An approach to address the intrinsic complexity in multi-modelling of enterprises for decision making

Kulkarni, Vinay, Barat, Souvik, Clark, Tony and Barn, Balbir ORCID logoORCID: (2015) An approach to address the intrinsic complexity in multi-modelling of enterprises for decision making. In: 2015 Summer Simulation Multi-Conference, July 26-29 2015, Chicago, Illinois, USA. . [Conference or Workshop Item] (Accepted/In press)

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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.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 16683
Notes on copyright: Access to full text restricted pending copyright check.
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Depositing User: Balbir Barn
Date Deposited: 01 Jun 2015 15:14
Last Modified: 29 Nov 2022 22:55

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