Abstract
In the current fast-changing and turbulent operational environments, the organizations are continually being pressured by many endogenous and exogenous environmental variables. Many and complex effects occur simultaneously and large volumes of data are available. For this reason, in a process-based organization, when change is demanded (e.g., business processes re-engineering) it is difficult to collect, and interpret, the complete information about the current state of the organization. Therefore, a problem is how to decide which design actions should be enacted with the incomplete information available from the executed business processes. In this context, this paper combines information systems engineering (DEMO business transactions design) and operation research (Markov theories) to contribute to the decision-making body of knowledge. As the result, this solution enforces the organization with resiliency capabilities that are triggered whenever any misalignment occurs. The proposed solution is evaluated through argumentation and by a qualitative comparison between two Markov theories (MDP and POMDP) based on a real-world case study.
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Notes
- 1.
These \(S\) triples conform to the representation proposed by [23] where a triple describes each system state and supports the subsequent simulation results.
- 2.
An example of this standard format could be consulted at [2].
- 3.
The full POMDP file is public available with doi:10.13140/2.1.4433.2326.
- 4.
- 5.
Toolbox public available at http://www7.inra.fr/mia/T/MDPtoolbox.
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Acknowledgments
This work was supported by Project nr 652643 (Respon-SEA-ble), under the title: “Sustainable oceans: our collective responsibility, our common interest. Building on real-life knowledge systems for developing interactive and mutual learning media" from H2020 programme.
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Guerreiro, S. (2015). Engineering the Decision-Making Process Using Multiple Markov Theories and DEMO. In: Aveiro, D., Pergl, R., Valenta, M. (eds) Advances in Enterprise Engineering IX. EEWC 2015. Lecture Notes in Business Information Processing, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-319-19297-0_2
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