Preface | p. xxiii |
Decision Support Systems and Business Intelligence | p. 1 |
Decision Support Systems and Business Intelligence | p. 3 |
Opening Vignette: Toyota Uses Business Intelligence to Excel | p. 4 |
Changing Business Environments and Computerized Decision Support | p. 6 |
Managerial Decision Making | p. 9 |
Computerized Support for Decision Making | p. 11 |
An Early Framework for Computerized Decision Support | p. 14 |
Intelligent Price Setting Using an ADS | p. 17 |
Decision Support at Hallmark for Better Strategy and Performance | p. 19 |
The Concept of Decision Support Systems | p. 20 |
The Houston Minerals Case | p. 21 |
Helping Atlantic Electric Survive in the Deregulated Marketplace | p. 23 |
A Framework for Business Intelligence | p. 24 |
Predictive Analytics Helps Collect Taxes | p. 29 |
A Work System View of Decision Support | p. 30 |
The Major Tools and Techniques of Managerial Decision Support | p. 31 |
United Sugars Corporation Optimizes Production, Distribution, and Inventory Capacity with Different Decision Support Tools | p. 33 |
Implementing Computer-Based Managerial Decision Support Systems | p. 34 |
Plan of the Book | p. 35 |
Resources, Links, and the Teradata University Network Connection | p. 37 |
End of Chapter Application Case Decision Support at a Digital Hospital | p. 41 |
References | p. 42 |
Computerized Decision Support | p. 43 |
Decision Making, Systems, Modeling, and Support | p. 45 |
Opening Vignette: Decision Making at the U.S. Federal Reserve | p. 46 |
Decision Making: Introductory and Definitions | p. 47 |
Models | p. 51 |
Phases of the Decision-Making Process | p. 53 |
Decision Making: The Intelligence Phase | p. 55 |
Decision Making: The Design Phase | p. 57 |
Decision Making Between a Rock and a Hard Place; or What Can You Do When There Are No Good or Even Feasible Alternatives? | p. 60 |
Decision Making From the Gut: When Intuition Can Fail | p. 64 |
Too Many Alternatives Spoils the Broth | p. 66 |
Decision Making: The Choice Phase | p. 68 |
Decision Making: The Implementation Phase | p. 69 |
How Decisions Are Supported | p. 70 |
Union Pacific Railroad: If You're Collecting Data, Use It Profitably! | p. 72 |
Advanced Technology for Museums: RFID Makes Art Come Alive | p. 75 |
Resources, Links, and the Teradata University Network Connection | p. 76 |
End of Chapter Application Case Strategic Decision Making in the Pharmaceutical Industry: How Bayer Decides Whether or Not to Develop a New Drug | p. 80 |
References | p. 81 |
Decision Support Systems Concepts, Methodologies, and Technologies: An Overview | p. 84 |
Opening Vignette: Decision Support System Cures For Health Care | p. 85 |
Decision Support Systems Configurations | p. 87 |
Decision Support Systems Description | p. 88 |
Web/GIS-Based DSS Aid in Disaster Relief and Identifying Food Stamp Fraud | p. 89 |
Decision Support Systems Characteristics and Capabilities | p. 90 |
Components of DSS | p. 92 |
The Data Management Subsystem | p. 97 |
Roadway Drives Legacy Applications onto the Web | p. 98 |
The Model Management Subsystem | p. 104 |
Web-Based Cluster Analysis DSS Matches Up Movies and Customers | p. 107 |
The User Interface (Dialog) Subsystem | p. 109 |
Clarissa: A Hands-Free Helper for Astronauts | p. 111 |
The Knowledge-Based Management Subsystem | p. 115 |
IAP Systems's Intelligent DSS Determines the Success of Overseas Assignments and Learns from the Experience | p. 116 |
The Decision Support Systems User | p. 116 |
Decision Support Systems Hardware | p. 117 |
Decision Support Systems Classification | p. 118 |
Database-Oriented DSS: Glaxo Wellcome Accesses Life-Saving Data | p. 119 |
Resources, Links, and the Teradata University Network Connection | p. 124 |
End of Chapter Application Case FedEx Tracks Customers Along with Packages | p. 127 |
References | p. 129 |
Modeling and Analysis | p. 131 |
Opening Vignette: Winning Isn't Everything...But Losing Isn't Anything: Professional Sports Modeling for Decision Making | p. 132 |
Management Support Systems Modeling | p. 135 |
United Airlines Model-Based DSS Flies the Friendly Skies | p. 137 |
Forecasting/Predictive Analytics Boosts Sales for Cox Communications | p. 139 |
Static and Dynamic Models | p. 142 |
Certainty, Uncertainty, and Risk | p. 143 |
Management Support Systems Modeling with Spreadsheets | p. 145 |
Decision Analysis with Decision Tables and Decision Trees | p. 147 |
Johnson & Johnson Decides About New Pharmaceuticals by Using Trees | p. 150 |
The Structure of Mathematical Models for Decision Support | p. 151 |
Mathematical Programming Optimization | p. 153 |
Complex Teacher Selection Is a Breeze in Flanders | p. 154 |
Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking | p. 158 |
Problem-Solving Search Methods | p. 162 |
Heuristic-Based DSS Moves Milk in New Zealand | p. 164 |
Simulation | p. 165 |
Pratt & Whitney Canada Gets Real Savings Through Virtual Manufacturing | p. 65 |
Simulation Applications | p. 170 |
Visual Interactive Simulation | p. 171 |
Quantitative Software Packages and Model Base Management | p. 173 |
Resources, Links, and the Teradata University Network Connection | p. 174 |
End of Chapter Application Case Major League Baseball Scheduling: Computerized Mathematical Models Take Us Out to the Ballgame | p. 180 |
References | p. 181 |
Business Intelligence | p. 185 |
Special Introductory Section: The Essentials of Business Intelligence | p. 187 |
A Preview of the Content of Chapters 5 through 9 | p. 187 |
The Origins and Drivers of Business Intelligence (BI) | p. 188 |
The General Process of Intelligence Creation and Use | p. 189 |
The Major Characteristics of Business Intelligence | p. 192 |
Toward Competitive Intelligence and Advantage | p. 195 |
The Typical Data Warehouse and Business Intelligence User Community | p. 197 |
Successful Business Intelligence Implementation | p. 198 |
France Telecom Business Intelligence | p. 199 |
Structure and Components of Business Intelligence | p. 201 |
Conclusion: Business Intelligence Today and Tomorrow | p. 203 |
Resources, Links and the Teradata University Network Connection | p. 203 |
References | p. 205 |
Data Warehousing | p. 206 |
Opening Vignette: Continental Airlines Flies High With Its Real-Time Data Warehouse | p. 206 |
Data Warehousing Definitions and Concepts | p. 209 |
Data Warehousing Process Overview | p. 212 |
Data Warehousing Supports First American Corporation's Corporate Strategy | p. 212 |
Data Warehousing Architectures | p. 214 |
Data Integration and the Extraction, Transformation, and Load (ETL) Processes | p. 222 |
Bank of America's Award-Winning Integrated Data Warehouse | p. 223 |
Data Warehouse Development | p. 226 |
Things Go Better with Coke's Data Warehouse | p. 227 |
HP Consolidates Hundreds of Data Marts into a Single EDW | p. 231 |
Real-Time Data Warehousing | p. 238 |
Egg Plc Fries the Competition in Near-Real-Time | p. 239 |
Data Warehouse Administration and Security Issues | p. 243 |
Resources, Links, and the Teradata University Network Connection | p. 244 |
End of Chapter Application Case Real-Time Data Warehousing at Overstock.com | p. 249 |
References | p. 250 |
Business Analytics and Data Visualization | p. 253 |
Opening Vignette: Lexmark International Improves Operations with Business Intelligence | p. 254 |
The Business Analytics (BA) Field: An Overview | p. 256 |
Ben & Jerry's Excels with BA | p. 257 |
Online Analytical Processing (OLAP) | p. 261 |
TCF Financial Corp.: Conducting OLAP, Reporting, and Data Mining | p. 266 |
Reports and Queries | p. 266 |
Multidimensionality | p. 269 |
Advanced Business Analytics | p. 273 |
Predictive Analysis Can Help You Avoid Traffic Jams | p. 274 |
Data Visualization | p. 276 |
Financial Data Visualization at Merrill Lynch | p. 279 |
Geographic Information Systems (GIS) | p. 280 |
GIS and GPS Track Where You Are and Help You with What You Do | p. 282 |
Real-time Business Intelligence Automated Decision Support (ADS), and Competitive Intelligence | p. 284 |
Business Analytics and the Web: Web Intelligence and Web Analytics | p. 289 |
Web Analytics Improves Performance for Online Merchants | p. 291 |
Usage, Benefits, and Success of Business Analytics | p. 292 |
Retailers Make Steady BI Progress | p. 294 |
End of Chapter Application Case State Governments Share Geospatial Information | p. 298 |
References | p. 299 |
Data, Text, and Web Mining | p. 302 |
Opening Vignette: Highmark, Inc., Employs Data Mining to Manage Insurance Costs | p. 302 |
Data Mining Concepts and Applications | p. 304 |
Data Help Foretell Customer Needs | p. 306 |
Motor Vehicle Accidents and Driver Distractions | p. 309 |
Data Mining to Identify Customer Behavior | p. 310 |
Customizing Medicine | p. 311 |
A Mine on Terrorist Funding | p. 312 |
Data Mining Techniques and Tools | p. 313 |
Data Mining Project Processes | p. 325 |
DHS Data Mining Spinoffs and Advances in Law Enforcement | p. 328 |
Text Mining | p. 329 |
Flying Through Text | p. 330 |
Web Mining | p. 333 |
Caught in a Web | p. 334 |
End of Chapter Application Case Hewlett-Packard and Text Mining | p. 340 |
References | p. 341 |
Neural Networks for Data Mining | p. 343 |
Opening Vignette: Using Neural Networks To Predict Beer Flavors with Chemical Analysis | p. 343 |
Basic Concepts of Neural Networks | p. 346 |
Neural Networks Help Reduce Telecommunications Fraud | p. 349 |
Learning in Artificial Neural Networks (ANN) | p. 355 |
Neural Networks Help Deliver Microsoft's Mail to the Intended Audience | p. 356 |
Developing Neural Network-Based Systems | p. 362 |
A Sample Neural Network Project | p. 367 |
Other Neural Network Paradigms | p. 370 |
Applications of Artificial Neural Networks | p. 372 |
Neural Networks for Breast Cancer Diagnosis | p. 373 |
A Neural Network Software Demonstration | p. 374 |
End of Chapter Application Case Sovereign Credit Ratings Using Neural Networks | p. 380 |
References | p. 381 |
Business Performance Management | p. 383 |
Opening Vignette: Cisco and the Virtual Close | p. 384 |
Business Performance Management (BPM) Overview | p. 386 |
Strategize: Where Do We Want To Go? | p. 388 |
Plan: How Do We Get There? | p. 390 |
Monitor; How are we Doing? | p. 392 |
Discovery-Driven Planning: The Case of Euro Disney | p. 94 |
Act and Adjust: What Do We Need To Do Differently | p. 395 |
Performance Measurement | p. 398 |
International Truck and Engine Corporation | p. 400 |
Business Performance Management Methodologies | p. 402 |
Business Performance Management Architecture and Applications | p. 409 |
Performance Dashboards | p. 417 |
Dashboards for Doctors | p. 419 |
Business Activity Monitoring (BAM) | p. 421 |
City of Albuquerque Goes Real-time | p. 422 |
End of Chapter Application Case Vigilant Information Systems at Western Digital | p. 428 |
References | p. 429 |
Collaboration, Communication, Group Support Systems, and Knowledge Management | p. 431 |
Collaborative Computer-Supported Technologies and Group Support Systems | p. 433 |
Opening Vignette: Collaborative Design at Boeing-Rocketdyne | p. 434 |
Making Decisions in Groups: Characteristics, Process, Benefits, and Dysfunctions | p. 436 |
Supporting Groupwork with Computerized Systems | p. 439 |
How General Motors is Collaborating Online | p. 440 |
Tools for Indirect Support of Decision Making | p. 443 |
Videoconferencing Is Ready for Prime Time | p. 446 |
Integrated Groupware Suites | p. 448 |
NetMeeting Provides a Real-Time Advantage | p. 449 |
Direct Computerized Support for Decision Making: From Group Decision Support Systems (GDSS) to Group Support Systems (GSS) | p. 452 |
Eastman Chemical Boosts Creative Processes and Saves $500,000 with Groupware | p. 456 |
Products and Tools for GDSS/GSS and Successful Implementation | p. 458 |
Emerging Collaboration Tools: From VoIP to Wikis | p. 462 |
Collaborative in Planning, Design, and Project Management | p. 465 |
CPFR Initiatives at Ace Hardware and Sears | p. 468 |
Creativity, Idea Generation, and Computerized Support | p. 469 |
End of Chapter Application Case Dresdner Kleinwort Wasserstein Uses Wiki for Collaboration | p. 475 |
References | p. 476 |
Knowledge Management | p. 478 |
Opening Vignette: Siemens Knows What It Knows Through Knowledge Management | p. 479 |
Introduction to Knowledge Management | p. 481 |
Cingular Calls on Knowledge | p. 485 |
Organizational Learning and Transformation | p. 486 |
Knowledge Management Activities | p. 488 |
Approaches to Knowledge Management | p. 490 |
Texaco Drills for Knowledge | p. 492 |
Information Technology (IT) in Knowledge Management | p. 495 |
Knowledge Management System (KMS) Implementation | p. 500 |
Portal Opens the Door to Legal Knowledge | p. 502 |
Knowledge Management: You Can Bank on It at Commerce Bank | p. 504 |
Roles of People in Knowledge Management | p. 507 |
Online Knowledge Sharing at Xerox | p. 510 |
Ensuring the Success of Knowledge Management Efforts | p. 513 |
The British Broadcasting Corporation Knowledge Management Success | p. 514 |
How the U.S. Department of Commerce Uses an Expert Location System | p. 515 |
When KMS Fail, They Can Fail in a Big Way | p. 518 |
End of Chapter Application Case DaimlerChrysler EBOKs with Knowledge Management | p. 524 |
References | p. 526 |
Intelligent Systems | p. 531 |
Artificial Intelligence and Expert Systems | p. 533 |
Opening Vignette: Cigna Uses Business Rules to Support Treatment Request Approval | p. 534 |
Concepts and Definitions of Artificial Intelligence | p. 535 |
Intelligent Systems Beat Chess Grand Master | p. 535 |
The Artificial Intelligence Field | p. 537 |
Automatic Speech Recognition in Call Centers | p. 542 |
Agents for Travel Planning at USC | p. 544 |
Basic Concepts of Expert Systems (ES) | p. 545 |
Applications of Expert Systems | p. 549 |
Sample Applications of Expert Systems | p. 549 |
Structure of Expert Systems | p. 552 |
How Expert Systems Work: Inference Mechanisms | p. 555 |
Problem Areas Suitable for Expert Systems | p. 558 |
Development of Expert Systems | p. 560 |
Benefits, Limitations, and Success Factors of Expert Systems | p. 564 |
Expert Systems on the Web | p. 567 |
Banner with Brains:Web-Based ES for Restaurant Selection | p. 568 |
Rule-Based System for Online Student Consulation | p. 568 |
End of Chapter Application Case Business Rule Automation at Farm Bureau Financial Services | p. 573 |
References | p. 574 |
Advanced Intelligent Systems | p. 575 |
Opening Vignette: Improving Urban Infrastructure Management in the City of Verdun | p. 576 |
Machine-Learning Techniques | p. 577 |
Case-Based Reasoning (CBR) | p. 580 |
CBR Improves Jet Engine Maintenance, Reduces Costs | p. 585 |
Genetic Algorithm Fundamentals | p. 587 |
Developing Genetic Algorithm Applications | p. 592 |
Genetic Algorithms Schedule Assembly Lines at Volvo Trucks North America | p. 593 |
Fuzzy Logic Fundamentals | p. 595 |
Natural Language Processing (NLP) | p. 598 |
Voice Technologies | p. 601 |
Developing Integrated Advanced Systems | p. 605 |
Hybrid ES and Fuzzy Logic System Dispatches Trains | p. 607 |
End of Chapter Application Case Barclays Uses Voice Technology to Excel | p. 611 |
References | p. 612 |
Intelligent Systems over the Internet | p. 614 |
Opening Vignette: Netflix Gains High Customer Satisfaction from DVD Recommendation | p. 615 |
Web-Based Intelligent Systems | p. 617 |
Intelligent Agents: An Overview | p. 629 |
Characteristics of Intelligent Agents | p. 622 |
Why Use Intelligent Agents? | p. 624 |
Classification and Types of Intelligent Agents | p. 626 |
Internet-Based Software Agents | p. 629 |
Fujitsu(Japan) Uses Agents for Targeted Advertising | p. 635 |
Wyndham Uses Intelligent Agents in Its Call Center | p. 637 |
Agents and Multiagents | p. 637 |
The Semantic Web: Representing Knowledge for the Intelligent Agents | p. 641 |
Web-Based Recommendation Systems | p. 647 |
Amazon.com Uses Collaborative Filtering to Recommend Products | p. 648 |
Content-Based Filtering at Euro Vacations.com | p. 653 |
Managerial Issues of Intelligent Agents | p. 654 |
End of Chapter Application Case Spartan Uses Intelligent Systems to Find the Right Person and Reduce Turnover | p. 659 |
References | p. 660 |
Implementing Decision Support Systems | p. 663 |
System Development and Acquisition | p. 665 |
Opening Vignette: Osram Sylvania Thinks Small, Strategizes Big to Develop the HR Infonet Portal System | p. 666 |
What Types of Support Systems Should You Build? | p. 668 |
The Landscape and Framework of Management Support Systems Application Development | p. 670 |
Development Options for Management Support System Applications | p. 673 |
Prototyping: A Practical Management Support System Development Methodology | p. 681 |
Criteria for Selecting an Management Support System Development Approach | p. 687 |
Third-Party Providers of Management Support System Software Packages and Suites | p. 689 |
Floriculture Partnership Streamlines Real-Time Ordering | p. 692 |
Connecting to Databases and Other Enterprise Systems | p. 693 |
The Rise of Web Services, XML, and the Service-Oriented Architecture | p. 695 |
Lincoln Financial Excels by Using Web Services | p. 696 |
User-Developed Management Support System | p. 697 |
End-User Development Using Wikis | p. 697 |
Management Support System Vendor and Software Selection | p. 700 |
Putting Together an Management Support System | p. 701 |
End of Chapter Application Case A Fully Integrated MSS for Sterngold: An Old Dental Manufacturer Adopts New IT Tricks | p. 705 |
References | p. 706 |
Integration, Impacts and the Future of Management Support Systems | p. 708 |
Opening Vignette: Elite Care Supported by Intelligent Systems | p. 709 |
Systems Integration: An Overview | p. 711 |
Types of Management Support System Integration | p. 715 |
Integration with Enterprise Systems and Knowledge Management | p. 720 |
The Impacts of Management Support Systems: An Overview | p. 725 |
Management Support Systems Impacts on Organizations | p. 726 |
Management Support Systems Impacts on Individuals | p. 730 |
Automating Decision Making and the Manager's Job | p. 731 |
Issues of Legality, Privacy, and Ethics | p. 733 |
Intelligent and Automated Systems and Employment Levels | p. 737 |
Robots | p. 738 |
Other Societal Impacts of Management Support Systems and the Digital Divide | p. 739 |
The Future of Management Support Systems | p. 742 |
End of Chapter Application Case An Intelligent Logistics Support System | p. 747 |
References | p. 748 |
Online Material | |
Enterprise Systems | p. 751 |
Knowledge Acquisition, Representation, and Reasoning | p. 752 |
Online Files | |
Representative Decision Support Tools | |
Decision Support Technologies and the Web | |
Emerging Technologies That May Benefit Decision Support | |
Additional References | |
Teradata University Network | |
Online Files | |
The MMS Running Case | |
Web Sources for Decision-Making Support Sampler | |
Further Reading | |
Online Files | |
Databases | |
Major Capabilities of the UIMS | |
Ad Hoc Visual Basic DSS Example | |
Further Reading on DSS | |
Online Files | |
Influence Diagrams | |
Links to Spreadsheet-Based DSS Excel Files in Chapter 4 | |
Spreadsheet-Based Economic Order Quantity Simulation Model | |
Waiting Line Modeling (Queueing) in a Spreadsheet | |
Linear Programming Optimization: The Blending Problem | |
Lindo Example: The Product-Mix Model | |
Lingo Example: The Product-Mix Model | |
The Goal Programming MBI Model | |
Links to Excel Files of Section 4.9 | |
Table of Models and Web Impacts | |
Model Base Management | |
Additional References | |
BI Preview Chapter Online Files | |
The General Process of Intelligence Creation and Use as Reflected in Continental Airline Case | |
BI Governance | |
The BI User Community | |
An Action Plan for the Information Systems Organization | |
Online Files | |
Capabilities of EIS | |
SAP Analytics | |
Trends in Visualization Products for Decision Support | |
Virtual Realty Visualization | |
Competitive Intelligence on the Internet | |
Cabela's | |
Online Files | |
Data Mining | |
Online Files | |
Heartdisease.sta | |
Creditrisk.xls | |
Movietrain.xls | |
Movietest.xls | |
Statistica Coupon | |
Online Files | |
Portfolio of Options | |
Rolling Forecasts and Real-Time Data | |
Effective Performance Measurement | |
Six Sigma Roles | |
Problems with Dashboard Displays | |
Online Files | |
Seven Sins of Deadly Meetings and Seven Steps to Salvation | |
Whiteboards | |
Internet Voting | |
GroupSystems Tools for Support of Group Processes | |
Collaboration in Designing Stores | |
Online Files | |
Leveraging Knowledge through Knowledge Management Systems | |
Online Files | |
Intelligent Systems | |
Internet-Based Intelligent Tutoring Systems | |
Automating the Help Desk | |
Assignment ES | |
Online Files | |
Steps in the CBR Process | |
Automating a Help Desk with Case-Based Reasoning | |
Automatic Translation of Web Pages | |
Online Files | |
Guidelines for a "Think Small, Strategize Big" Implementation | |
Project Management Software | |
Utility Computing | |
Agile Development and Extreme Programming (XP) | |
A Prototyping Approach to DSS Development | |
IBM's WebSphere Commerce Suite | |
XML, Web Services and Service-Oriented Architecture | |
The Process of Selecting a Software Vendor and an MSS Package | |
Online Files | |
An Active and Self-Evolving Model of Intelligent DSS | |
Cookies and Spyware | |
A Framework for Ethical Issues | |
A Hybrid Intelligent System | |
Online Tutorials | |
Systems | |
Forecasting | |
Text Mining Project | |
Statistica Software Project | |
References | p. 748 |
Glossary | p. 751 |
Index | p. 763 |
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