Transcript Decision Making

CHAPTER 9
Enabling OrganizationDecision Making
Learning Outcomes
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Define the four 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 systems
List and describe three types of artificial intelligence
systems
Describe three types of data-mining analysis
capabilities
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Overview
 Model – a simplified representation or
abstraction of reality
 The following systems use models to support
decision making, problem solving, and
opportunity capturing:
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Decision support systems (DSS)
Executive information systems (EIS)
Artificial intelligence (AI)
Data mining
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Decision Support System (DSS)
 DSS – models information to support managers and
business professionals during the decision-making
process
 Three quantitative models typically used by DSSs:
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Sensitivity analysis – the study of the impact that
changes in one (or more) parts of the model have on
other parts of the model
What-if analysis – checks the impact of a change in
an assumption on the proposed solution
Goal-seeking analysis – finds the inputs necessary to
achieve a goal such as a desired level of output
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What-If Analysis
Goal Seeking Analysis
Executive Information System (EIS)
 Executive information system (EIS) – a
specialized DSS that supports senior level
executives within the organization
 Most EISs offering the following capabilities:
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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
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Digital dashboard – integrates information from multiple
components and present it in a unified display
Artificial Intelligence (AI)
 Intelligent systems – various commercial
applications of artificial intelligence
 AI – simulates human intelligence such as
the ability to reason and learn and typically
can:
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Learn or understand from experience
Make sense of ambiguous or contradictory
information
Use reasoning to solve problems and make
decisions
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AI Components
1. Expert systems – computerized advisory programs
that imitate the reasoning processes of experts in
solving difficult problems
2. Neural Networks – attempts to emulate the way the
human brain works
3. Intelligent agents – special-purposed knowledgebased information system that accomplishes specific
tasks on behalf of its users
Most expert systems contain information from many
human experts and can therefore perform a better
analysis than any single human
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Data Mining
 Data-mining software typically includes many
forms of AI such as neural networks and
expert systems
 Common forms of data-mining analysis
capabilities include
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Cluster analysis
Association detection
Statistical analysis
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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
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Some Examples of Cluster Analysis
 Consumer goods by content, brand loyalty or
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similarity
Product market typology for tailoring sales strategies
Retail store layouts and sales performances
Corporate decision strategies using social
preferences
Control, communication, and distribution of
organizations
Industry processes, products, and materials
Design of assembly line control functions
Character recognition logic in OCR readers
Data base relationships in management information
systems
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Association Detection
 Association detection – 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
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Statistical Analysis
 Statistical analysis – performs such
functions as information correlations,
distributions, calculations, and variance
analysis
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Forecasts – predictions made on the basis of
time-series information
Time-series information – time-stamped
information collected at a particular frequency
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Finding the Best Buy
 Best Buy has annual revenues of over $1
billion and employs over 10,000 people
 The company uses data-mining to:
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Simplify information
Consolidate information
Enhance infrastructure operations
Reduce complexity
Increase performance
Streamline business processes
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Case Questions
1. Summarize why decision making has
improved at Best Buy with the
implementation of a data warehouse
2. Determine what types of information might
be presented to a Best Buy marketing
executive through a digital dashboard
3. Evaluate how Best Buy could use the
information in the data warehouse for sales
forecasting
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