Learning
Outcomes
- Define the systems organizations use to make decisions and gain competitive advantages
- Describe the three quantitative models typically used by decision support systems
- Describe the relationship between digital dashboards and executive information system
- List and describe four types of artificial intelligence system
- Describe three types of data-mining analysis capabilities
Decision
Making
- 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
- IT systems in an enterprise
Transaction
Processing Systems
- 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
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.
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.
- 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
- Expert system – computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems.
- Neural Network – attempts to emulate the way the human brain works.
- Genetic algorithm – an artificial intelligent system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem.
- Intelligent agent – special-purposed knowledge-based information system that accomplishes specific tasks on behalf of its users
- Multi-agent systems
- Agent-based modeling
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:
- Cluster analysis
- Association detection
- Statistical analysis
Cluster
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
- 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
- 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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