Download CISB434: Decision Support Systems

B. INFORMATION TECHNOLOGY (IS)
CISB434: DECISION SUPPORT
SYSTEMS
Chapter 1:
Introduction to Decision
Support Systems
LEARNING OUTCOMES

Identify information systems for aiding decision
making





MIS and DSS
Types of Decision-Support Systems
Components of DSS
DSS Applications
Web-based Customer DSS
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INTRODUCTION
DEFINITION

A Decision Support System (DSS)

assists management decision-making by
combining data;
 sophisticated analytical models and tools;
 and user friendly software


a single powerful system that can support

semi-structured and unstructured decision making
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INTRODUCTION TO DECISION
SUPPORT SYSTEMS
MIS and DSS
MANAGEMENT INFORMATION SYS.
MIS
 Earliest
applications for supporting
management decision
 Provide information on firm’s performance

help managers monitor and control the
business
 Produce

fixed, scheduled reports
data extracted and analyzed from Transaction
Processing System (TPS)
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MANAGEMENT INFORMATION SYS.
TYPICAL MIS REPORT
Summarise monthly sales
 Highlight exceptional conditions

e.g. drop of sales quotas below a set level
 employees have exceeded spending limit in health
care


Latest MISs offer online access
On-demand
 Intranet and Web-based

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DECISION-SUPPORT SYSTEMS
DSS
Provide nonroutine decisions and user control
 Emphasize change, flexibility and rapid response
 Easier access to structured information flows


Greater emphasis on models, assump-tions, ad hoc
queries and display
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DECISION-SUPPORT SYSTEMS
STRUCTURES OF PROBLEMS
Problems
Solutions Types
Solution
Provider
Structured
Known algorithms provide
Repetitive and routine solutions
MIS
No known algorithms.
Unstructured
Discuss, ruminate,
Novel and nonroutine
brainstorm to decide
DSS
Semistructured
Midway
Midway between the above
solution types
DSS
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EXAMPLE OF A STRUCTURED
SEMISTRUCTURED PROBLEM


Structured problem: How much will I earn
after two years if I invest $100,000 in
municipal bonds that pay 4 percent per
annum tax free?
Semistructured problem: If I invest $100,000
in stock XYZ and sell the stock in two years,
how much money will I make?
How are these problems different?
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
AND
EXAMPLES OF STRUCTURED AND
SEMISTRUCTURED PROBLEMS
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INTRODUCTION TO DECISION
SUPPORT SYSTEMS
Types of Decision Support System
TWO TYPES OF DSS
MODEL-DRIVEN
Stand-alone system
 Uses models to perform what-if analysis


Usually developed in isolation for a particular group
Utilizes strong theory or model
 Good user interface


Easy to use
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TWO TYPES OF DSS
MODEL-DRIVEN: EXAMPLE
13
TWO TYPES OF DSS
DATA-DRIVEN

Analyzes large pools of data from firm’s
information systems


Allows users to extract useful information
Data from Transaction Processing Sys-tems
(TPS) are collected in a Data Warehouse

Online analytical processing (OLAP) and data mining
are used to analyze the data
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DATA-DRIVEN DSS
OLAP
Traditional database queries provide onedimensional data analysis
 OLAP supports

multidimensional data analysis, and
 complex request for information

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DATA-DRIVEN DSS
DATA MINING

Data mining offers
insights into corporate data by finding hid-den
patterns and relationships
 inferring rules to predict future behaviour


Use the patterns and rules to
guide decision making
 forecast the effect of the decisions

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DATA-DRIVEN DSS
DATA MINING INFORMATION

Associations



occurrences linked to a single event
e.g. sales of drinks and crisps increases by 80% when
there is a football match
Sequences


linking of events over time
e.g. when a new house is bought, orders for kitchen
cabinet happens 65% after two weeks
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DATA-DRIVEN DSS
DATA MINING INFORMATION

Classification
describe a group to which an item belongs by
examining existing items and inferring a set of rules
 e.g. identify characteristics of customers who are
likely to leave, who they are, so as to devise special
campaign

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DATA-DRIVEN DSS
DATA MINING INFORMATION

Clustering
discover different groupings within data
 e.g. finding affinity groups for bank cards


Forecasting
Use a series of values to forecast what other values
will be
 e.g forecasting sales figures from prior sales
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DATA-DRIVEN DSS
DATA MINING TOOLS

Data mining uses



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statistical analysis tools
neural networks
fuzzy logic
genetic algorithms
rule-based systems
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DATA-DRIVEN DSS
KNOWLEDGE DISCOVERY

Data mining offers knowledge disco-very
the process of identifying novel and valuable pattern
 in large volumes of data
 through selection, preparation and evalua-tion of
contents of large databases

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INTRODUCTION TO DECISION
SUPPORT SYSTEMS
Components of DSS
COMPONENTS OF DSS
DSS DATABASE
 Collection
of
current or historical
data


e.g a small database
a Data Warehouse
 Extracts
or copies of
production database

avoids interfering
with operational systems
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COMPONENTS OF DSS
DSS SOFTWARE SYSTEM & UI
 Software
tools for
data analysis



OLAP tools
data mining tools
mathematical and
analytical models
 User



interface
easy interactions
supports dialogue
Web-based
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COMPONENTS OF DSS
MODELS
A model is an abstract representation to illustrate
the components or relation-ships of a
phenomenon
 DSS is built for a specific set of purpose


It has different collections of models
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COMPONENTS OF DSS
SOME DSS MODELS

Statistical models
full range of statistical functions: mean, median,
deviations, etc.
 ability to project future outcomes
 help to establish relationships


Optimization models

use linear programming to determine opti-mal
resource allocation, e.g. time or cost
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COMPONENTS OF DSS
SOME DSS MODELS

Forecasting models
use to forecast sales
 a range of historical data used to project future
conditions and sales


Sensitivity analysis
what-if analysis
 determines impact of changes in one or more factors
on outcomes

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INTRODUCTION TO DECISION
SUPPORT SYSTEMS
DSS Applications
DSS APPLICATIONS
SUPPLY CHAIN MANAGEMENT
Comprehensive examination of supply
management chain
 Searches for most efficient and cost-effective
combination
 Reduces overall costs
 Increases speed and accuracy of filling customer
orders

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DSS APPLICATIONS
CUSTOMER RELATIONSHIP MANAGEMENT
Uses data mining to guide decisions
 Consolidates customer information into massive
data warehouses
 Uses various analytical tools to slice information
into small segments

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DSS APPLICATIONS
CUSTOMER RELATIONSHIP MANAGEMENT
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INTRODUCTION TO DECISION
SUPPORT SYSTEMS
Web-based Customer DSS
WEB-BASED CUSTOMER DSS
Customers use multiple sources of in-formation
to make purchasing decision
 A Customer DSS

supports the decision-making process of customers
 provides online access to databases, infor-mation
pools and data analysis tools

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