Ø Reasons for the growth of
decision-making information systems
§ People need to analyze
large amounts of information
§ People must make decisions
quickly
§ People must apply sophisticated analysis techniques, such as
modeling and forecasting, to make good decisions
§ People must protect the
corporate asset of organizational information
Ø Model – a simplified representation or abstraction of reality
Ø IT systems in an enterprise'
Transaction
Processing Systems(TPS)
Ø Moving up through the
organizational pyramid users move from requiring transactional information to
analytical information
Ø Transaction processing
system - the basic business system that serves the operational level (analysts)
in an organization
Ø Online transaction
processing (OLTP) – the capturing of transaction and event information using
technology to (1) process the information according to defined business rules,
(2) store the information, (3) update existing information to reflect the new
information
Ø Online analytical
processing (OLAP) – the manipulation of information to create business
intelligence in support of strategic decision making
Ø Decision Support
Systems(DSS)
Models information to support managers and business professionals
during the decision-making process.
Ø Three quantitative models
used by DSSs include:
1. Sensitivity analysis
– the study of the impact that changes in one (or more) parts of the model have
on other parts of the model.
Eg: What will happen to the supply chain if a tsunami in Sabah
reduces holding inventory from 30% to 10%?
2. What-if analysis –
checks the impact of a change in an assumption on the proposed solution.
Eg: Repeatedly changing revenue in small increments to determine it
effects on other variables.
3. Goal-seeking analysis
– finds the inputs necessary to achieve a goal such as a desired level of
output.
Eg: Determine how
many customers must purchase a new product to increase gross profits to $5
million
What-if analysis
Goal-seeking analysis
Interaction between a TPS and a DSS
Ø Executive Information
Systems
A specialized DSS that supports senior level executives within the
organization
Ø Most EISs offering the
following capabilities:
§ Consolidation –
involves the aggregation of information and features simple roll-ups to complex
groupings of interrelated information.
Eg: Data for different sales
representatives can be rolled up to an office level. Then state level, then a
regional sales level.
§ Drill-down – enables
users to get details, and details of details, of information.
Eg: From regional sales data then drill down to each sales
representatives at each office.
§ Slice-and-dice – looks
at information from different perspectives.
Eg: One slice of information could display all product sales during
a given promotion, another slice could display a single product’s sales for all
promotions.
Interaction between a TPS and an EIS
Ø Digital dashboard –
integrates information from multiple components and presents it in a unified
display
Ø Intelligent system –
various commercial applications of artificial intelligence
Ø Artificial intelligence
(AI) – simulates human intelligence such as the ability to reason and learn
§ Advantages: can check
info on competitor
The ultimate goal of AI is
the ability to build a system that can mimic human intelligence
Ø Four most common categories
of AI include:
Expert system – computerized advisory programs that imitate the
reasoning processes of experts in solving difficult problems.
Eg: Playing
Chess.
Neural Network – attempts to emulate the way the human brain works.
Eg: Finance industry uses neural network to review loan applications and create
patterns or profiles of applications that fall into two categories – approved
or denied.
Fuzzy logic – a mathematical method of handling imprecise or
subjective information.
Eg: Washing machines that determine by themselves how
much water to use or how long to wash
Genetic algorithm
– an artificial intelligent system that mimics the evolutionary,
survival-of-the-fittest process to generate increasingly better solutions to a
problem.
Eg: Business
executives use genetic algorithm to help them decide which combination of
projects a firm should invest.
Intelligent agent
– special-purposed knowledge-based information system that accomplishes
specific tasks on behalf of its users
• Multi-agent systems
• Agent-based modeling
Eg: Shopping bot: Software that will search
several retailers’ websites and provide a comparison of each retailers’
offering including prive and availability
#Common forms of data-mining analysis capabilities include:
- Cluster analysis
- Association detection
- Statistical analysis
Cluster analysis – a technique used to divide an information set
into mutually exclusive groups such that the members of each group are as close
together as possible to one another and the different groups are as far apart
as possible
CRM systems depend on cluster analysis to segment customer
information and identify behavioral traits
Eg: Consumer goods by content, brand loyalty or similarity
Association detection – reveals the degree to which variables are
related and the nature and frequency of these relationships in the information
Market basket analysis – analyzes such items as Web sites and
checkout scanner information to detect customers’ buying behavior and predict
future behavior by identifying affinities among customers’ choices of products
and services
Eg: Maytag uses association detection to ensure that each generation
of appliances is better than the previous generation.
Statistical analysis – performs such functions as information
correlations, distributions, calculations, and variance analysis
- Forecast – predictions made on the basis of time-series information
- Time-series information – time-stamped information collected at a
particular frequency
Eg: Kraft uses statistical analysis to assure consistent flavor,
color, aroma, texture, and appearance for all of its lines of foods
