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BUSINESS DRIVEN
TECHNOLOGY
UNIT 3: Enhancing Business Decisions
OPENING CASE
Revving Up Sales at Harley-Davidson
Unit Three
• The chapters in this unit include:
– Chapter Nine – Enabling the Organization – Decision
Making
– Chapter Ten – Extending the Organization – Supply
Chain Management
– Chapter Eleven – Building a Customer-centric
Organization – Customer Relationship Management
– Chapter Twelve – Integrating the Organization from
End to End – Enterprise Resource Planning
Unit Three
• Decision-enabling, problem-solving, and
opportunity-seizing systems
BUSINESS DRIVEN
TECHNOLOGY
Chapter Nine:
Enabling the Organization –
Decision Making
LEARNING OUTCOMES
9.1 Define the four systems organizations use to
make decisions and gain competitive advantages
9.2 Describe the three quantitative models typically
used by decision support systems
9.3 Describe the relationship between digital
dashboards and executive information systems
LEARNING OUTCOMES
9.4 List and describe three types of artificial
intelligence systems
9.5 Describe three types of data-mining analysis
capabilities
CHAPTER NINE OVERVIEW
• The amount of information people must understand
to make decisions, solve problems, and find
opportunities is growing exponentially
CHAPTER NINE OVERVIEW
• Model – a simplified representation or abstraction
of reality
• The following systems use models to support
decision making, problem solving, and opportunity
capturing:
– Decision support systems (DSS)
– Executive information systems (EIS)
– Artificial intelligence (AI)
– Data mining
DECISION SUPPORT SYSTEMS
•
Decision support system (DSS) – models information
to support managers and business professionals during
the decision-making process
•
Three quantitative models typically used by DSSs:
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
2. What-if analysis – checks the impact of a change in an
assumption on the proposed solution
3. Goal-seeking analysis – finds the inputs necessary to achieve
a goal such as a desired level of output
DECISION SUPPORT SYSTEMS
•
What-if Analysis
DECISION SUPPORT SYSTEMS
•
Goal-seeking analysis
EXECUTIVE INFORMATION SYSTEMS
•
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
EXECUTIVE INFORMATION SYSTEMS
•
Digital dashboard – integrates information from
multiple components and present it in a unified
display
ARTIFICAL INTELLIGENCE (AI)
•
Intelligent systems – various commercial
applications of artificial intelligence
•
Artificial intelligence (AI) – simulates human
intelligence such as the ability to reason and learn
and typically can:
– Learn or understand from experience
– Make sense of ambiguous or contradictory information
– Use reasoning to solve problems and make decisions
ARTIFICAL INTELLIGENCE (AI)
•
The ultimate goal of AI is the ability to build a
system that can mimic human intelligence
ARTIFICAL INTELLIGENCE (AI)
•
The three most common categories of AI include:
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
DATA MINING
•
Data-mining software typically includes many
forms of AI such as neural networks and expert
systems
DATA MINING
•
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
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
Statistical Analysis
•
Statistical analysis – performs such functions as
information correlations, distributions,
calculations, and variance analysis
– Forecasts – predictions made on the basis of timeseries information
– Time-series information – time-stamped information
collected at a particular frequency
OPENING CASE STUDY QUESTIONS
Revving Up Sales at Harley-Davidson
1.
Explain how Talon helps Harley-Davidson employees
improve their decision-making capabilities and highlights
potential business opportunities
2.
Assess the business impact Harley-Davidson could gain
by using executive information systems
3.
Determine how Harley-Davidson can benefit from using
artificial intelligence to support its business operations
CHAPTER NINE CASE
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:
–
–
–
–
–
–
Simplify information
Consolidate information
Enhance infrastructure operations
Reduce complexity
Increase performance
Streamline business processes
CHAPTER NINE 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