Management Information System Lecture 3 MBA

Lec 4 of AAST MIS lectueres for MBA

1. 4-1
Decision Support Systems:
An Overview
Chapter 4

2. 4-2
Learning Objectives
•Learn the basic concepts of decision
making.
•Learn four phases of decision making.
•Learn characteristics and capabilities of
DSS.
•Understand DSS components.
•Describe structure of DSS components.
•Learn the role of the user in DSS.
•Understand DSS hardware and integration.

3. Decision Making
•Process of choosing amongst
alternative courses of action for the
purpose of attaining a goal or goals.
•The four phases of the decision
process are:
–Intelligence
–Design
–Choice
–implementation
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4. Decision Support System
•A Decision Support System (DSS) is an
interactive computer-based system or
subsystem intended to help decision makers
use communications technologies, data,
documents, knowledge and/or models to
identify and solve problems, complete decision
process tasks, and make decisions.
•DSS are interactive computer-based systems,
which help decision makers utilize data and
models to solve unstructured problems
•Decision Support System is a general term for
any computer application that enhances a
person or group’s ability to make decisions.

5. The Role of the Decision Maker
•Decision makers can be
–Individuals
–Teams
–Groups
–Organizations
•All of these types of decision makers will differ in their knowledge and
experience; therefore, there will be differences in how they will react to a
given problem scenario

6. Spreadsheet-based decision
support systems
•A DSS is made up of a model (or models), a
source of data, and a user interface.
•When a model is implemented in Excel, it is
possible to use Visual Basic for Applications
(VBA) to make the system more efficient by
automating interactive tasks that users would
otherwise have to repeat routinely.
•VBA can also make the system more powerful
by extending the functionality of a spreadsheet
model and by customizing its use.

7. Why DSS?
•Increasing complexity of decisions
–Technology
–Information:
• “Data, data everywhere, and not the time to think!”
–Number and complexity of options
–Pace of change
•Increasing availability of computerized support
–Inexpensive high-powered computing
–Better software
–More efficient software development process
•Increasing usability of computers

8. Perceived benefits
–decision quality
–improved communication
–cost reduction
–increased productivity
–time savings
–improved customer and employee
satisfaction

9. Types for DSS
•Using the mode of assistance as the
criterion, there are four types for DSS:
–communication-driven DSS,
–data-driven DSS,
–document-driven DSS,
–model-driven DSS.

10. Model-driven DSS
•A model-driven DSS 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; they are not necessarily data
intensive. Dicodess is an example of an open source
model-driven DSS generator (Gachet 2004).
•Other examples:
–A spread-sheet with formulas in
–A statistical forecasting model

11. Data-driven (retrieving) DSS
•A data-driven DSS or data-oriented DSS emphasizes
access to and manipulation of a time series of internal
company data and, sometimes, external data.
•Simple file systems accessed by query and retrieval tools
provides the elementary level of functionality. Data
warehouses provide additional functionality. OLAP provides
highest level of functionality.
•Examples:
–Accessing Student data base from 2000 to 2010 ......

12. Model and data-retrieving DSS
•Examples:
–Collect weather observations at all
stations and forecast tomorrow’s
weather

13. Communication-driven DSS
•A communication-driven DSS use
network and comminication
technologies to faciliate collaboartion on
decision making. It supports more than
one person working on a shared task.
• examples include integrated tools like
Microsoft's NetMeeting or Groove
(Stanhope 2002), Vide conferencing.
•It is related to group decision support
systems.

14. Document-driven DSS
•A document-driven DSS uses storage
and processing technologies to document
retrieval and analysis. It manages,
retrieves and manipulates unstructured
information in a variety of electronic
formats.
•Document database may include: Scanned
documents, hypertext documents, images,
sound and video.
•A search engine is a primary tool
associated with document drivel DSS.

15. Characteristics and Capabilities of
DSS
•DSS (Figure 3.1)
1. Provide support in semi-structured and
unstructured situations
2. Support for various managerial levels
3. Support to individuals and groups
4. Support to interdependent and/or
sequential decisions
5. Support all phases of the decision-making
process
6. Support a variety of decision-making
processes and styles

16. 7. Are adaptive
8. Have user friendly interfaces
9. Goal is to improve the effectiveness of decision
making
10. The decision maker controls the decision-making
process
11. End-users can build simple systems
12. Utilizes models for analysis
13. Provides access to a variety of data sources,
formats, and types
Characteristics and Capabilities of
DSS

17. Components of DSS
•Three fundamental components of DSS:
–the database management system (DBMS),
–the model management system (MBMS), and
–the dialog generation and management system (DGMS).
•the Data Management Component stores information
(which can be further subdivided into that derived from an
organization's traditional data repositories, from external
sources such as the Internet, or from the personal insights
and experiences of individual users);
•the Model Management Component handles
representations of events, facts, or situations (using
various kinds of models, two examples being optimization
models and goal-seeking models); and
•the User Interface Management Component is of course
the component that allows a user to interact with the
system.

18. The Structure of Decision Support Systems
Model
Management
Data
Management
Dialog
Management
Decision Support System
Internal and External
Databases
User
...
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19. 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
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20. Database Management System
•Extracts data
•Manages data and their relationships
•Updates (add, delete, edit, change)
•Retrieves data (accesses it)
•Queries and manipulates data
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21. DSS Database Issues
•Data warehouse
•Data mining
•Extraction of data from internal, external, and private
sources
•Web browser data access
•database servers
•Multimedia databases
•Online Analytical Processing (OLAP)
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22. Online Analytical Processing
•OLAP
–Enables managers and analysts to
manipulate large amounts of detailed
and consolidated data from many
perspectives
–Done interactively, in real time, with
rapid response to queries
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23. Online Analytical Operations
Roll-Up
Aggregation of data
Ex: sales office data, rolled up to the district level
Drill-Down
Display underlying detail data
Ex: sales figures by individual product
Slicing and Dicing
Viewing database from different viewpoints
Often performed along a time axis
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24. Online Analytical Operations
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25. Data Mining
•Main purpose is to provide decision support to
managers and business professionals through
knowledge discovery
•Analyzes vast store of historical business data
•Tries to discover patterns, trends, and correlations
hidden in the data that can help a company
improve its business performance
•Use regression, decision tree, neural network,
cluster analysis, or market basket analysis

26. Data Visualization Systems
•DVS
–DSS that represents complex data using
interactive three-dimensional graphical forms
such as charts, graphs, and maps
–DVS tools help users to interactively sort,
subdivide, combine, and organize data while it
is in its graphical form.

27. The User
Different usage patterns for the user, the
manager, or the decision maker
•Managers
•Staff specialists
•Intermediaries
1. Staff assistant
2. Expert tool user
3. Business (system) analyst
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28. DSS Hardware
Evolved with computer hardware and
software technologies
Major Hardware Options
•Mainframe
•Workstation
•Personal computer
•Web server system
–Internet
–Intranets
–Extranets
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29. MIS vs. DSS
Management
Information Systems
Decision Support
Systems
Decision
support
provided
Provide information
about the performance of
the organization
Provide information and
techniques to analyze
specific problems
Information
form and
frequency
Periodic, exception,
demand, and push
reports and responses
Interactive inquiries and
responses
Information
format
Pre-specified, fixed
format
Ad hoc, flexible, and
adaptable format
Information
processing
methodology
Information produced by
extraction and
manipulation of business
data
Information produced by
analytical modeling
of business data
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30. Expert Systems
•ES
•A knowledge-based information system
(KBIS) that uses its knowledge about a
specific, complex application to act as an
expert consultant to end users
•KBIS is a system that adds a knowledge
base to the other components on an IS

31. Expert System Components
•Knowledge Base
–Facts about specific subject area
–Heuristics that express the reasoning procedures of an
expert (rules of thumb)
•Software Resources
–Inference engine processes the knowledge and makes
inferences to make recommend course of action
–User interface programs to communicate with end user
–Explanation programs to explain the reasoning process to
end user

32. Enterprise Information Portals
•A Web-based interface and integration of
MIS, DSS, EIS, and other technologies
–Available to all intranet users and select
extranet users
–Provides access to a variety of internal and
external business applications and services
–Typically tailored or personalized to the user
or groups of users
–Often has a digital dashboard
–Also called enterprise knowledge portals
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33. Business Intelligence
Applications
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