Updated Architectures for the Integration of Decision Making Support Functionalities

Author(s):  
Guisseppi A. Forgionne

Information systems research continues to examine ways to improve support for decision making. The evolution from simple data access and reporting to complex analytical, creative, and artificially intelligent support for decision making persists (Holsapple & Whinston, 1996). In the evolution, existing information systems still, and new intelligent systems have been created to, provide the desired decision making support. By studying the existing, and new, systems’ characteristics, advantages, and disadvantages, researchers and practitioners can better design, develop, and implement robust decision making support systems (Kumar, 1999). The original article facilitated such study by presenting and illustrating the underlying information system architectures for robust decision making support (Forgionne, 2005). This article updates the original by offering additional contributions to the subject. New literature on intelligent decision making support is examined, and the relevant findings are discussed. The title has been modified slightly to reflect the updates.

Author(s):  
Guisseppi A. Forgionne

Because of the importance to individual, group, and organizational success, information systems research has examined ways to improve support for decision making for the last three decades. The research has generated a variety of information systems to provide the necessary support. In the process, there has been an evolution from simple data access and reporting to complex analytical, creative, and artificially intelligent support for decision making (Holsapple & Whinston, 1996).


Author(s):  
Guisseppi A. Forgionne

Various information systems have evolved to support the decision making process. There are decision support systems (DSS), executive information systems (EIS), artificially intelligent systems (AIS), and integrated combinations of these systems. Each of the individual systems supports particular phases and steps of the decision making process, but none of the individual systems supports the entire process in an integrated and complete manner. The integrated systems alleviate the support deficiencies, and each of the integration approaches has specific advantages and disadvantages. By studying these advantages and disadvantages, researchers and practitioners can better design, develop, and implement robust decision making support systems. This chapter facilitates such study by presenting and illustrating the underlying information system architectures for robust decision making support.


Author(s):  
Guisseppi Forgionne ◽  
Manuel Mora ◽  
Jatinder N.D. Gupta ◽  
Ovsei Gelman

Decision-making support systems (DMSS) are computerbased information systems designed to support some or all phases of the decision-making process (Forgionne, Mora, Cervantes, & Kohli, 2000). There are decision support systems (DSS), executive information systems (EIS), and expert systems/knowledge-based systems (ES/KBS). Individual EIS, DSS, and ES/KBS, or pair-integrated combinations of these systems, have yielded substantial benefits in practice. DMSS evolution has presented unique challenges and opportunities for information system professionals. To gain further insights about the DMSS field, the original version of this article presented expert views regarding achievements, challenges, and opportunities, and examined the implications for research and practice (Forgionne, Mora, Gupta, & Gelman, 2005). This article updates the original version by offering recent research findings on the emerging area of intelligent decision-making support systems (IDMSS). The title has been changed to reflect the new content.


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