Supporting organisational decision making in presence of uncertainty

Effective organizational decision-making often requires deep understanding of various aspects of an organisation such as goals, structure, business-As-usual operational processes etc. The large size of the organisation, its socio-Technical characteristics, and fast business dynamics make this a challenging endeavor. Current industry practice relies on human experts thus making organisational decision-making time-, effortand intellectually-intensive. This paper proposes a simulatable language capable of specifying the relevant aspects of enterprise in a machine-processable manner so as to support simulation-driven decision-making in presence of uncertainty. A possible implementation of the language is outlined. Validation of the proposed approach using a real-life example is discussed.