Intelligent agents offer a new and exciting way of understanding the world of work. Agent-based simulation (ABS), one way of using intelligent agents, carries great potential for progressing our understanding of management practices and how they link to retail performance. We have developed simulation models based on research by a multidisciplinary team of economists, work psychologists, and computer scientists. We will discuss our experiences of implementing these concepts working with a well-known retail department store. There is no doubt that management practices are linked to the performance of an organisation (Reynolds, Howard, Dragun, Rosewell, & Ormerod, 2005; Wall & Wood, 2005). Best practices have been developed, but when it comes down to the actual application of these guidelines considerable ambiguity remains regarding their effectiveness within particular contexts (Siebers, Aickelin, Battisti, et al., 2008). Most operational research (OR) methods can only be used as analysis tools once management practices have been implemented. Often they are not very useful for giving answers to speculative “what-if” questions, particularly when one is interested in the development of the system over time rather than just the state of the system at a certain point in time. Simulation can be used to analyse the operation of dynamic and stochastic systems. ABS is particularly useful when complex interactions between system entities exist, such as autonomous decision making or negotiation. In an ABS model the researcher explicitly describes the decision process of simulated actors at the micro level. Structures emerge at the macro level as a result of the actions of the agents and their interactions with other agents and the environment. We will show how ABS experiments can deal with testing and optimising management practices such as training, empowerment or teamwork. Hence, questions such as “will staff setting their own break times improve performance?” can be investigated.
Key Terms in this Chapter
Multi-Agent Model: To simulate a real-life situation a single agent is not sufficient; populations of agents are needed to model most scenarios. For instance, we need a population of customers, a group of managers, and various different sales staff. What makes multi-agent models interesting is that the agents do not exist in isolation or a linear fashion, but are able to communicate with and respond to other agents.
Agent: The word agent has many meanings in operational research. In our work, an agent refers to a single entity that will be simulated, that is, one sales staff, one manager, or one customer. Agents are modelled through state charts, describing how, when, and why they can change their behaviour.
Management Practices: Management practices usually refers to the working methods and innovations that managers use to improve the effectiveness of work systems. Common management practices include: empowering staff, training staff, introducing schemes for improving quality, and introducing various forms of new technology.
Agent-Based Simulation: Agent-based simulations are a relatively recent addition to the set of decision support tools. Agent-based simulation extends earlier simulation techniques by allowing the entities that are simulated to make “autonomous decisions” and to “negotiate” with other agents. For example, sales staff may decide when to take break times depending on how busy the shop is.