On complex adaptive systems and agent-based modelling for improving decision-making in manufacturing and logistics settings.

Purpose -- This paper aims to contribute to the tactical and operational decision making of manufacturing and logistics operations by providing novel insights into modelling and simulation, based on complex adaptive systems (CAS). Design/methodology/approach -- The research approach is theoretically based on CAS with agent-based modelling (ABM) as the implementation method. A case study is presented where an agent-based model has contributed to increased understanding and precision in decision making at a packaging company in the UK. Findings -- The results suggest that ABM provides decision-makers with robust and accurate "what-if scenarios of the dynamic interplay among several business functions. These scenarios can guide managers in the process of moving from policy space to performance space, i.e. concerning priorities of improvement efforts and choices of production/manufacturing policies, warehouse policies, customer service policies and logistics policies. Furthermore, it is found that ABM can include and pay attention to several aspects of CAS and thus provide understanding of, and explanation for, the patterns and effects which emerge in manufacturing and logistics settings. Practical implications -- Aided by agent-based models and simulations, practitioners' levels of intuition can be enhanced since patterns on the macro level emerge from agents' interactive behaviour. Together with insights from CAS these emergent patterns can be explained and understood, and are thus beneficial for the improvement of decision making in companies. Originality/value -- The case presented distinguishes this paper from what has been written in previous articles on the application of ABM, since such articles have not produced any empirically verified results after implementation of ABM. [ABSTRACT FROM AUTHOR]