© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-1 Chapter 3 Decision Support Systems: An Overview Decision Support Systems

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-2 Outline 1.Decision Support Systems. 2.Characteristics and capabilities of DSS. 3. DSS components. 4. Data Management Subsystem. 5. Model Management Subsystem. 6. User Interface system 7. Knowledge-based Management System 8. DSS Hardware. 9. DSS classification.

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Decision Support Systems Systems designed to support managerial decision-making in unstructured problems More recently, emphasis has shifted to inputs from outputs Mechanism for interaction between user and components Usually built to support solution or evaluate opportunities

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-4 DSS A DSS is a methodology that supports decision-making. It is: –Flexible; –Adaptive; –Interactive; –GUI-based; –Iterative; and –Employs modeling.

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Characteristics and capabilities of DSS Because there is no consensus on exactly what a DSS is, there is no agreement on standard characteristics and capabilities of DSS. The term “business intelligence” is synonymous with DSS The list in the following figure is an ideal set.

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-6

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-7 Business Intelligence Proactive BI focusses on accelerating decision-making Increases information flows Five components of proactive BI: –Real-time warehousing –Exception and anomaly detection –Proactive alerting with automatic recipient determination –Seamless follow-through workflow –Automatic learning and refinement

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Components of DSS Subsystems: –Data management Managed by DBMS –Model management Managed by MBMS –User interface –Knowledge Management and organizational knowledge base

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-9

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Data Management Subsystem Components: –Database –Database management system –Data directory –Query facility

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-11

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-12 Database A database is a collection of interrelated data extracted from various sources, stored for use by the organization, and queried. The data from the DSS database are extracted from: –Internal data, usually from TPS –External data from government agencies, trade associations, market research firms, forecasting firms –Private data or guidelines used by decision- makers

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-13 Database Management System Extracts data Manages data and their relationships Updates (add, delete, edit, change) Retrieves data (accesses it) Queries and manipulates data Employs data dictionary

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-14 Data Directory Catalog of all data –Contains data definitions –Answers questions about the availability of data items –Source –Meaning –Allows for additions, removals, and alterations

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Model Management Subsystem Model management subsystem of a DSS consists of the components: –Model base –Model base management system –Modeling language –Model directory –Model execution, integration, and command processor

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-16 The structure of the model management subsystem

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-17 Category of Models Strategic –Supports top management decisions Tactical –Used primarily by middle management to allocate resources Operational –Supports daily activities Analytical –Used to perform analysis of data

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-18 Model Base Management System Functions: –Model creation –Model updates –Model data manipulation –Generation of new routines Model directory: –Catalog of models –Definitions

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-19 Model Management Activities Model execution –Controls running of model Model command processor –Receives model instructions from user interface –Routes instructions to MBMS or module execution or integration functions Model integration –Combines the operations of several models

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang User Interface System Knowledge-based system Data management and DBMS Model management and MBMS User Interface Management System (UIMS) Natural Language Processor Input Action Languages Output Display Language UsersPrinters, Plotters PC Display Based on Figure 3.6, Schematic View of the User Interface

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-21 User Interface Management System GUI Natural language processor Interacts with model management and data management subsystems Examples –Speech recognition –Display panel –Tactile interfaces –Gesture interface

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang Knowledge-Base Management System Many unstructured/semistructured problems need expertise (knowledge) for their solutions. Such expertise can be provided by some knowledge engineers who interview the domain experts and gather the information necessary for the knowledge- base. More advanced DSSs are equipped with a component called knowledge base management subsystem. This subsystem can achieve complex problem solving and it can enhance operations of other components. The knowledge base is where the “knowledge” of the DSS is stored. By knowledge, we mean the rules, heuristics, constraints, previous outcomes and any other “knowledge” that may have been programmed into the DSS.

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-23 Knowledge retrieval Once we have the knowledge stored in the knowledge base, we need a method of getting them out in an organized fashion. The inference engine (IE) is that part of the knowledge base that facilitates this process. The IE is the module that activates all the domain knowledge that has been gathered and performs inferencing (reasoning) to work toward a solution or conclusion.

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang DSS Hardware De facto standard Web server with DBMS: –Operates using browser –Data stored in variety of databases –Can be mainframe, server, workstation, or PC –Any network type –Access for mobile devices

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang DSS Classifications Alter –Extent to which outputs can directly support or determine the decision –Data-oriented or model-oriented Holsapple and Whinston –Text-oriented, database-oriented, spreadsheet- oriented, solver-oriented, rule-oriented, or compound Intelligent DSS –Knowledge-based DSS, rule-oriented DSS

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-26 Notes on Solver-oriented DSS A solver is an algorithm or procedure written as a program for performing certain computation for solving a particular problem type. EX: linear regression routine, linear programming routine. Solver can be written in a programming languages such as C++, Java. DSS builder can incorporate the solver in creating the DSS application.

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-27 (ad hoc analysis)

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-28 Other DSS Classifications Donovan and Madnick –Institutional (Problems of recurring nature) –Ad hoc (Problems that are not anticipated or are not repetitive) Hackathorn and Keen –Personal support, –Group support, or organizational support

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-29 DSS Classifications Group SS v. Individual DSS –Decisions made by entire group or by lone decision maker Custom-made v. vendor-ready-made –Generic DSS may be modified for use Database, models, interface, support are built in Addresses repeatable industry problems Reduces costs

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-30 Web and DSS The web can be used for data collection The Web can be used for communications and collaborations The Web can be used to download DSS software. Database vendors provide Web capabilities by running directly on Web servers Simplifies integration problems Increased usability features

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 3-31