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Decision Support Systems

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Presentation on theme: "Decision Support Systems"— Presentation transcript:

1 Decision Support Systems
Chapter 3: Decision Support Systems Concepts, Methodologies and Technologies: An Overview

2 Learning Objectives Understand possible decision support systems(DSS) configurations. Understand the key differences and similarities between DSS and BI systems. Describe DSS characteristics & capabilities. Understand the essential definitions of DSS. Understand DSS components and how they integrate. Describe the components and structure of each DSS component: the data management subsystem, the model management subsystem, the user interface (dialog) subsystem, the knowledge-based management system and the user.

3 Learning Objectives Explain internet impact on DSS and vice versa.
Explain the unique role of the user in DSS versus management information systems (MIS). Describe DSS hardware and software platforms. Understand important DSS classifications. Become familiar with some DSS application areas and applications. Understand important current DSS issues.

4 DSS Configurations Depends on the management-decision situation and the specific technologies used for support. Technologies are typically deployed over the web and are assembled from: Data Models User Interface Knowledge (optional) Components are emphasized by the support provided (i.e. Model-Oriented DSS -> Model (spreadsheets), Data-Oriented DSS -> Database).

5 DSS Description BI DSS Monitor Situations.
Identify problems and/or opportunities using analytical methods. User must identify wither a particular situation warrants attention and then analytical methods can be applied. Support the solution of a certain problem. Evaluate an opportunity. Utilized models and data access. Arguably considers DSS part of its internal building blocks. Utilizes models and data access, but they have their own databases that are used to solve a specific problem or set of problems (DSS Applications) Focuses on reporting and identification of problems by scanning data extracted from a data warehouses. Built to solve a specific problem and include their own databases

6 Some DSS Definitions Systems designed to support managerial decision-making in unstructured problems. Little (1970): Model based set of procedures for processing data and judgements to assist manager in his decision making.. Must be simple, robust, easy adaptive, complete Moore and Chang (1980): Structured problems are structured, because we treat them in that way.. DSS is an expandable system capable of supporting ad hoc data analysis and decision modeling for planning the future Bonczek (1980): A computerbased system with 3 interacting components, a language system, a knowledge system, problem processing system Keen (1980): Final system can be developed by the adaptive process of learning and evolution by the user, the DSS builder, and the DSS itself

7 Generic DSS Description
DSS is an approach (or methodology) for supporting decision making. Uses Interactive, Flexible, Adaptive CBIS developed for supporting the solution to a specific nonstructured management problem, it uses data, provides an easy user interface, and can incorporate the decision maker own insight. Includes models and is developed (possibly by end users) through an interactive and iterative process. Supports all phases of decision making and may include knowledge component. Can be used by a single user on a PC or can be Web based for use by many people in several locations.

8 DSS characteristics and capabilities
There is no consensus on exactly what a DSS is, and there is obviously no agreement on the standard characteristics and capabilities of DSS. Terms Business Analytics (BA): implies the use of models and data to improve the organization’s performance or competitive posture. The focus is the use of models, even if they are deeply buried inside the system. Data mining and OLAP systems have models embedded in them but are still not well understood in practice. Web analytics: is an approach to using analytics tools on real-time Web information to assist in decision making . Predictive analytics: describes the business analytics methods of forecasting problems and opportunities rather that simply reporting them as they occur. It utilized advanced forecasting and simulation models.


10 Component of DSS DSS application can be composed of
Data Management subsystem. Model Management subsystem. User Interface subsystem. Knowledge Management subsystem.


Includes database that contains relevant data for the situation and is managed by DBMS. Can be interconnected with the corporate data warehouse [A repository for corporate relevant decision-making data], usually, the data are stored or accessed via database Web server. MODEL MANAGEMENT SUBSYSTEM [MBMS]: Software package that includes financial, statistical, management science, or other quantitative models that provide the system’s analytical capabilities and appropriate software management. Modeling languages for building custom models are included. Often called Model Base Management System [MBMS]. Can be connected to corporate or external storage of models. Model solution methods and management systems are implemented in Web development systems (such as Java) to run on application servers.

User communicated with and commands the DSS through the user interface subsystem. User is considered part of the system. Researchers assert that some of the unique contributions of DSS are derived from the intensive interaction between the computer and the decision maker. Web browser provides a familiar, consistent graphical user interface (GUI) for most DSS. THE KNOWLEDGE-BASE MANAGEMENT SUBSYSTEM Can support any of the other subsystems or act as an independent component. It provides intelligence to augment the decision maker’s own. It can be interconnected with the organization's knowledge repository (part of a Knowledge management system [KMS] (The Organizational Knowledge Base) Knowledge may be provided via Web servers. By definition DSS must include the three major components DBMS, MBMS and user interface, the KBMS is optional but it can provide many benefits by providing intelligence in and to the three major components. The user may be considered a component of a DSS.

14 How DSS Component integrate
Can be connected to a corporate intranet, extranet or the internet. Component communicate through web technologies. Web browsers are excellent choice for UI.

15 A Web Based DSS Architecture
Web Browser Web Server Application Optimization/ Simulation, etc. Data Warehouse or DBMS Read about DSS & the Web mutual impact

The Data management subsystem is composed of the following elements: 1- DSS database 2- DBMS 3- Data Directory 4- Query Facility


Interrelated data extracted from various sources, stored for use by the organization, and queried. 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.

19 DATA MANAGEMENT SUBSYSTEM: Database Management System
Data Organization Should DSS have their own Databases. Data Extraction ETL The process of capturing data from several sources & the integration process.

20 Data Management Subsystem Query Facility
Access, manipulate and query data Accepts requests for data Consults the data directory Formulates the direct requests Reports the results (on a web structured page) Ex: Search for all sales in the Southeast region during June 2006 and summarize sales by salesperson.

21 Data Management Subsystem 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

22 Key Database Management System Issues
Data Quality: [GIGO]. Data Integration: Single version of the truth. Scalability. Data Security.

23 Model Management Subsystem
Components: Model base Model base management system Modeling language Model directory Model execution, integration, and command processor

24 Model Management Subsystem
Models (Model Base) Strategic, tactical operational Statistical, financial, marketing, Management science, Accounting engineering Model building blocks Model Base Management Modeling commands: creation Maintenance: update Database interface Modeling language Data Management Interface Knowledge based subsystem Model Directory Model execution, integration and command processor

25 Model Management Subsystem Models in the Model Base
Clasification with respect to time span Strategic models: Supports top management decisions Tactical models: Used primarily by middle management to allocate resources Operational models: Supports daily activities Analytical models Used to perform analysis of data for strategic, tactical and operational decisions Also there are model building blocks and routines, like Random number generation, curve fitting, present value computation

26 Model Management Subsystem Model Management Activities
Model execution Controls running of model Model integration Combines several models’ operations Model command processor Receives model instructions from user interface Routes instructions to MBMS or model execution or integration functions

27 Model Management Subsystem Model Base Management System
Functions: Model creation Model updates Model data manipulation Generation of new routines

28 Model Management Subsystem Model Directory
Catalog of models and software Definitions Functions to answer questions about availability and capability of the models

29 User Interface Management System
Interacts with model, data and knowledge management subsystems Includes a natural language processor or standard objects (pull down menus, internet browsers) Includes GUI, frequently by web browsers Accomodates the user with a variety of input devices Provides output with a various formats and output devices. Provides help capabilities

30 User Interface Management System
Stores data Process multiple functions concurrently Support cummunication b/w users and tech. Staff Provides training Provides flexibility and adaptiveness Captures, stores and analyzes the dialog usage

31 User Interface System Data management and DBMS Knowledge-based system
Model management and MBMS User Interface Management System (UIMS) Natural Language Processor Input Action Languages Output Display Language PC Display Printers, Plotters Users Based on Figure 3.6, Schematic View of the User Interface

32 New User Interface Developments
Voice/speech recognition (Ex: Clarissa developed at NASA Ames Research Team) Handwriting recognition Translation of text into voice Automatic real time natural language speech translator (on process) Displays are getting better by crisp images, holographic displays (Ex: LCD panels developed at Philips Research)

33 New User Interface Developments
Tactile interfaces (Ex: Immersion Corp.’s Cyberforce Sys. includes a spandex glove that sense the doctors get when performing surgery) Videoconferencing (Mİcrososft developed RingCam, an omnidirectional videocamera to view the entire room Gesture interface that utilizes holographic displays Gesture

34 New Developments in DSS
Access data from a data warehouse, use models from OLAP or data mining tools. Web technologies Link components for accessing data and knowledge via web browsers or web like user interfaces Enable virtual teams to collaborate Reduced technological barriers; made transactions easier and less costly by mobile communication Hardware shrinks in size, increases in speed etc. Faster, intelligent search engines by AI techniques In the future some DSS may include emotions, mood, tacit values and other soft factors tacit values

35 Knowledge-Based Management System
Expert or intelligent agent system component to enhance the operation of other DSS components Complex problem solving in unstructured/semistructured systems Gives aid in models selection and construction Enhances operations of other components A DSS that includes this optional component is called an intelligent DSS, DSS/ES, expert support system or knowledge-based DSS Caution: A KMS is a text oriented DSS; not a Knowledge-based Management System

36 DSS Hardware Hardware affects the functionality and usability of DSS, De facto standard Major hardware options: mainframe, server, workstation, PC, client/server system Distributed DSS runs on different networks including internet, intranet, extranet Access by client pc’s or by mobile devices notebook pc’s, PDA’s, cell phones

37 DSS Hardware Models run either on the server, mainframe, any exernal system or client pc 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

38 DSS Classifications Association for Information Systems Special Interest Group In DSS [AIS SIGDSS] Communications-driven and group DSS Data-driven DSS Document-driven DSS, data mining, and management ES applications Model-driven DSS Holsapple and Whinston Text oriented, database oriented, spreadsheet oriented, solver oriented, rule oriented, or compound

39 DSS Classifications Alter
Extent to which outputs can directly support or determine the decision Data oriented or model oriented


41 DSS Classifications Donovan and Madnick Ad hoc Hackathorn and Keen
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

42 DSS Classifications GSS v. Individual DSS
Decisions made by entire group or by one 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

43 Web and DSS Data collection Communications Collaborations
Download capabilities Run on Web servers Simplifies integration problems Increased usability features


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