Using simulation to address intrinsic complexity in multi-modelling of enterprises for decision making

Modern enterprises are complex systems operating in increasingly dynamic environment and are thus subject to multiple inter-dependent change drivers. The size and complexity of modern enterprises ensures that the understanding of all aspects: why, what and how, is available only for highly localized parts distributed amongst multiple stakeholders. Existing enterprise modelling tools cater principally to only one of the three aspects. Consequently, the problems under consideration need to be decomposed into aspect-specific sub-problems, to be solved independently, and part-solutions integrated into a consistent whole: the intrinsic complexity in multi-modelling for decision making. Current practice relies extensively on human expertise to overcome the intrinsic complexity - a time-, cost- and effort-intensive endeavour. This paper proposes an approach supported by a language that enables specification of the why, what and how aspects in a localised integrated manner for each stakeholder of interest. The simulation-capable nature of the language leads to informed data-driven decision-making thus significantly reducing dependence on human expertise. Moreover, the approach enables less experienced users to function at the level of experts. A real-life case study illustrating the proposed approach is presented.