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 (Universidad Autónoma de Aguascalientes, Mexico), Fen Wang (Central Washington University, USA), Ovsei Gelman (Universidad Nacional Autónoma de México, Mexico) and Miroljub Kljajic (University of Maribor, Slovenia)
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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