Agent-based modelling of quality management effects on organizational productivity

This paper presents the application of agent-based simulation as a modelling metaphor for investigating the relationship between quality management (QM) and organizational productivity. The effects of QM on organizational productivity are traditionally researched by inductive reasoning through statistical models. Adopting a macro (system) level, top-down approach, statistical models fall short of providing an explanatory account of micro-level factors like individual’s problem-solving characteristics or customer requirements complexity, because organizations are considered as black boxes in such models and hence constructs of QM are defined at an organizational level. The question is how an explanatory, bottom-up account of QM effects can be provided. By virtue of the agent-based modelling paradigm, an innovative model, fundamentally different from the dominant statistical models is presented to fill this gap. Regarding individuals’ characteristics, results show that a well-balanced organization comprised of similar agents (in terms of agents’ problem-solving time and accuracy) outperforms other scenarios. Furthermore, from the results for varying complexity of customer requirements, it can be argued that more intricacy does not always lead to less productivity. Moreover, the usefulness of quality leadership represented as a reinforcement learning algorithm is reduced in comparison to a random algorithm when the complexity of customer requirements increases. The results have been validated by face validation and real data.