IDSSE-M: A Software System Engineering Methodology for Developing Intelligent Decision-Making Support Systems

IDSSE-M: A Software System Engineering Methodology for Developing Intelligent Decision-Making Support Systems

Manuel Mora, Fen Wang, Ovsei Gelman, Miroljub Kljajic
Copyright: © 2013 |Pages: 29
DOI: 10.4018/978-1-4666-4002-3.ch003
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Abstract

Decision-making Support Systems (DMSSs) have been traditionally designed and built by using mainly the Waterfall method, Prototyping-Evolutive, or Adaptive approach in the last three decades. In this paper, the authors argue that while such approaches have guided to DMSS developers, they have been also demanded for adding ad-hoc, non-standardized activities and extra techniques based on their own expertise due to the scarcity of open-access available information of them. Additionally, from a Software Systems Engineering (SSE) viewpoint, such approaches cannot be considered as well-defined methodologies. This article contributes to the research stream of SSE-based DMSS development methodologies by reporting an initial empirical evaluation of IDSSE-M, a free-access methodology for designing and building Intelligent Decision Support Systems. IDSSE-M extends and adapts Turban and Aronson’s DSS Building Paradigm (open access), and Saxena’s Decision Support Engineering Methodology (proprietary). IDSSE-M offers DMSS developers at least a moderate level of usefulness, compatibility, and results demonstrability, which leads to a positive, good and beneficial attitude of using the methodology.
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1. Introduction

Decision Support Systems (DSS) (Keen & Scott-Morton, 1978) or their current and integrated versions referred to as Decision-making Support Systems (DMSS) (Forgionne, Mora, Gupta, & Gelman, 2005), are Information Systems (IS) designed specially to support some, several or all phases of an individual, team, organizational or intra-organizational decision-making process. Ever since its origin in the early 1970s (Scott-Morton, 1971), organizations, mainly large-scale ones with available special staffs and external consultants, have pursued the development of DMSS (McCosh & Correa-Perez, 2006) - in order to achieve many of the expected benefits as reported in Table 1.

Table 1.
Benefits of using DMSS
Purposes and Needs for Using Model-Based DSS (DSS)Purposes and Needs for Using Executive-Based DSS (EIS)
• Improve the quality of decisions.
• Increase productivity of analysts.
• Facilitate communication between decision makers and analysts.
• Save analysis time.
• Support objective-based decisions.
• Reduce costs derived from wrong decisions.
• Incorporate decision-makers insights and judgments into analysis.		• Increased competition.
• A highly dynamic business environment.
• Need of a fast executive response.
• Need of timely executive information.
• Need of improved communications.
• Need of rapid status on operational data.
• Scan the external decision environment.
• Capture, filter, and focus on external and internal data.
Purposes and Needs for Using Knowledge-Based DSS (ES)Purposes and Needs for Using General-Based DSS (DMSS)
• Preserve valuable and scarce knowledge.
• Share valuable and scarce knowledge.
• Enhance problem solving abilities of users.
• Develop user’s job skills.
• Increase productivity.
• Improve quality of solution provided.
• Guide the user through the problem solving process.
• Provide explanations for recommended actions.		• Improve some or several phases of an individual, team or organizational decision-making process.
• Increase the probabilities of better outcomes of a decision-making process.
• Improve the decision makers’ shared-vision of the organization
• Seek efficiency and effectiveness of top decision makers regarding decisional tasks.
• Explore consequences of critical decisions before them be taken and implemented.

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