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Towards the development of a simulator for investigating the impact of people management practices on retail performance

By Peer-Olaf Siebers, Uwe Aickelin, Helen Celia and Chris Clegg


Often models for understanding the impact of management practices on retail performance are developed under the assumption of stability, equilibrium and linearity, whereas retail operations are considered in reality to be dynamic, non-linear and complex. Alternatively, discrete event and agent-based modelling are approaches that allow the development of simulation models of heterogeneous non-equilibrium systems for testing out different\ud scenarios.\ud When developing simulation models one has to abstract and simplify from the real world, which means that one has to try and capture the ‘essence’ of the system required for\ud developing a representation of the mechanisms that drive the progression in the real system. Simulation models can be developed at different levels of abstraction. To know the\ud appropriate level of abstraction for a specific application is often more of an art than a science. We have developed a retail branch simulation model to investigate which level of\ud model accuracy is required for such a model to obtain meaningful results for practitioners

Publisher: Palgrave Macmillan
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Provided by: Nottingham ePrints

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  10. (2005). Assessing the productivity of the UK retail sector.
  11. (1995). BDI agents: From theory to practice. In:
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  16. (1999). Evaluation of advertising effectiveness using agent-based modeling and simulation. In:
  17. (2001). Fashions, habits and changing preferences: Simulation of psychological factors affecting market dynamics.
  18. (1996). H A doi
  19. (2008). Heterogeneity and network structure in the dynamics of diffusion: Comparing agent-based and differential equation models.
  20. (2005). Integrating agent based modeling into a discrete event simulation. In:
  21. (2009). Modelling and simulating retail management practices: A first approach. doi
  22. (2001). Multi-agent simulation of consumer behaviours in a competitive market. In:
  23. (2002). One brand, three ways to shop”: Situational variables and multichannel consumer behaviour.
  24. (2004). Pedestrian behaviour modelling: An application to retail movements using a genetic algorithm. In:
  25. (2008). Petrescu A and Peixoto A
  26. (2005). Romance of human resource management and business performance and the case for big science. doi
  27. (1995). Self-organization of markets: An example of a computational approach. doi
  28. (2003). Simulation of competitive market situations using intelligent agents.
  29. (2004). Simulation: The practice of model development and use,
  30. (2005). Sociology and simulation: Statistical and qualitative cross-validation.
  31. (2002). System dynamics and intelligent agent-based simulation: Where is the synergy? In: Davidsen P I, Mollona E,
  32. (1975). Systems simulation: The art and science. Prentice-Hall: Englewood Cliffs,
  33. (2007). The four P's in social simulation, a perspective on how marketing could benefit from the use of social simulation.
  34. (2008). The impact of human resource and operational management practices on company productivity: A longitudinal study. doi
  35. (2006). The organisation of productivity: Re-thinking skills and work organisation.
  36. (2003). UK productivity and competitiveness indicators.
  37. (2005). Uses of agent-based modeling in innovation / new product development research.
  38. (2007). Visualise it: Agent-based simulations might help to make better marketing decisions. Market Research. Winter 2007:22-29 • Baxter N, Collings D and Adjali I (2003). Agent-based modelling - intelligent customer relationship management.
  39. W (2007b). Using intelligent agents to understand management practices and retail productivity. In: Henderson

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