On the identity of decision support systems (abstract only)

Abstract

Lack of consensus on the identification of Decision Support Systems (DSS) in the market and in the academic indicates that DSS as a field is not maturely defined and modeled. Moreover, an integrated view of the field is necessary to assess the role and value of DSS products and services. Such view is also a prerequisite for understanding integration and the potential contributions of Artificial Intelligence (AI) and Expert Systems (ES).To understand the role and value of a product or a service called “Decision Support System,” one has to evaluate such tools and services in the context of the decision-action process within the organization. The process involves a sequence of ten phases: Utility structure: setting goals and overall direction.Understanding cause and effect relationship relevant to the goals. (A logical deduction of the utility structure, a mental modeling)Observation and measurement of the internal and external operation environment.Detection of problems and opportunities.Analysis and model formulation. (Formal and specific modeling, making “Situation Models”)Seeking alternative solutions. (Making scenarios)Processing data through models to produce information.Processing information through optimization criteria to produce choiceActing according to the chosen strategy which involves: Processing data to produce information as instructions.Processing material, labor, and instructions to produce goods and/or services.Verifying model prediction with actual results to guide both the detection and modeling phases.Four categories of information are identified to support these phases: Operational information as instructions (phase 9.)Historical information to generate historical scenarios (phase 6.)Information as prediction of the values of alternatives (phase 7.)Information as patterns of data, phenomenon and experiences to facilitate abstraction (phase 2 and phase 5.)Although, there are many views and definitions of MIS and DSS, they can be divided into two categories. The first category has a hierarchical view which looks at “DSS as providing a higher level of decision support than does MIS,” [2]. The second category looks at EDP to provide transaction processing and DSS to provide decision support for managerial control and strategic planning, both under the umbrella of MIS, [1].A variation of the second view is being suggested with the emphasis on the capability that a support system provides rather than at what level of the organizational hierarchy it is used. Therefore, with this view, it is the MIS that is growing with new capabilities: from EDP to database query, to DSS, and in the future, to ES and AI.The model includes positions of the future contributions of AI and Expert Systems. AI and ES, often used interchangeably, pertain to a verity of rather different areas and systems, all sharing the common goal of reaching near human intelligence capability. As the field of AI/ES grows, more identifying titles, such as “Natural Language Processor” and “ Model Management” are being used. In the context of the presented model, ES is defined as a system which can search the knowledge base and construct a correct “situation model.”Finally, it is concluded that the synergistic effects of ES and DSS will bring more excitements. On one hand, ES plus Natural Language interface will invite more executives to use DSS modeling capabilities added with qualitative and judgmental features of ES [3]. On the other hand widespread practice of modeling will lead to clear analysis of many expertise which in turn can lead to development of new Expert Systems.