Towards Actionable Business Intelligence: Can System Dynamics Help?

Abstract : Business intelligence (BI) and data analytics provide modern enterprises with insights about internal operations, performance, as well as environmental trends, and enable them to make data-driven decisions. Insights resulting from these systems often suggest several alternative changes or corrective actions within the enterprise. In this context, to trade-off and find the most proper action(s) is a non-trivial task due to existing dynamics and complexities of the enterprise. This paper proposes a model-based approach to support the analysis and selection of best alternative actions in adaptive enterprise contexts. The proposed approach links and synthesizes two existing modeling frameworks, the Business Intelligence Model (BIM) and System Dynamics, in a systematic step-by-step way to assist decision makers in finding best response action(s) from a given set of alternatives, and hence to make BI more actionable and understandable. The applicability of this approach in illustrated in a scenario adapted from literature.
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Communication dans un congrès
Ulrich Frank; Pericles Loucopoulos; Óscar Pastor; Ilias Petrounias. 7th IFIP Working Conference on The Practice of Enterprise Modeling (PoEM), Nov 2014, Manchester, United Kingdom. Springer, Lecture Notes in Business Information Processing, LNBIP-197, pp.246-260, 2014, The Practice of Enterprise Modeling. 〈10.1007/978-3-662-45501-2_18〉
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Soroosh Nalchigar, Eric Yu, Steve Easterbrook. Towards Actionable Business Intelligence: Can System Dynamics Help?. Ulrich Frank; Pericles Loucopoulos; Óscar Pastor; Ilias Petrounias. 7th IFIP Working Conference on The Practice of Enterprise Modeling (PoEM), Nov 2014, Manchester, United Kingdom. Springer, Lecture Notes in Business Information Processing, LNBIP-197, pp.246-260, 2014, The Practice of Enterprise Modeling. 〈10.1007/978-3-662-45501-2_18〉. 〈hal-01282004〉

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