CHAPTER 9 : ENABLING THE ORGANIZATION – DECISION MAKING
DECISION MAKING
·
Reasons
for the growth of decision-making information systems
· 
·
Model
– a simplified representation or abstraction of reality
·
IT
systems in an enterprise
· 
TRANSACTION PROCESSING SYSTEM (TPS)
·
Moving
up through the organizational pyramid users move from requiring transactional information to
analytical information
·
Transaction
processing system - the basic
business system that serves the operational level (analysts) in an organization
·
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
·
Online
analytical processing (OLAP) –
the manipulation of information to create business intelligence in support of
strategic decision making
DECISION
SUPPORT SYSTEMS (DSS)
·
Models
information to support managers and business professionals during the
decision-making process.
·
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
· 
EXECUTIVE
INFORMATION SYSTEMS
·
A
specialized DSS that supports senior level executives within the organization
·
Most
EISs offering the following capabilities:
1.
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.
2.
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.
3.
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
·
Digital
dashboard – integrates information from
multiple components and presents it in a unified display
ARTIFICIAL
INTELLIGENCE (AI)
·
Intelligent
system – various commercial applications
of artificial intelligence
·
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
·
Four
most common categories of AI include:
1.
Expert system – computerized advisory programs that imitate the reasoning
processes of experts in solving difficult problems.
Eg: Playing Chess.
2.
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
3.
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.
4.
Intelligent
agent – special-purposed knowledge-based
information system that accomplishes specific tasks on behalf of its users
·
Multi-agent
systems
·
Agent-based
modelling
Eg: Shopping bot: Software
that will search several retailers’ websites and provide a comparison of each
retailers’ offering including prive and availability.
DATA
MINING
·
Data-mining
software includes many forms of AI such as neural networks and expert systems
· 
·
Common
forms of data-mining analysis capabilities include:
1.
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
Eg: Consumer
goods by content, brand loyalty or similarity
2.
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 behaviour and predict future behaviour 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.
3.
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 flavour, colour, aroma,
texture, and appearance for all of its lines of foods
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