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Transcript
Chapter
10
Supporting Decision Making
McGraw-Hill/Irwin
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved.
Learning Objectives
• Identify the changes taking place in the form
and use of decision support in business
• Identify the role and reporting alternatives
of management information systems
• Describe how online analytical processing
can meet key information needs of
managers
• Explain the decision support system concept
and how it differs from traditional
management information systems
10-2
Learning Objectives
• Explain how the following information
systems can support the information needs of
executives, managers, and business
professionals
– Executive information systems
– Enterprise information portals
– Knowledge management systems
• Identify how neural networks, fuzzy logic,
genetic algorithms, virtual reality, and
intelligent agents can be used in business
10-3
Learning Objectives
• Give examples of several ways expert
systems can be used in business decisionmaking situations
10-4
Decision Support in Business
• Provide responses to:
– Changing market conditions
– Customer needs
• Several types of systems
– Management information
– Decision support
– Other information systems
10-5
RWC 1: Fact-Based Decision Making
• Decisions based on facts beat decisions
based on gut
• Dashboard
– Makes detailed statistics available in real-time
• Scorecard
– Software compares details to defined metrics
• How prepared are organizations to
synthesize and share key performance
indicators?
• How prepared are executives to draw insight
from information?
10-6
Levels of Managerial Decision Making
10-7
Attributes of Information Quality
10-8
Decision Structure
• Structured (operational)
– Procedures can be specified in advance
• Unstructured (strategic)
– Not possible to specify procedures in advance
• Semi-structured (tactical)
– Decision procedures can be pre-specified,
but not enough to lead to the correct decision
10-9
Decision Support Systems
Management Information
Systems
Decision Support
Systems
Decision
support
provided
Provide information about the
performance of the
organization
Provide information and
techniques to analyze
specific problems
Information form
and frequency
Periodic, exception, demand,
and push reports and
responses
Interactive inquiries and
responses
Prespecified, fixed format
Ad hoc, flexible, and
adaptable format
Information produced by
extraction and manipulation of
business data
Information produced by
analytical modeling of
business data
Information
format
Information
processing
methodology
10-10
Decision Support Trends
Add info from new
paragraphs
10-11
Business Intelligence Applications
10-12
DSS Components
10-13
Management Information Systems
10-14
Online Analytical Processing
10-15
GIS and DVS Systems
10-16
Using Decision Support Systems
10-17
Data Mining
• Provides decision support through
knowledge discovery
– Analyzes vast stores of historical business data
– Looks for patterns, trends, and correlations
– Goal is to improve business performance
• Types of analysis
–
–
–
–
–
Regression
Decision tree
Neural network
Cluster detection
Market basket analysis
10-18
Market Basket Analysis
• One of the most common uses for data
mining
– Determines what products customers purchase
together with other products
• Other uses
–
–
–
–
–
–
Cross Selling
Product Placement
Affinity Promotion
Survey Analysis
Fraud Detection
Analyze Customer Behavior
10-19
Executive Information Systems (EIS)
• Combines many features of MIS and DSS
• Provides immediate and easy information
• Identifies critical success factors
• Features
– Customizable graphical user interfaces
– Exception reports
– Trend analysis
– Drill down capability
10-20
Enterprise Information Portal Components
10-21
Enterprise Knowledge Portal
10-22
RWC 2: Shopping in Virtual Stores
• Benefits of virtual stores
–
–
–
–
–
–
–
Help understand customer behavior
Test products faster, more convenient and precise
Win shelf space
Focus on ways to get customers’ attention
Avoids tipping off competitors
Cuts testing time
Avoid displays that clash with store decor
• Environment of virtual shopping
– Change variables with each test
10-23
Attributes of Intelligent Behavior
10-24
Domains of Artificial Intelligence
10-25
Components of an Expert System
10-26
Methods of Knowledge Representation
• Case-Based
– Examples from the past
• Frame-Based
– Collection of knowledge about an entity
• Object-Based
– Data elements include both data and the methods
or processes that act on those data
• Rule-Based
– Factual statements in the form of a premise and a
conclusion (If, Then)
10-27
Expert System Application Categories
• Decision Management
– Loan portfolio analysis
– Employee performance evaluation
– Insurance underwriting
• Diagnostic/Troubleshooting
– Equipment calibration
– Help desk operations
– Medical diagnosis
– Software debugging
10-28
Expert System Application Categories
• Design/Configuration
• Selection/Classification
• Process Monitoring/Control
10-29
Benefits of Expert Systems
• Captures human experience in a computerbased information system
Limitations of Expert Systems
• Limited focus
• Inability to learn
• Maintenance problems
• Development cost
• Can only solve specific types of problems
in a limited domain of knowledge
10-30
Development Tool
• Expert System Shell
– The easiest way to develop an expert system
– A software package consisting of an expert system
without its knowledge base
– Has an inference engine and user interface
programs
10-31
Knowledge Engineering
• A knowledge engineer
– Works with experts to capture the knowledge they
possess
• Facts and rules of thumb
– Builds the knowledge base
• if necessary, the rest of the expert system
– Similar role to systems analysts
10-32
Neural Networks
• Modeled after the brain’s mesh-like network
of interconnected processing elements
(neurons)
– Interconnected processors operate in parallel
and interact with each other
– Allows the network to learn from the data it
processes
10-33
Example of Fuzzy Logic Rules and Query
10-34
Genetic Algorithms
• Genetic algorithm software
– Uses Darwinian, randomizing, and other
mathematical functions
– Simulates an evolutionary process, yielding
increasingly better solutions to a problem
– Used to model a variety of scientific, technical, and
business processes
– Useful when thousands of solutions are possible
10-35
Virtual Reality (VR)
• Virtual reality is a computer-simulated reality
– Fast-growing area of artificial intelligence
– Originated from efforts to build natural, realistic,
multi-sensory human-computer interfaces
– Relies on multi-sensory input/output devices
– Creates a three-dimensional world through
sight, sound, and touch
• Telepresence
– Using VR to perform a task in a different location
10-36
Intelligent Agents
• Software surrogate for an end user or a
process that fulfills a stated need or activity
– Uses built-in and learned knowledge base to
accomplish tasks
• Software robots or bots
10-37
Types of Intelligent Agents
• User Interface Agents
–
–
–
–
Interface Tutors
Presentation Agents
Network Navigation Agents
Role-Playing Agents
• Information Management Agents
– Search Agents
– Information Brokers
– Information Filters
10-38
RWC 3: Driving Competitive Advantage
• Advanced technologies impact businesses
– Goodyear reduced time to market
– Public Utility Company JEA determines optimal
combinations of oil and natural gas
– The Ohio State University Medical Center
(OSUMC) uses robots to move supplies
10-39
10-39
RWC 4: Business Intelligence Deployments
•
•
•
•
•
Analyze raw data (e.g., sales transactions)
Extract useful insights
Can transform business processes
Can impact the bottom line
Major impediment - most companies don’t
understand their business processes well
enough
• Uncovering flawed business processes beats
merely to monitoring
10-40