Decision Support System Part II

Decision Support System Part II
Characteristics and Capabilities of DSSBecause 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:Support for decision makers in semistructured and unstructured problems.Support managers at all levels.Support individuals and groups.Support for interdependent or sequential decisions.Support intelligence, design, choice, and implementation.Support variety of decision processes and styles.DSS should be adaptable and flexible.DSS should be interactive and provide ease of use.Effectiveness balanced with efficiency (benefit must exceed cost).Complete control by decision-makers.Ease of development by (modification to suit needs and changing environment) end users.Support modeling and analysis.Data access.Standalone, integration and Web-based.TaxonomiesAs with the definition, there is no universally accepted taxonomy of DSS either. Different authors propose different classifications. Using the relationship with the user as the criterion, Haettenschwiler differentiates passive, active, and cooperative DSS. A passive DSS is a system that aids the process of decision making, but that cannot bring out explicit decision suggestions or solutions. An active DSS can bring out such decision suggestions or solutions. A cooperative DSS allows the decision maker (or its advisor) to modify, complete, or refine the decision suggestions provided by the system, before sending them back to the system for validation. The system again improves, completes, and refines the suggestions of the decision maker and sends them back to her for validation. The whole process then starts again, until a consolidated solution is generated.Using the mode of assistance as the criterion, Power differentiates communication-driven DSS, data-driven DSS, document-driven DSS, knowledge-driven DSS, and model-driven DSS.A model-driven DSS emphasizes access to and manipulation of a statistical, financial, optimization, or simulation model. Model-driven DSS use data and parameters provided by users to assist decision makers in analyzing a situation; they are not necessarily data intensive. Dicodess is an example of an open source model-driven DSS generator.A communication-driven DSS supports more than one person working on a shared task; examples include integrated tools like Microsoft's NetMeeting or GrooveA data-driven DSS or data-oriented DSS emphasizes access to and manipulation of a time series of internal company data and, sometimes, external data. A document-driven DSS manages, retrieves and manipulates unstructured information in a variety of electronic formats.A knowledge-driven DSS provides specialized problem solving expertise stored as facts, rules, procedures, or in similar structures.Using scope as the criterion, Power differentiates enterprise-wide DSS and desktop DSS. An enterprise-wide DSS is linked to large data warehouses and serves many managers in the company. A desktop, single-user DSS is a small system that runs on an individual manager's PC.ArchitecturesThis article may require cleanup to meet Wikipedia's quality standards.Please improve this article if you can. (December 2007)Once again, different authors identify different components in a DSS. For example, Sprague and Carlson identify three fundamental components of DSS: (a) the database management system (DBMS), (b) the model-base management system (MBMS), (c) the dialog generation and management system (DGMS).describe these three components in more detail:The Data Management Component stores information (which can be further subdivided into that derived from an organization's traditional data repositories, from external sources such as the Internet, or from the personal insights and experiences of individual users); the Model Management Component handles representations of events, facts, or situations (using various kinds of models, two examples being optimization models and goal-seeking models); and the User Interface Management Component is, of course, the component that allows a user to interact with the system.According to Power, academics and practitioners have discussed building DSS in terms of four major components: (a) the user interface, (b) the database, (c) the model and analytical tools, and (d) the DSS architecture and network. identifies five components of DSS:(a) users with different roles or functions in the decision making process (decision maker, advisors, domain experts, system experts, data collectors),(b) a specific and definable decision context,(c) a target system describing the majority of the preferences,(d) a knowledge base made of external data sources, knowledge databases, working databases, data warehouses and meta-databases, mathematical models and methods, procedures, inference and search engines, administrative programs, and reporting systems, and(e) a working environment for the preparation, analysis, and documentation of decision alternatives.proposes a generalized architecture made of five distinct parts:(a) the data management system,(b) the model management system,(c) the knowledge engine,(d) the user interface, and(e) the user(s).Development FrameworksDSS systems are not entirely different from other systems and require a structured approach. A framework was provided by Sprague and Watson (1993). The framework has three main levels1. Technology levels 2. People involved 3. The developmental approachTechnology LevelsSprague has suggested that there are three levels of hardware and software that has been proposed for DSS.a) Level 1 – Specific DSSThis is the actual application that will be used to by the user. This is the part of the application that allows the decision maker to make decisions in a particular problem area. The user can act upon that particular problem.b) Level 2 – DSS GeneratorThis level contains Hardware/software environment that allows people to easily develop specific DSS applications.This level makes use of case tools or systems like Crystalc) Level 3 – DSS ToolsContains lower level hardware/software. DSS generators including special languages, function libraries and linking modulesPeople InvolvedSprague suggests there are 5 roles involved in a typical DSS development cycle.a) The end user.b) An intermediary.c) DSS developerd) Technical supportere) Systems ExpertDevelopmentalThe developmental approach for a DSS system should be strongly iterative. This will allow for the application to be changed and redesigned at various intervals. The initial problem is used to design the system on and then tested and revised to ensure the desired outcome is achieved.Classifying DSSThere are several ways to classify DSS applications. Not every DSS fits neatly into one category, but a mix of two or more architecture in one.Holsapple and Whinston classify DSS into the following six frameworks: Text-oriented DSS, Database-oriented DSS, Spreadsheet-oriented DSS, Solver-oriented DSS, Rule-oriented DSS, and Compound DSS.A compound DSS is the most popular classification for a DSS. It is a hybrid system that includes two or more of the five basic structures described by Holsapple and Whinston.The support given by DSS can be separated into three distinct, interrelated categories : Personal Support, Group Support, and Organizational Support.Additionally, the build up of a DSS is also classified into a few characteristics1) inputs: this is used so the DSS can have factors, numbers, and characteristics to analyze.2) user knowledge and expertise: This allows the system to decide how much it is relied on, and exactly what inputs must be analyzed with or without the user.3) outputs: This is used so the user of the system can analyze the decisions that may be made and then potentially4) make a decision: This decision making is made by the DSS, however, it is ultimately made by the user in order to decide on which criteria it should use.DSSs which perform selected cognitive decision-making functions and are based on artificial intelligence or intelligent agents technologies are called Intelligent Decision Support Systems (IDSS).Applications DSSAs mentioned above, there are theoretical possibilities of building such systems in any knowledge domain.Some of the examples is Clinical decision support system for medical diagnosis. Other examples include a bank loan officer verifying the credit of a loan applicant or an engineering firm that has bids on several projects and wants to know if they can be competitive with their costs.DSS is extensively used in business and management. Executive dashboard and other business performance software allow faster decision making, identification of negative trends, and better allocation of business resources.A growing area of DSS application, concepts, principles, and techniques is in agricultural production, marketing for sustainable development. For example, the DSSAT4 package, developed through financial support of USAID during the 80's and 90's, has allowed rapid assessment of several agricultural production systems around the world to facilitate decision-making at the farm and policy levels. There are, however, many constraints to the successful adoption on DSS in agriculture.A specific example concerns the Canadian National Railway system, which tests its equipment on a regular basis using a decision support system. A problem faced by any railroad is worn-out or defective rails, which can result in hundreds of derailments per year. Under a DSS, CN managed to decrease the incidence of derailments at the same time other companies were experiencing an increase.DSS has many applications that have already been spoken about. However, it can be used in any field where organization is necessary. Additionally, a DSS can be designed to help make decisions on the stock market, or deciding which area or segment to market a product toward.Benefits of DSSImproving Personal EfficiencyExpediting Problem SolvingFacilitating Interpersonal CommunicationPromoting Learning or TrainingIncreasing Organizational Control