Abstract
Agents offer a new and exciting way of understanding the world of work. In this paper we describe the development of agent-based simulation models, designed to help to understand the relationship between people management practices and retail performance. We report on the current development of our simulation models which includes new features concerning the evolution of customers over time. To test the features we have conducted a series of experiments dealing with customer pool sizes, standard and noise reduction modes, and the spread of customers’ word of mouth. To validate and evaluate our model, we introduce new performance measure specific to retail operations. We show that by varying different parameters in our model we can simulate a range of customer experiences leading to significant differences in performance measures. Ultimately, we are interested in better understanding the impact of changes in staff behavior due to changes in store management practices. Our multi-disciplinary research team draws upon expertise from work psychologists and computer scientists. Despite the fact we are working within a relatively novel and complex domain, it is clear that intelligent agents offer potential for fostering sustainable organizational capabilities in the future.
Highlights
The retail sector has been identified as one of the biggest contributors to the productivity gap, whereby the productivity of the UK lags behind that of France, Germany and the USA [1, 2]
State charts show the different states an entity can be in and define the events that cause a transition from one state to another. This is exactly the information we need in order to represent our agents at a later stage within the simulation environment. We have found this form of graphical representation a useful part of the agent design process because it is easier for an expert in the real system to quickly take on board the model conceptualization and provide useful validation of the model structure and content
We predict that: Hypothesis 6: In the dynamic conditions, customer pool size will increase during the simulation run for each department up to a maximum value, and maintain this level for the run duration
Summary
The retail sector has been identified as one of the biggest contributors to the productivity gap, whereby the productivity of the UK lags behind that of France, Germany and the USA [1, 2]. A recent literature review of management practices and organizational productivity concluded that management practices are multidimensional constructs that generally do not demonstrate a straightforward relationship with productivity variables [4], and that both management practices and productivity measures must be context specific to be (respectively) effective and meaningful. There are many different types of simulation, each of which has its specific field of application. Agent-Based Simulation (ABS) is useful when complex interactions between system entities exist such as autonomous decision making or proactive behavior. Agent-Based Modeling (ABM) shows how micro-level processes affect macro level outcomes; macro level behavior is not explicitly modeled, it emerges from the micro-decisions made by the individual entities [6]
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