This document outlines the topics to be covered in a lesson on decision support systems (DSS). It will define DSS, describe their characteristics and components, explain different types of decisions and levels in organizations, and approaches to decision making and problem solving. Specifically, it will cover learning objectives, an overview of DSS, the difference between data-driven and model-driven DSS, and how DSS can be used to support structured and unstructured decision making at operational, tactical and strategic levels.

2. Followings are the topic out line of DSS
 Learning Objectives
 An Overview of DSS
 Characteristics and capabilities of DSS
 Components of a DSS
 Decision Types and Levels in organization
 Decision Making and Problem Solving
Approaches
 Data-driven versus Model-driven DSS
 Summary

3. Students will be able to define DSS in business
context
Students will be able to understand the various
types of decision taken at various level in a
typical business organization
Students will be able to use DSS in taking
decision by applying the knowledge acquired as
per lower levels of RBT
Define the stages of decision making.
Discuss the importance of implementation and
monitoring in problem solving.

4. Decisions support systems includes _
 People and Procedures
 Software, Databases, and devices that are used in
problem-specific decision-making and problem-
solving.
 Particularly useful when dealing with semi-
structured, poorly structured, or unstructured
situations.
 Although decision support systems are used most
often at higher levels of management, all employees
may use them to assist in even programmable
decisions.

5. Following are the important characteristics viz.
 Management level
 Computer system combines database and
modelbase,
 User - friendly software for semi-structured &
unstructured decision making
 Offer potential to assist in solving both semi-
structured and unstructured problems

6.  Perform complex, sophisticated analysis
Simulation - allows a user to model a problem by
duplicating features of a real system. This generally
involves some uncertainty or probability.
What-if analysis - even simple spreadsheets support
“what-if analysis”, which allows the user to make changes
to input variables to see the result on outcomes.
Goal-seeking analysis – Allows the user to set the goal
and input can be defined by DSS.

7.  Handles large amount of data from various sources
 Provide report and presentation flexibility
 Offer both textual and graphical orientation
 Support drill down analysis

8.  Support all problem-solving phases
 Support different decision frequencies
 Support different problem structures
 Support various decision-making levels

10. Intelligence
Design
Choice
Implementation
Monitoring
Solution Types
Decisions
 Optimized
 Satisfying
 Heuristic
Multiple
Yes
No
No Choice – No Decision
F
E
E
D
B
A
C
K

11. Operational Tactical Strategic
Un-
structured
Cash
Management
Re-engineering a
process
New e-business initiatives
Company re-organization
Semi-
structured
Production
Scheduling
Employee Performance
Evaluation, Capital
Budgeting
Mergers, Site Location
Structured Payroll

12. Strategic
Management
Tactical Management
Operational Management
Decision Structure Information
Characteristics
Unstructured
Semi-structured
Structured
Ad Hoc
Unscheduled
Summarized
Infrequent
Forward Looking
External
Wide Scope
Pre-specified
Scheduled
Detailed
Frequent
Historical
Internal
Narrow Focus
Organization Levels

13.  Programmed decisions
 Structured situations with well defined relationships
 Quantifiable
 Management information system
 Easy to computerize
 Non-programmed decisions
› Rules and relationships not defined
› Problem is not routine
› Not easily quantifiable

14.  Optimization: find the optimum solution
 Satisfying: find a good solution
 Heuristics: rules of thumb

15.  Data-driven DSS - primarily performs qualitative
analysis based on the company’s databases
 Model-driven DSS - primarily performs
mathematical or quantitative analysis

16.  An organized collection of people, procedures,
software, databases, and devices working to support
managerial decision making
 Specialized decision support systems designed to
meet the needs of senior management