The document discusses decision support systems (DSS), including their architecture, characteristics, capabilities, classification, and the steps in designing a DSS. It describes the key components of a DSS architecture as including data management, user interface, model management, and knowledge management. It also classifies DSS as data-driven, model-driven, knowledge-driven, document-driven, communication-driven, and inter/intra-organizational. The design process involves planning, analysis, design, and implementation phases.

1. DECISION SUPPORT
SYSTEM
C A Yogaraja,
Assistant Profesor, CSE
K L University.

2. Introduction
■ Decision support systems (DSS) are interactive software-based systems
intended to help managers in decision-making by accessing large
volumes of information generated from various related information
systems involved in organizational business processes
■ DSS uses the summary information, exceptions, patterns, and trends
using the analytical models
■ A decision support system helps in decision-making but does not
necessarily give a decision itself
■ The decision makers compile useful information from raw data,
documents, personal knowledge, and/or business models to identify and
solve problems and make decisions

3. DSS Architecture

4. DSS Architecture
Architecture of DSS has four components
– Data Management
– User Interface
– Model Management
– Knowledge Management

5. DSS Architecture
Data Management
– Data management subsystem includes a database contains relevant data for the
situation and is managed by the software called DBMS.
– The data management subsystem may be interconnected with corporate data
warehouse, a repository for corporate relevant decision making data.
– The data management subsystem is composed of
■ DSS Database
■ DBMS
■ Data Directory
■ Query facility

6. DSS Architecture
Model Management
– Includes software with financial, statistical, management science, or other quantitative
models that provide the system’s analytical capabilities and appropriate software
management
– Modelling languages for building custom models are also included
– Often called as Model Base Management System (MBMS)
– The model management subsystem includes
■ Model base
■ MBMS
■ Modelling language
■ Model Directory
■ Model execution, integration and command Processor

7. DSS Architecture
User Interface
– Enables the users to communicate with and command the DSS
Knowledge Management
– Provides knowledge for solution of the problem; supports any of the
other subsystems or act as an independent component

8. Scope of DSS
■ Enterprise wide DSS
– Linked to large data warehouse and serves many managers in the
company.
■ Desktop DSS
– Single use DSS is a small system that runs on a individual manager’s
PC.

9. Characteristics
Rapid Access to Information
■ Some DSS provide fast and Continuous access to information
– Gauges on the dashboard of a vehicle
Handle large amount of Data from multiple sources
■ Advanced DBMS and Data warehouses have allowed decision
makers to search for information with a DSS even when some data
resides in different databases on different computer systems or
networks.
Provide Report and Presentation flexibility
■ Managers can get the information as they want, presented in a
format that suits their needs, even output can be displayed on
computer screens or printed depending on their needs.

10. Characteristics
Offer both textual and graphical orientation
■ DSS’s can produce Text, tables, charts and more to get a better
understanding of results
Support drill down analysis
■ A manager can get more level of detail when they needed by
drilling down the data
Perform complex, Sophisticated Analysis and Comparisons
using advanced software Packages
■ Marketing research surveys

11. Capabilities of DSS
■ Using Decision Support System four basic types of analytical modeling
activities are involved
– What-if analysis: An end user makes the change to variables or
relationship among variables, and observe the resulting changes in the
values.
■ Example: what if we cut advertising by 10%? What would happen to sales?
– Goal Oriented: Process of determining the input values required to
achieve a certain goal
■ Example: House buyers determine the monthly payment they can afford and
calculate the number of such payments required to pay the desired house.

12. Capabilities of DSS
– Risk Analysis: It is the important factor that affects the business
enterprise. DSS allows the managers to assess the risks associated with
various alternatives. Decisions can be then classified as low risk,
medium risk, high risk.
– Model Building: DSS allows decision makers to identify the most
appropriate model for solving the problems. It takes into account input
variables, inter relationship among the variables and constraints.
■ Example: A marketing manager of a TV manufacturing company is charged with
the responsibility of developing a sales forecasting model for color Televisions
– Graphical Analysis: This helps the managers to quickly digest the large
volumes of data and visualize the impacts of various course of action.

13. Components of a Decision Support
System
Like any other software system, DSS also has components and phases of
development. No matter what kind of decision support system you’re looking
to develop, you must plan around these four components:
■ Input: What kind of input does it require to carry out the analysis? As
mentioned earlier, it can be rule, problem, spreadsheet, text or database
oriented.
■ User Knowledge/Expertise: Whether inputs will require manual analysis by
the user or not
■ Output: Should the outcomes be comparative or generic?
■ Decisions: Whether it should be a suggestion support system? Or you just
want it to analyze the data and outcome of different actions?

14. Classification of DSS
Data driven DSS
– These DSS has file drawer systems, data analysis systems, analysis
information systems, data warehousing and emphasizes access to and
manipulation of large databases of structured data
Model driven
– 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
– These systems usually are not data intensive and consequently are not
linked to very large databases.

15. Classification of DSS
Knowledge driven
– These systems provide recommendation and/or suggestion schemes which
aids the user in selecting an appropriate alternative to a problem at hand.
– provides specialized problem-solving expertise stored as facts, rules,
procedures, or in similar structures
– Knowledge driven DSS are often referred to as management expert systems
or intelligent decision support systems.

16. Classification of DSS
Document Driven
– These systems help managers retrieve and manage unstructured documents
and web pages by integrating a variety of storage and processing
technologies to provide complete document retrieval and analysis.
– It also access documents such as company policies and procedures, product
specification, catalogs, corporate historical documents, minutes of meetings,
important correspondence, corporate records, etc. and are usually driven by
a task-specific search engine.

17. Classification of DSS
Communication driven
– This breed of DSS is often called group decision support systems
(GDSS).
– They are a special type of hybrid DSS that emphasizes the use of
communications and decision models intended to facilitate the
solution of problems by decision makers working together as a group.
– GDSS supports electronic communication, scheduling, document
sharing and other group productivity and decision enhancing activities
and involves technologies such as two-way interactive video, bulletin
boards, e-mail, etc.

18. Classification of DSS
Inter and Intra Organization DSS
– These systems are driven by the rapid growth of Internet and other
networking technologies such as broadband WAN’s, LAN’s, WIP, etc.
– Inter-organization DSS are used to serve companies stakeholders
(customers, suppliers, etc.), whereas intra-organization DSS are
more directed towards individuals inside the company and specific
user groups.

19. Classification of DSS
New breeds of DSS
– Hybrid Systems, which are combinations units using aspects of
more than one different type of DSS.
– A very popular example is Web based DSS, which can be driven
by a combination of different models such as document-driven,
communication driven and knowledge drive.
– Web-based DSS are computerized systems that delivers decision
support information or decision support tools to a manager or
business analyst using a "thin-client" Web browser like Netscape
Navigator or Internet Explorer.

20. Classification of DSS
New breeds of DSS
– On-line Analytical Processing (OLAP) - it performs
multidimensional analysis of business data and provides the
capability for complex calculations, trend analysis, and
sophisticated data modeling.
– It is the foundation for many kinds of business applications for
Business Performance Management, Planning, Budgeting,
Forecasting, Financial Reporting, Analysis, Simulation Models,
Knowledge Discovery, and Data Warehouse Reporting.

21. Steps in Designing DSS

22. Planning-Why Build the System?

23. Analysis-Who, What, When, Where?

24. Design- How will the system work?

25. Implementation- System Delivery?