What is DSS?
The following is the IAADS view what DSS means. In particular it is the view of the Founders.

Decision Suppo...
tools. Obviously, the more powerful and valuable the data resources they draw on and the more engaging and
interactive the...
Success and failure of decision support systems: Learning as
we go
Much effort and money have been devoted to the developm...
participation, support and availability, and participatory learning processes. The case study
provided evidence of a perce...
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Dss - Decision Support System

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Dss - Decision Support System

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Transcript of "Dss - Decision Support System"

  1. 1. What is DSS? The following is the IAADS view what DSS means. In particular it is the view of the Founders. Decision Support Systems, DSS, jelled in the mid 70’s and early 80’s. Dr. Jerry Wagner started the first professional DSS conference and the DSS Transactions in 1981. At the time, Jerry was CEO of Execucom Systems Corporation. This conference and DSS transactions attracted large contributions from researchers and practitioners over its 10-year lifetime. They filled a need in closing the gap between Management Science/Operations Research and Information Systems. Jerry is now the Founder and Director of IAADS. IAADS is timely in that there is once again an urgent need to close what has become a widening gap. Today the world of MS/OR and IS are very much their own separate disciplines, as is apparent in their journals, professional societies and university curricula. In the early days, DSS was filled with magic – we thought that managers would be directly involved with computers to evaluate scenarios and support their decision-making! The dream lost steam and focus in about the mid 80’s. That was because “information” took over from “decisions” in the use of IT. DSS was largely reduced to spreadsheet reports and accessing and reporting historical data. Executive Information Systems (EIS) that followed DSS provided what became routine reporting systems for historical data with their routine, non-imaginative, complex, and even boring interfaces. Data base extraction and display and spreadsheet reports were very different from the DSS focus and skills for finding and analyzing creative solutions to support decision makers. Following EIS came data warehousing, business intelligence systems, and other information management tools all of which were about reporting historical data and not about DSS for “quantitatively rehearsing the future”. Thanks to our co-founder Dr. Peter Keen who has coined the new phrase “Rehearsing the Future” in his forthcoming new book (2003). That is what DSS is for! There is no reason to gather information for managers except to rehearse the future and make decisions about that future - and do this all in the face of vast uncertainty. The disciplines of Operations Research and Management Science provided the base of quantitative and modeling tools in the early days of DSS. There were a number of quantitative modeling tools for supporting these creative minds such as optimization, simulation, multi criteria analysis, decision trees, and statistical modeling and forecasting. Today there are many underutilized analytic and quantitative methods and many powerful new modeling and interface tools that give value to information. OR and MS were highly multidisciplinary. Anyone from any discipline that could contribute to creative problem solving was an asset. The Secretary of the Air Force James G. Roche recently delivered a speech in support of rejuvenating Operations Research. He said “The original ops researchers understood that to be effective, they needed teams of mathematicians, historians, military theorists, psychologists and economists, among others. They understood the natural complexity of war, to include second-order effects. War is not just a mechanical or scientific act. In practice, it is an art and science that operates in a foggy sea of strategy, politics and luck” (December 2002, OR/MS Today). Any CEO of any organization might very well use similar words. The multidisciplinary team approach to problem solving is very much an IAADS thrust, as it was for DSS in its intellectual and practical heyday. As an example, most DSS’s involve software and the “interface is the software” – the interface determines ease of use, mesh with decision processes and the range of model-data combinations of value to decision makers. Thus, IAADS has made interface design one of its core priorities. Very soon software users will expect interaction with business software to have the same engaging and entertainment quality as TV programs and computer based games. To achieve this requires teams of designers and developers representing fine art, communications, drama, theater, business, computer science, and others. IAADS has brings together leading experts in design, art, communications, social sciences and multimedia technology to team up with experts in computer science and quantitative analysis to design and build engaging and powerful new interfaces. The attention to understandable and useful quantitative tools is also being renewed. The accelerating movement is towards new tools for interactive visual simulation and business gaming are creating a “new breed” of quantitative
  2. 2. tools. Obviously, the more powerful and valuable the data resources they draw on and the more engaging and interactive the interfaces they lie behind, the greater their contribution to decision makers and decision processes. IAADS is about the next generation of DSS tools and designers for “rehearsing the future”. IAADS has initiated the rejuvenation of the original spirit of DSS with today’s technologies, methods, tools, processes, skills and needs. Characteristics and Capabilities of DSS Because there is no exact definition of DSS, there is obviously no agreement on the standard characteristics and capabilities of DSS. Turban, E., Aronson, J.E., and Liang, T.P. constitute an ideal set of characteristics and capabilities of DSS. The key DSS characteristics and capabilities are as follows: 1. Support for decision makers in semi structured and unstructured problems. 2. Support managers at all levels. 3. Support individuals and groups. 4. Support for interdependent or sequential decisions. 5. Support intelligence, design, choice, and implementation. 6. Support variety of decision processes and styles. 7. DSS should be adaptable and flexible. 8. DSS should be interactive and provide ease of use. 9. Effectiveness balanced with efficiency (benefit must exceed cost). 10.Complete control by decision-makers. 11.Ease of development by (modification to suit needs and changing environment) end users. 12.Support modeling and analysis. 13.Data access. 14.Standalone, integration and Web-based.
  3. 3. Success and failure of decision support systems: Learning as we go Much effort and money have been devoted to the development of decision support systems (DSS) to enhance the decision-making capabilities of primary producers and their advisers. These initiatives have been partly driven by the increased complexity of primary production brought on by market globalization, the need for sustainable production practices, and the increasing rate and volume of information exchange. Decision support systems can assist producers in making better decisions by integrating information into a more useable form, altering production systems, enhancing management skills, and reducing costs of production. Despite these benefits, the adoption of DSS by producers has been limited. Reasons include the lack of end user evaluation preceding and during DSS development, as well as the fact that the DSS output may not fit the producer's decision-making style or because the complexity needed to operate the DSS is great and requires considerable data input. Information systems-development methodologies are investigated as a way of increasing DSS adoption. These methodologies, including quot;hardquot; and quot;softquot; systems approaches, are discussed as ways to provide greater concordance between developer and end user. A case study of quot;Hot Cross,quot; a DSS under development to evaluate crossbreeding systems in northern Australia, was used to identify issues involved in DSS development and use. Issues highlighted include industry consultation, target audience focus, evaluation of DSS success, user
  4. 4. participation, support and availability, and participatory learning processes. The case study provided evidence of a perceptible shift in the development process because greater emphasis was put on the learning process of breeding program design by end-users rather than emphasis on learning how to use the DSS itself. Greater end user involvement through participatory learning approaches (action learning, action research, and soft systems methodologies), iterative prototyping (evolving development processes), as well as keeping DSS development manageable and small in scope, will provide avenues for improving the rate of DSS adoption.

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