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Event Management for Sensing Enterprises with Decision Support Systems

Event Management for Sensing Enterprises with Decision Support Systems Sensing enterprises make use of new technologies to capture real-time information and fed constantly the decision making process. Decision support systems (DSS) are exposed to these real-time events and it is possible to start the decision process from scratch in case any unexpected internal and external events take place. Thus, an event monitoring and management system should interact with the DSS to manage events that might affect their decisions. It should act as a supra-system to identify when decisions made are still valid or need to be reanalysed. The traditional configuration of DSS (where they collect internal and external information of the organization and the decision-maker is involved in the decision-making process) should be extended to treat event management using a monitoring and management system, which monitors internal and external information and facilitate the introduction of no monitored events. This monitor and manager systems become more and more necessary due to the incessant incorporation of new technologies that enables the companies to be more context-sensitive. Furthermore, this new and/or more accurate information, which is obtained for the organization, requires a proper management. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Data Science Springer Journals

Event Management for Sensing Enterprises with Decision Support Systems

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Publisher
Springer Journals
Copyright
Copyright © 2015 by Springer-Verlag Berlin Heidelberg
Subject
Economics / Management Science; Business/Management Science, general; Statistics for Business/Economics/Mathematical Finance/Insurance; Computing Methodologies
ISSN
2198-5804
eISSN
2198-5812
DOI
10.1007/s40745-015-0034-z
Publisher site
See Article on Publisher Site

Abstract

Sensing enterprises make use of new technologies to capture real-time information and fed constantly the decision making process. Decision support systems (DSS) are exposed to these real-time events and it is possible to start the decision process from scratch in case any unexpected internal and external events take place. Thus, an event monitoring and management system should interact with the DSS to manage events that might affect their decisions. It should act as a supra-system to identify when decisions made are still valid or need to be reanalysed. The traditional configuration of DSS (where they collect internal and external information of the organization and the decision-maker is involved in the decision-making process) should be extended to treat event management using a monitoring and management system, which monitors internal and external information and facilitate the introduction of no monitored events. This monitor and manager systems become more and more necessary due to the incessant incorporation of new technologies that enables the companies to be more context-sensitive. Furthermore, this new and/or more accurate information, which is obtained for the organization, requires a proper management.

Journal

Annals of Data Science – Springer Journals

Published: Apr 17, 2015

References

  • A design and application methodology for hierarchical production planning decision support systems in an enterprise integration context
    Vicens, E; Alemany, ME; Andrés, C; Guarch, JJ
  • The planning flexibility bottleneck in food processing industries
    Wezel, W; Donk, DP; Gaalman, G
  • A hierarchical decision support system for production planning (with case study)
    Özdamar, L; Bozyel, MA; Birbil, SI
  • Intelligent non-hierarchical manufacturing networks
    Shamsuzzoha, Ah; Rintala, S; Cunha, PF; Ferreira, PS; Kankaanpää, T; Maia Carneiro, L
  • Planning and control systems: a framework for analysis
    Anthony, RN
  • The new science of management decision
    Simon, HA
  • Past, present, and future of decision support technology
    Shim, JP; Warkentin, M; Courtney, JF; Power, DJ; Sharda, R; Carlsson, C
  • Descriptive framework for the field of data mining and knowledge discovery
    Peng, Y; Kou, G; Shi, Y; Chen, ZA
  • La falta de homogeneidad del producto (FHP) en las empresas cerámicas y su impacto en la reasignación del inventario
    Alarcón, F; Alemany, MME; Lario, FC; Oltra, RF
  • Behavioral operations in planning and scheduling
    Cegarra, J; Wezel, W
  • SYMIAN: analysis and performance improvement of the IT incident management process
    Bartolini, C; Stefanelli, C; Tortonesi, M
  • A model-driven DSS architecture for delivery management in collaborative supply chains with lack of homogeneity in products
    Boza, A; Alemany, MME; Alarcón, F; Cuenca, L
  • Enterprise interoperability
    Boza, A; Ortiz, A; Vicens, E; Poler, R
  • Hybrid control of supply chains: a structured exploration from a systems perspective
    Grefen, P; Dijkman, R