Decision Support and Business Intelligence Systems / Edition 8

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Overview

This completely revised and re-titled edition now incorporates an expanded coverage of Business Intelligence (BI) reflecting the emphasis that most DSS courses are now taking. New features: Five new chapters (5-9) on Business Intelligence topics. Links to Teradata University Network assignment material and other online resources provided in most chapters.

I: Decision Support and Business Intelligence: 1. Decision Support Systems and Business Intelligence, II: Computerized Decision Support: 2. Decision Making Systems, Modeling, and Support, 3. Decision Support Systems Concepts, Methodologies, and Technologies: An Overview, 4. Modeling and Analysis, III: Business Intelligence: 5. Special Introductory Section: The Essentials of Business Intelligence, 5. Data Warehousing, 6. Business Analytics and Data Visualization, 7. Data, Text, and Web Mining, 9. Business Performance Management, IV: Collaboration, Communication, Group Support Systems, and Knowledge Management, 10. Collaborative Computing-Supported Technologies and Group Support Systems, 11. Knowledge Management, V: Intelligent Systems, 12. Artificial Intelligence and Expert Systems, 13. Advanced Intelligent Systems, 14. Intelligent Systems over the Internet, VI: Implementing Decisions Support Systems, 15. Systems Development and Acquisition, 16. Integration, Impacts, and the Future of Management Support Systems.

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Product Details

  • ISBN-13: 9780131986602
  • Publisher: Prentice Hall
  • Publication date: 1/1/2007
  • Series: Alternative eText Formats Series
  • Edition description: Older Edition
  • Edition number: 8
  • Pages: 850
  • Product dimensions: 8.01 (w) x 10.03 (h) x 1.53 (d)

Meet the Author

Efraim Turban (M.B.A., Ph.D., University of California Berkeley) is a visiting scholar at the Pacific Institute for Information System Management, University of Hawaii. Prior to this he was on the staff of several universities including City University of Hong Kong, Lehigh University, Florida International University, California State University Long Beach, Eastern Illinois University, and the University of Southern California. Dr. Turban is the author of over 100 refereed papers published in leading journals such as Management Science, MIS Quarterly, and Decision Support Systems. He also the author of 20 books including Electronic Commerce: A Managerial Perspective and Information Technology for Management. He is also a consultant to major corporations world wide. Dr. Turban’s current areas of interest are Web-based decision support systems, using intelligent agents in electronic commerce systems, and collaboration issues in global electronic commerce.

Jay E. Aronson (M.S., M.S., Ph.D., Carnegie Mellon University) is a professor of Management Information Systems in the Terry College of Business at The University of Georgia. Prior to this he was on the faculty at Southern Methodist University. Dr. Aronson is the author of about 50 refereed papers that have appeared in leading journals including Management Science, Information Systems Research, and MIS Quarterly. He is the author of three books, and contributes to several professional encyclopedias. He is also a consultant to major international corporations and organizations. Dr. Aronson’s current areas of research include knowledge management, collaborative computing, and parallel computing.

Ting-Peng Liang (MA, Ph.D., University of Pennsylvania) is a National Chair Professor of Information Systems at National Sun Yat-sen University in Taiwan and a visiting professor at Chinese University of Hong Kong. Prior to this, he had been on the faculties of University of Illinois (Urbana-Champaign) and Purdue University. Dr. Liang has published more than 50 referred research papers in leading journals such as Management Science, MIS Quarterly, Decision Support Systems, and Journal of MIS. He is also the author of three books and a consultant to several major companies in the United States and Taiwan. Dr. Liang’s current areas for research and teaching include Web-based intelligent systems, electronic commerce, knowledge management, and strategic applications of information technologies.

Ramesh Sharda (MBA, Ph.D, University of Wisconsin-Madison) is Director of the Institute for Research in Information Systems (IRIS), ConocoPhillips Chair of Management of Technology, and a Regents Professor of Management Science and Information Systems in the Spears School of Business Administration at Oklahoma State University. He started and served as the Director of the MS in Telecommunications Management Program at OSU. Over 100 papers describing his research have been published in major journals including Management Science, Information Systems Research, Decision Support Systems, Journal of Management Information Systems, and many others. Dr. Sharda serves on several editorial boards including INFORMS Journal on Computing, Decision Support Systems, and Information Systems Frontiers. His current research interests are in decision support systems, collaborative applications, and technologies for managing information overload. Dr. Sharda is also a co-founder of a company that produces virtual trade fairs, iTradeFair.com.

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Table of Contents


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