecture Notes in Artificial Intelligence (LNAI), Vol. 1534. Springer: Berlin, Germany, pp 10–25.
Patel S and Schlijper A (2004). Models of Consumer Behaviour. Smith Institute (Unilever): UK.
Pourdehnad J, Maani K and Sedehi H (2002). System dynamics and intelligent agent-based simulation: Where is the synergy? In: Davidsen PI et al (eds). Proceedings of the 20th International Conference of the System Dynamics Society. Palermo, Italy.
Rahmandad H and Sterman J (2008). Heterogeneity and network structure in the dynamics of diffusion: Comparing agent-based and differential equation models. Mngt Sci 54(5): 998–1014.
Rao AS and Georgeff MP (1995). BDI agents: From theory to practice. In: Lesser VR and Gasser L (eds). Proceedings of the 1st International Conference on Multi-Agent Systems. San Francisco, CA. MIT Press: Cambridge, MA, pp 312–319.
Reynolds JE et al (2005). Assessing the productivity of the UK retail sector. International Review of Retail, Distribution and Consumer Research 15: 237–280.
Robinson S (2004). Simulation: The Practice of Model Development and Use. John Wiley & Sons: West Sussex, UK.
Said LB and Bouron T (2001). Multi-agent simulation of consumer behaviours in a competitive market. Proceedings of the 10th European Workshop on Multi-Agent Systems, Modelling Autonomous Agents in A Multi-Agent World. Annecy, France.
Said LB, Bouron T and Drogoul A (2002). Agent-based interaction analysis of consumer behaviour. Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems, Bologna, Italy. ACM: New York, NY, pp 184–190.
Schenk TA, Loeffler G and Rau J (2007). Agent-based simulation of consumer behaviour in grocery shopping on a regional level. J Bus Res 60(8): 894–903.
Schwaiger A (2007). Modellierung und Analyse individuellen Konsumentenverhaltens mit probabilistischen Holonen PhD Thesis, Universität des Saarlandes, Germany.
Schwaiger A and Stahmer B (2003). SimMarket: Multi-agent based customer simulation and decision support for category management. In: Schillo M, Klusch M, Muller J and Tianfield H (eds). Lecture Notes in Artificial Intelligence (LNAI) Vol. 2831, Springer: Berlin, pp 74–84.
Shannon RE (1975). Systems Simulation: The Art and Science. Prentice-Hall: Englewood Cliffs, NJ.
Siebers PO et al (2008). Enhancing productivity: The role of management practices in closing the productivity gap. Advanced Institute of Management Research (AIM) Working Paper No. 065-February-2008, UK.
Siebers PO, Aickelin U, Celia H and Clegg CW (2007a). A multi-agent simulation of retail management practices. In: Wainer GA and Vakilzadian H (eds). Proceedings of the 2007 Summer Computer Simulation Conference, San Diego, CA. SCS Publishing House: Erlangen, San Diego, pp 959–966.
Siebers PO, Aickelin U, Celia H and Clegg CW (2007b). Using intelligent agents to understand management practices and retail productivity. In: Henderson SG et al (eds). Proceedings of the 2007 Winter Simulation Conference, Washington, DC. IEEE: Piscat-away, NJ, pp 2212–2220.
Siebers PO, Aickelin U, Celia H and Clegg CW (2009). Modelling and simulating retail management practices: A first approach. International Journal of Simulation and Process Modelling 5(3): 215–232.
Simon F and Usunier JC (2007). Cognitive, demographic and situational determinants of service customer preference for personnel-in-contact over self-service technology. Int J Res Mark 24: 163–173.
Simon HA (1996). The Sciences of the Artificial. 3rd edn. MIT Press: Cambridge, MA.
Twomey P and Cadman R (2002). Agent-based modeling of customer behavior in the telecoms and media markets. Info—The Journal of Policy, Regulation and Strategy for Telecommunications 4(1): 56–63.
Vriend NJ (1995). Self-organization of markets: An example of a computational approach. Comput Econ 8: 205–231.
Wall TD and Wood SJ (2005). Romance of human resource management and business performance and the case for big science. Hum Relat 58: 429–462.
XJTEK (2005). AnyLogic User’s Guide. XJ Technologies Company Ltd. St.: Petersburg, Russia.
Yi Y (1990). A critical review of consumer satisfaction. In: Zeithaml VA (ed). Review of Marketing. American Marketing Association: Chicago, IL, USA, pp 68–122.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Copyright information
© 2014 Operational Research Society
About this chapter
Cite this chapter
Siebers, P.O., Aickelin, U., Celia, H., Clegg, C.W. (2014). Towards the development of a simulator for investigating the impact of people management practices on retail performance. In: Taylor, S.J.E. (eds) Agent-Based Modeling and Simulation. The OR Essentials series. Palgrave Macmillan, London. https://doi.org/10.1057/9781137453648_7
Download citation
DOI: https://doi.org/10.1057/9781137453648_7
Publisher Name: Palgrave Macmillan, London
Print ISBN: 978-1-349-49773-7
Online ISBN: 978-1-137-45364-8
eBook Packages: Palgrave Business & Management CollectionBusiness and Management (R0)