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.

12. Component of DSS DATA 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.

13. Component of DSS THE USER INTERFACE SUBSYSTEM
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

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

18. DATA MANAGEMENT SUBSYSTEM: DATABASE
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