DECISION MAKING
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Reasons for growth of decision-making information systems
1. People need to analyze large amounts
of information—Improvements
in technology itself, innovations in communication, and globalization have
resulted in a dramatic increase in the alternatives and dimensions people need
to consider when making a decision or appraising an opportunity.
2. People must make decisions quickly—Time is of the essence and people
simply do not have time to sift through all the information manually.
3. People must apply sophisticated
analysis techniques, such as modeling and forecasting, to make good decisions—Information systems substantially
reduce the time required to perform these sophisticated analysis techniques.
4. People must protect the corporate
asset of organizational information— Information systems offer the security
required to ensure organizational information remains safe.
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Model –
a simplified representation or abstraction of reality. Models can calculate
risks, understand uncertainty, change variables, and manipulate time
IT systems in an
enterprise
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Decision support system (DSS) – models information to support managers and
business professionals during the decision-making process
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Executive information system (EIS) – a specialized DSS that supports senior level
executives within the organization
•
Artificial intelligence (AI) – simulates human intelligence such as the
ability to reason and learn
•
Data mining – typically includes many forms of AI such as neural networks and
expert systems. Data mining tools apply
algorithms to information sets to uncover inherent trends and patterns in the
information.
TRANSACTION PROCESSING SYSTEMS
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Moving
up through the organizational pyramid users move from requiring transactional
information to analytical information
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The
structure of a typical organization is similar to a pyramid
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Organizational
activities occur at different levels of the pyramid
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People
in the organization have unique information needs and thus require various sets
of IT tools (see Figure)
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At
the lower levels of the pyramid, people perform daily tasks such as processing
transactions
•
Moving
up through the organizational pyramid, people (typically managers) deal less
with the details (“finer” information) and more with meaningful aggregations
of information (“coarser” information) that help them make broader decisions
for the organization
•
Granularity
refers to the extent of detail in the information (means fine and detailed or
“coarse” and abstract information)
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Transaction processing system - the basic business system that serves the operational level (analysts) in
an organization
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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
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Online analytical processing (OLAP) – the manipulation of information to create
business intelligence in support of strategic decision making
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Analysts
typically use TPS to perform their daily tasks
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What
types of TPS are used at your college?
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Payroll
system (Tracking hourly employees)
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Accounts
Payable system
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Accounts
Receivable system
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Course
registration system
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Human
resources systems (tracking vacation, sick days)
DECISION SUPPORT SYSTEMS
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Decision support system (DSS) – models information to support managers and
business professionals during the decision-making process
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In
a DSS, data is first queried and collected from the knowledge database
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Results
from the query are then checked and analyzed against decision models
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Once
checked against the decision models, the results are then generated for review
to find a “best” solution for the situation
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One
national insurance company uses DSSs to analyze the amount of risk the company
is undertaking when it insures drivers who have a history of driving under the
influence of alcohol. The DSS discovered that only 3 percent of married male
homeowners in their forties received more than one DUI. The company decided to
lower rates for customers falling into this category, which increased its revenue
while mitigating its risk.
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Three
quantitative models used by DSSs include:
Sensitivity analysis – the study of the impact that
changes in one (or more) parts of the model have on other parts of the model. Sensitivity
analysis – studies the impact on a single change in a current model. For example – if we continually change the
amount of inventory we carry, how low can our inventories go before issues
start occurring in other parts of the supply chain? This would require changing the inventory
level and watching the model to see “how sensitive” it is to inventory levels.
What-if analysis – checks the impact of a change in
an assumption on the proposed solution. What-if analysis – determines the
impact of change on an assumption or an input.
For example – if the economic condition improves, how will it affect our
sales?
Goal-seeking analysis – finds the inputs necessary to
achieve a goal such as a desired level of output. Goal-seeking analysis –
solves for a desired goal. For example –
we want to improve revenues by 30 percent, how much does sales have to increase
and costs have to decrease to meet this goal?
EXECUTIVE INFORMATION SYSTEMS
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Executive information system (EIS) – a specialized DSS that supports senior level
executives within the organization
•
Most
EISs offering the following capabilities:
Consolidation – involves the aggregation of information and
features simple roll-ups to complex groupings of interrelated information
Drill-down – enables users to get details, and details of
details, of information
Slice-and-dice – looks at information from different
perspectives
Interaction between a
TPS and an EIS
Why would you need interaction between a TPS
and EIS?
§ The EIS needs information from the
TPS to help executives make decisions
§ Without knowing order information,
inventory information, and shipping information from the TPSs, it would be very
difficult for the CEO to make strategic decisions for the organization
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Digital dashboard – integrates information from multiple components and presents it in a
unified display. As digital dashboards become easier to use, more executives
can perform their own analysis without inundating IT personnel with queries and
request for reports
ARTIFICIAL INTELLIGENCE (AI)
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Intelligent system – various commercial applications of artificial intelligence
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Artificial intelligence (AI) – simulates human intelligence such as the
ability to reason and learn
•
RivalWatch
offers a strategic business information service using AI that enables
organizations to track the product offerings, pricing policies, and promotions
of online competitors
•
Clients
can determine the competitors they want to watch and the specific information
they wish to gather, ranging from products added, removed, or out of stock to
price changes, coupons offered, and special shipping terms
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RivalWatch
allows its clients to check each competitor, category, and product either
daily, weekly, monthly, or quarterly
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The
ultimate goal of AI is the ability to build a system that can mimic human
intelligence
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Four
most common categories of AI include:
1. Expert system
§
A computerized advisory programs that imitate the
reasoning processes of experts in solving difficult problems. Example robot.
§ Human
expertise is transferred to the expert system, and users can access the expert
system for specific advice
§ Most
expert systems contain information from many human experts and can therefore
perform a better analysis than any single human
2. Neural Network
§ attempts to emulate the way the human
brain works. Example California police.
§ Fuzzy logic – a mathematical method of handling
imprecise or subjective information
§ Neural
networks are most useful for decisions that involve patterns or image
recognition
§ Typically
used in the finance industry to discover credit card fraud by analyzing
individual spending behavior
3.
Genetic algorithm
§
an artificial intelligent system that mimics the
evolutionary, survival-of-the-fittest process to generate increasingly better
solutions to a problem.
§ Example to determine fiber optic by
telecommunication
§ Essentially
an optimizing system, it finds the combination of inputs that give the best
outputs
4.
Intelligent agent
§ special-purposed knowledge-based
information system that accomplishes specific tasks on behalf of its users.
§ Example Ford Motor Co. Balance with cost
and demands.
§ Used for
environmental scanning and competitive intelligence
§ An
intelligent agent can learn the types of competitor information users want to
track, continuously scan the Web for it, and alert users when a significant
event occurs
§ RivalWatch
uses intelligent agents
DATA MINING
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
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CRM systems depend on
cluster analysis to segment customer information and identify behavioral traits
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Some examples of
cluster analysis include:
§ Consumer
goods by content, brand loyalty or similarity
§ Product
market typology for tailoring sales strategies
§ Retail
store layouts and sales performances
§ Corporate
decision strategies using social preferences
2.
Association detection
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reveals the degree to
which variables are related and the nature and frequency of these relationships
in the information
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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
3.
Statistical analysis –
•
performs such
functions as information correlations, distributions, calculations, and
variance analysis
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Forecast – predictions made on the basis of
time-series information
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Time-series information – time-stamped information collected at a
particular frequency
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