DECISION MAKING
- Reasons for the growth of decision making information systems
- people need to analyze large amounts of information
- people must make decision 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
TRANSACTION PROCESSING SYSTEMS
v Transaction
processing system -
the basic business system that serves the
operational level (analysts) in an organization
v 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
v Online
analytical processing (OLAP) – the
manipulation of information to create business intelligence in support of
strategic decision making
DECISION SUPPORT SYSTEMS
- Models information to support managers and business professionals during the decision-making process
vThree
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.
EXECUTIVE INFORMATION SYSTEMS
- A specialized DSS that supports senior level executives within the organization
vMost
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.
Artificial
Intelligence (AI)
v Intelligent
system – various commercial applications of
artificial intelligence
v Artificial
intelligence (AI) – simulates
human intelligence such as the ability to reason and learn
Advantages:
can check info on competitor
v 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 retailer’s websites and provide a comparison of each retailers’s
offering including prive and availability.
Data Mining
- common forms of data-mining analysis capabilities include :
- cluster analysis
- association detection
- statistical analysis
CLUSTER ANALYSIS
v 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
v 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
v 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
v CRM
systems depend on cluster analysis to segment customer information and identify
behavioral traits
•Eg:
Consumer goods by content, brand loyalty or similarity
STATISTICAL ANALYSIS
v 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
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