Decision Support And Business Intelligence Systems

Cover
Contents
Preface
Part I: Decision Support and Business Intelligence
	Chapter 1 Decision Support Systems and Business Intelligence
		1.1 Opening Vignette: Norfolk Southern Uses Business Intelligence for Decision Support to Reach Success
		1.2 Changing Business Environments and Computerized Decision Support
		1.3 Managerial Decision Making
		1.4 Computerized Support for Decision Making
		1.5 An Early Framework for Computerized Decision Support
		APPLICATION CASE 1.1 Giant Food Stores Prices the Entire Store
		1.6 The Concept of Decision Support Systems (DSS)
		APPLICATION CASE 1.2 A DSS for Managing Inventory at GlaxoSmithKline
		1.7 A Framework for Business Intelligence (BI)
		APPLICATION CASE 1.3 Location, Location, Location
		APPLICATION CASE 1.4 Alltel Wireless: Delivering the Right Message, to the Right Customers, at the Right Time
		1.8 A Work System View of Decision Support
		1.9 The Major Tools and Techniques of Managerial Decision Support
		APPLICATION CASE 1.5 United Sugars Corporation Optimizes Production, Distribution, and Inventory Capacity with Different Decision Support Tools
		1.10 Plan of the Book
		APPLICATION CASE 1.6 The Next Net
		1.11 Resources, Links, and the Teradata University Network Connection
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		END OF CHAPTER APPLICATION CASE: Vodafone Uses Business Intelligence to Improve Customer Growth and Retention Plans
		References
Part II: Computerized Decision Support
	Chapter 2 Decision Making, Systems, Modeling, and Support
		2.1 Opening Vignette: Decision Modeling at HP Using Spreadsheets
		2.2 Decision Making: Introduction and Definitions
		2.3 Models
		2.4 Phases of the Decision-Making Process
		2.5 Decision Making: The Intelligence Phase
		APPLICATION CASE 2.1 Making Elevators Go Faster!
		2.6 Decision Making: The Design Phase
		TECHNOLOGY INSIGHTS 2.1 The Difference Between a Criterion and a Constraint
		TECHNOLOGY INSIGHTS 2.2 Are Decision Makers Really Rational?
		2.7 Decision Making: The Choice Phase
		2.8 Decision Making: The Implementation Phase
		2.9 How Decisions Are Supported
		TECHNOLOGY INSIGHTS 2.3 Decision Making in the Digital Age
		APPLICATION CASE 2.2 Advanced Technology for Museums: RFID Makes Art Come Alive
		2.10 Resources, Links, and the Teradata University Network Connection
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		End of Chapter Application Case: Decisions and Risk Management (!) That Led to the Subprime Mortgage Crisis
		References
	Chapter 3 Decision Support Systems Concepts, Methodologies, and Technologies: An Overview
		3.1 Opening Vignette: Decision Support System Cures for Health Care
		3.2 Decision Support System Configurations
		3.3 Decision Support System Description
		APPLICATION CASE 3.1 A Spreadsheet-Based DSS Enables Ammunition Requirements Planning for the Canadian Army
		3.4 Decision Support System Characteristics and Capabilities
		3.5 Decision Support System Classifications
		APPLICATION CASE 3.2 Expertise Transfer System to Train Future Army Personnel
		3.6 Components of Decision Support Systems
		3.7 The Data Management Subsystem
		APPLICATION CASE 3.3 Pacific Sunwear Tracks Business Performance
		TECHNOLOGY INSIGHTS 3.1 The Capabilities of a Relational DBMS in a DSS
		TECHNOLOGY INSIGHTS 3.2 The 10 Essential Ingredients of Data (Information) Quality Management
		3.8 The Model Management Subsystem
		APPLICATION CASE 3.4 SNAP DSS Helps OneNet Make Telecommunications Rate Decisions
		TECHNOLOGY INSIGHTS 3.3 Major Functions of an MBMS
		3.9 The User Interface (Dialog) Subsystem
		TECHNOLOGY INSIGHTS 3.4 Next Generation of Input Devices
		3.10 The Knowledge-Based Management Subsystem
		APPLICATION CASE 3.5 IAP Systems’ Intelligent DSS Determines the Success of Overseas Assignments and Learns from the Experience
		3.11 The Decision Support System User
		3.12 Decision Support System Hardware
		3.13 A DSS Modeling Language: Planners Lab
		APPLICATION CASE 3.6 Nonprofits Use Planners Lab as a Decision-Making Tool
		3.14 Resources, Links, and the Teradata University Network Connection
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		END OF CHAPTER APPLICATION CASE: Spreadsheet Model-Based Decision Support for Inventory Target Setting at Procter & Gamble
		References
	Chapter 4 Modeling and Analysis
		4.1 Opening Vignette: Model-Based Auctions Serve More Lunches in Chile
		4.2 Management Support Systems Modeling
		APPLICATION CASE 4.1 Lockheed Martin Space Systems Company Optimizes Infrastructure Project-Portfolio Selection
		APPLICATION CASE 4.2 Forecasting/Predictive Analytics Proves to be a Good Gamble for Harrah’s Cherokee Casino and Hotel
		4.3 Structure of Mathematical Models for Decision Support
		4.4 Certainty, Uncertainty, and Risk
		4.5 Management Support Systems Modeling with Spreadsheets
		APPLICATION CASE 4.3 Showcase Scheduling at Fred Astaire East Side Dance Studio
		4.6 Mathematical Programming Optimization
		APPLICATION CASE 4.4 Spreadsheet Model Helps Assign Medical Residents
		TECHNOLOGY INSIGHTS 4.1 Linear Programming
		4.7 Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking
		4.8 Decision Analysis with Decision Tables and Decision Trees
		APPLICATION CASE 4.5 Decision Analysis Assists Doctor in Weighing Treatment Options for Cancer Suspects and Patients
		4.9 Multicriteria Decision Making with Pairwise Comparisons
		APPLICATION CASE 4.6 Multicriteria Decision Support for European Radiation Emergency Support System
		4.10 Problem-Solving Search Methods
		APPLICATION CASE 4.7 Heuristic-Based DSS Moves Milk in New Zealand
		4.11 Simulation
		APPLICATION CASE 4.8 Improving Maintenance Decision Making in the Finnish Air Force Through Simulation
		APPLICATION CASE 4.9 Simulation Applications
		4.12 Visual Interactive Simulation
		4.13 Quantitative Software Packages and Model Base Management
		4.14 Resources, Links, and the Teradata University Network Connection
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		END OF CHAPTER APPLICATION CASE: HP Applies Management Science Modeling to Optimize Its Supply Chain and Wins a Major Award
		References
Part III: Business Intelligence
	Chapter 5 Data Mining for Business Intelligence
		5.1 Opening Vignette: Data Mining Goes to Hollywood!
		5.2 Data Mining Concepts and Applications
		APPLICATION CASE 5.1 Business Analytics and Data Mining Help 1-800-Flowers Excel in Business
		TECHNOLOGY INSIGHTS 5.1 Data in Data Mining
		APPLICATION CASE 5.2 Law Enforcement Organizations Use Data Mining to Better Fight Crime
		APPLICATION CASE 5.3 Motor Vehicle Accidents and Driver Distractions
		5.3 Data Mining Applications
		APPLICATION CASE 5.4 A Mine on Terrorist Funding
		5.4 Data Mining Process
		APPLICATION CASE 5.5 Data Mining in Cancer Research
		5.5 Data Mining Methods
		APPLICATION CASE 5.6 Highmark, Inc., Employs Data Mining to Manage Insurance Costs
		5.6 Data Mining Software Tools
		APPLICATION CASE 5.7 Predicting Customer Churn—A Competition of Different Tools
		5.7 Data Mining Myths and Blunders
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		END OF CHAPTER APPLICATION CASE: Data Mining Helps Develop Custom-Tailored Product Portfolios for Telecommunication Companies
		References
	Chapter 6 Artificial Neural Networks for Data Mining
		6.1 Opening Vignette: Predicting Gambling Referenda with Neural Networks
		6.2 Basic Concepts of Neural Networks
		TECHNOLOGY INSIGHTS 6.1 The Relationship Between Biological and Artificial Neural Networks
		APPLICATION CASE 6.1 Neural Networks Help Reduce Telecommunications Fraud
		6.3 Learning in Artificial Neural Networks
		APPLICATION CASE 6.2 Neural Networks Help Deliver Microsoft’s Mail to the Intended Audience
		6.4 Developing Neural Network–Based Systems
		TECHNOLOGY INSIGHTS 6.2 ANN Software
		6.5 Illuminating the Black Box of ANN with Sensitivity Analysis
		APPLICATION CASE 6.3 Sensitivity Analysis Reveals Injury Severity Factors in Traffic Accidents
		6.6 A Sample Neural Network Project
		6.7 Other Popular Neural Network Paradigms
		6.8 Applications of Artificial Neural Networks
		APPLICATION CASE 6.4 Neural Networks for Breast Cancer Diagnosis
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		END OF CHAPTER APPLICATION CASE: Coors Improves Beer Flavors with Neural Networks
		References
	Chapter 7 Text and Web Mining
		7.1 Opening Vignette: Mining Text for Security and Counterterrorism
		7.2 Text Mining Concepts and Definitions
		TECHNOLOGY INSIGHTS 7.1 Text Mining Lingo
		APPLICATION CASE 7.1 Text Mining for Patent Analysis
		7.3 Natural Language Processing
		APPLICATION CASE 7.2 Text Mining Helps Merck to Better Understand and Serve Its Customers
		7.4 Text Mining Applications
		APPLICATION CASE 7.3 Mining for Lies
		APPLICATION CASE 7.4 Flying Through Text
		7.5 Text Mining Process
		APPLICATION CASE 7.5 Research Literature Survey with Text Mining
		7.6 Text Mining Tools
		7.7 Web Mining Overview
		7.8 Web Content Mining and Web Structure Mining
		APPLICATION CASE 7.6 Caught in a Web
		7.9 Web Usage Mining
		7.10 Web Mining Success Stories
		APPLICATION CASE 7.7 Web Site Optimization Ecosystem
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		END OF CHAPTER APPLICATION CASE: HP and Text Mining
		References
	Chapter 8 Data Warehousing
		8.1 Opening Vignette: DirecTV Thrives with Active Data Warehousing
		8.2 Data Warehousing Definitions and Concepts
		APPLICATION CASE 8.1 Enterprise Data Warehouse Delivers Cost Savings and Process Efficiencies
		8.3 Data Warehousing Process Overview
		APPLICATION CASE 8.2 Data Warehousing Supports First American Corporation’s Corporate Strategy
		8.4 Data Warehousing Architectures
		8.5 Data Integration and the Extraction, Transformation, and Load (ETL) Processes
		APPLICATION CASE 8.3 BP Lubricants Achieves BIGS Success
		8.6 Data Warehouse Development
		APPLICATION CASE 8.4 Things Go Better with Coke’s Data Warehouse
		APPLICATION CASE 8.5 HP Consolidates Hundreds of Data Marts into a Single EDW
		TECHNOLOGY INSIGHTS 8.1 Hosted Data Warehouses
		APPLICATION CASE 8.6 A Large Insurance Company Integrates Its Enterprise Data with AXIS
		8.7 Real-Time Data Warehousing
		APPLICATION CASE 8.7 Egg Plc Fries the Competition in Near-Real-Time
		TECHNOLOGY INSIGHTS 8.2 The Real-Time Realities of Active Data Warehousing
		8.8 Data Warehouse Administration and Security Issues
		TECHNOLOGY INSIGHTS 8.3 Ambeo Delivers Proven Data Access Auditing Solution
		8.9 Resources, Links, and the Teradata University Network Connection
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		END OF CHAPTER APPLICATION CASE: Continental Airlines Flies High with Its Real-Time Data Warehouse
		References
	Chapter 9 Business Performance Management
		9.1 Opening Vignette: Double Down at Harrah’s
		9.2 Business Performance Management (BPM) Overview
		9.3 Strategize: Where Do We Want to Go?
		9.4 Plan: How Do We Get There?
		9.5 Monitor: How Are We Doing?
		APPLICATION CASE 9.1 Discovery-Driven Planning: The Coffee Wars
		9.6 Act and Adjust: What Do We Need to Do Differently?
		9.7 Performance Measurement
		APPLICATION CASE 9.2 Expedia.com’s Customer Satisfaction Scorecard
		9.8 BPM Methodologies
		TECHNOLOGY INSIGHTS 9.1 BSC Meets Six Sigma
		9.9 BPM Technologies and Applications
		9.10 Performance Dashboards and Scorecards
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		END OF CHAPTER APPLICATION CASE: Tracking Citywide Performance
		References
Part IV: Collaboration, Communication, Group Support Systems, and Knowledge Management
	Chapter 10 Collaborative Computer-Supported Technologies and Group Support Systems
		10.1 Opening Vignette: Procter & Gamble Drives Ideation with Group Support Systems
		10.2 Making Decisions in Groups: Characteristics, Process, Benefits, and Dysfunctions
		TECHNOLOGY INSIGHTS 10.1 Benefits of Working in Groups and Dysfunctions of the Group Process
		10.3 Supporting Groupwork with Computerized Systems
		APPLICATION CASE 10.1 GSS Boosts Innovation in Crime Prevention
		TECHNOLOGY INSIGHTS 10.2 Unsupported Aspects of Communication
		10.4 Tools for Indirect Support of Decision Making
		APPLICATION CASE 10.2 Catalyst Maintains an Edge with WebEx
		10.5 Integrated Groupware Suites
		APPLICATION CASE 10.3 Wimba Extends Classrooms at CSU, Chico
		10.6 Direct Computerized Support for Decision Making: From Group Decision Support Systems to Group Support Systems
		TECHNOLOGY INSIGHTS 10.3 Modeling in Group Decision Making: EC11 for Groups
		APPLICATION CASE 10.4 Collaborative Problem Solving at KUKA
		APPLICATION CASE 10.5 Eastman Chemical Boosts Creative Processes and Saves $500,000 with Groupware
		10.7 Products and Tools for GDSS/GSS and Successful Implementation
		TECHNOLOGY INSIGHTS 10.4 The Standard GSS Process
		10.8 Emerging Collaboration Tools: From VoIP to Wikis
		TECHNOLOGY INSIGHTS 10.5 VoIP System Helps Increase Productivity and Enhance Learning Experiences at the State University of New York
		10.9 Collaborative Efforts in Design, Planning, and Project Management
		APPLICATION CASE 10.6 CPFR Initiatives at Ace Hardware and Sears
		10.10 Creativity, Idea Generation, and Computerized Support
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		END OF CHAPTER APPLICATION CASE: Dresdner Kleinwort Wasserstein Uses Wiki for Collaboration
		References
	Chapter 11 Knowledge Management
		11.1 Opening Vignette: MITRE Knows What It Knows Through Knowledge Management
		11.2 Introduction to Knowledge Management
		APPLICATION CASE 11.1 KM at Consultancy Firms
		APPLICATION CASE 11.2 Cingular Calls on Knowledge
		11.3 Organizational Learning and Transformation
		APPLICATION CASE 11.3 NASA Blends KM with Risk Management
		11.4 Knowledge Management Activities
		11.5 Approaches to Knowledge Management
		APPLICATION CASE 11.4 Texaco Drills for Knowledge
		TECHNOLOGY INSIGHTS 11.1 KM: A Demand-Led Business Activity
		11.6 Information Technology (IT) In Knowledge Management
		11.7 Knowledge Management Systems Implementation
		APPLICATION CASE 11.5 Knowledge Management: You Can Bank on It at Commerce Bank
		11.8 Roles of People in Knowledge Management
		APPLICATION CASE 11.6 Online Knowledge Sharing at Xerox
		TECHNOLOGY INSIGHTS 11.2 Seven Principles for Designing Successful COP
		11.9 Ensuring the Success of Knowledge Management Efforts
		TECHNOLOGY INSIGHTS 11.3 MAKE: Most Admired Knowledge Enterprises
		APPLICATION CASE 11.7 The British Broadcasting Corporation Knowledge Management Success
		APPLICATION CASE 11.8 How the U.S. Department of Commerce Uses an Expert Location System
		TECHNOLOGY INSIGHTS 11.4 Six Keys to KM Success for Customer Service
		TECHNOLOGY INSIGHTS 11.5 KM Myths
		APPLICATION CASE 11.9 When KMS Fail, They Can Fail in a Big Way
		TECHNOLOGY INSIGHTS 11.6 Knowledge Management Traps
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		END OF CHAPTER APPLICATION CASE: Siemens Keeps Knowledge Management Blooming with ShareNet
		References
Part V: Intelligent Systems
	Chapter 12 Artificial Intelligence and Expert Systems
		12.1 Opening Vignette: A Web-Based Expert System for Wine Selection
		12.2 Concepts and Definitions of Artificial Intelligence
		APPLICATION CASE 12.1 Intelligent System Beats the Chess Grand Master
		12.3 The Artificial Intelligence Field
		TECHNOLOGY INSIGHTS 12.1 Artificial Intelligence Versus Natural Intelligence
		APPLICATION CASE 12.2 Automatic Speech Recognition in Call Centers
		APPLICATION CASE 12.3 Agents for Travel Planning at USC
		12.4 Basic Concepts of Expert Systems
		TECHNOLOGY INSIGHTS 12.2 Sample Session of a Rule-Based ES
		APPLICATION CASE 12.4 Expert System Helps in Identifying Sport Talents
		12.5 Applications of Expert Systems
		APPLICATION CASE 12.5 Sample Applications of ES
		12.6 Structure of Expert Systems
		APPLICATION CASE 12.6 A Fashion Mix-and-Match Expert System
		12.7 Knowledge Engineering
		TECHNOLOGY INSIGHTS 12.3 Difficulties in Knowledge Acquisition
		12.8 Problem Areas Suitable for Expert Systems
		APPLICATION CASE 12.7 Monitoring Water Quality with Sensor-Driven Expert Systems
		12.9 Development of Expert Systems
		12.10 Benefits, Limitations, and Critical Success Factors of Expert Systems
		12.11 Expert Systems on the Web
		APPLICATION CASE 12.8 Banner with Brains:Web-Based ES for Restaurant Selection
		APPLICATION CASE 12.9 Rule-Based System for Online Consultation
		TECHNOLOGY INSIGHTS 12.4 Automated and Real-Time Decision Systems
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		END OF CHAPTER APPLICATION CASE: Business Rule Automation at Farm Bureau Financial Services
		References
	Chapter 13 Advanced Intelligent Systems
		13.1 Opening Vignette: Machine Learning Helps Develop an Automated Reading Tutoring Tool
		13.2 Machine-Learning Techniques
		13.3 Case-Based Reasoning
		APPLICATION CASE 13.1 A CBR System for Optimal Selection and Sequencing of Songs
		13.4 Genetic Algorithms and Developing GA Applications
		APPLICATION CASE 13.2 Genetic Algorithms Schedule Assembly Lines at Volvo Trucks North America
		TECHNOLOGY INSIGHTS 13.1 Genetic Algorithm Software
		13.5 Fuzzy Logic and Fuzzy Inference Systems
		13.6 Support Vector Machines
		13.7 Intelligent Agents
		TECHNOLOGY INSIGHTS 13.2 Intelligent Agents, Objects, and ES
		13.8 Developing Integrated Advanced Systems
		APPLICATION CASE 13.3 International Stock Selection
		APPLICATION CASE 13.4 Hybrid ES and Fuzzy Logic System Dispatches Trains
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		END OF CHAPTER APPLICATION CASE: Improving Urban Infrastructure Management with Case-Based Reasoning
		References
Part VI: Implementing Decision Support Systems and Business Intelligence
	Chapter 14 Management Support Systems: Emerging Trends and Impacts
		14.1 Opening Vignette: Coca-Cola’s RFID-Based Dispenser Serves a New Type of Business Intelligence
		14.2 RFID and New BI Application Opportunities
		14.3 Reality Mining
		14.4 Virtual Worlds
		TECHNOLOGY INSIGHTS 14.1 Second Life as a Decision Support Tool
		14.5 The Web 2.0 Revolution
		14.6 Virtual Communities
		14.7 Online Social Networking: Basics and Examples
		APPLICATION CASE 14.1 Using Intelligent Software and Social Networking to Improve Recruiting Processes
		14.8 Cloud Computing and BI
		14.9 The Impacts of Management Support Systems: An Overview
		14.10 Management Support Systems Impacts on Organizations
		14.11 Management Support Systems Impacts on Individuals
		14.12 Automating Decision Making and the Manager’s Job
		14.13 Issues of Legality, Privacy, and Ethics
		14.14 Resources, Links, and the Teradata University Network Connection
		Chapter Highlights
		Key Terms
		Questions for Discussion
		Exercises
		END OF CHAPTER APPLICATION CASE: Continental Continues to Score with Data Warehouse
		References
Glossary
	A
	B
	C
	D
	E
	F
	G
	H
	I
	K
	L
	M
	N
	O
	P
	Q
	R
	S
	T
	U
	V
	W
Index
	A
	B
	C
	D
	E
	F
	G
	H
	I
	J
	K
	L
	M
	N
	O
	P
	Q
	R
	S
	T
	U
	V
	W
	X
	Y