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PART 1：DECISION MAKING AND COMPUTERIZED SUPPORT1

CHAPTER 1 Management Support Systems：An Overview3

1.1 Opening Vignette：Decision Support at Roadway Package System3

1.2 Managers and Decision Making5

1.3 Managerial Decision Making and Informative System6

1.4 Managers and Computerized Support9

1.5 The Need for Computerized Decision Support and the Supporting Technologies9

1.6 A Framework for Decision Support11

1.7 The Concept of Decision Support Systems13

1.8 Group Decision Support Systems15

1.9 Executive Information （Support） Systems17

1.10 Expert Systems17

1.11 Artificial Neural Networks18

1.12 Hybrid Support Systems19

1.13 The Evolution and Attributes of Computerized Decision Aids20

1.14 Plan of the Book23

Case Application 1.1：Manufacturing and Marketing of Machine Devices28

Appendix 1-A：Computer-Based Information Systems in a Personnel Department30

CHAPTER 2 Decision Making，Systems，Modeling，and Support32

2.1 Opening Vignette：How to Invest $1，000，00032

2.2 Introduction and Definitions33

2.3 Systems34

2.4 Models38

2.5 The Modeling Process：A Preview39

2.6 The Intelligence Phase42

2.7 The Design Phase43

2.8 The Choice Phase52

2.9 Evaluation：Multiple Goals，Sensitivity Analysis，What-If，and Goal Seeking55

2.10 The Implementation Phase59

2.11 How Decisions Are Supported60

2.12 Human Cognition and Decision Styles62

2.13 The Decision Makers63

PART 2：DECISION SUPPORT SYSTEMS71

CHAPTER 3 Decision Support Systems：An Overview73

3.1 Opening Vignette：Gotaas-Larsen Shipping Corp.73

3.2 DSS Configurations74

3.3 What Is a DSS？75

3.4 Characteristics and Capabilities of DSS77

3.5 Components of DSS78

3.6 The Data Management Subsystem79

3.7 The Model Management Subsystem82

3.8 The Knowledge Management Subsystem85

3.9 The User Interface （Dialog） Subsystem85

3.10 The User87

3.11 DSS Hardware88

3.12 Distinguishing DSS from Management Science and MIS88

3.13 Classifications of DSS90

Case Application 3.1：Decision Support for Military Housing Managers104

CHAPTER 4 Data Management：Warehousing，Access，and Visualization108

4.1 Opening Vignette：Data Warehousing and DSS at Group Health Cooperative108

4.2 Data Warehousing，Access，Analysis，and Visualization110

4.3 The Nature and Sources of Data111

4.4 Data Collection and Data Problems113

4.5 The Internet and Commercial Database Services113

4.6 Database Management Systems in DSS116

4.7 Database Organization and Structure117

4.8 Data Warehousing121

4.9 OLAP：Data Access and Mining，Querying，and Analysis125

4.10 Data Visualization and Multidimensionality130

4.11 Intelligent Databases and Data Mining132

4.12 The Big Picture135

Case Application 4.1：Data Warehousing at the Canadian Imperial Bank of Commerce141

CHAPTER 5 Modeling and Analysis145

5.1 Opening Vignette：Siemens Solar Industries Saves Millions by Simulation146

5.2 Modeling for MSS147

5.3 Static and Dynamic Models149

5.4 Treating Certainty，Uncertainty，and Risk150

5.5 Influence Diagrams150

5.6 MSS Modeling in Spreadsheets152

5.7 Decision Analysis of a Few Alternatives （Decision Tables and Trees）154

5.8 Optimization via Mathematical Programming158

5.9 Heuristic Programming161

5.10 Simulation163

5.11 Multidimensional Modeling167

5.12 Visual Spreadsheets170

5.13 Financial and Planning Modeling171

5.14 Visual Modeling and Simulation173

5.15 Ready-made Quantitative Software Packages178

5.16 Model Base Management180

CHAPTER 6 Knowledge-based Decision Support and Artificial Intelligence197

6.1 Opening Vignette：A Knowledge-based DSS in a Chinese Chemical Plant197

6.2 Concepts and Definitions199

6.3 Artificial Intelligence versus Natural Intelligence201

6.4 Knowledge in Artificial Intelligence202

6.5 How Artificial Intelligence Differs from Conventional Computing204

6.6 Does a Computer Really Think？205

6.7 The Artificial Intelligence Field206

6.8 Types of Knowledge-based Decision Support Systems214

6.9 Intelligent Decision Support Systems215

6.10 The Future of Artificial Intelligence218

Appendix 6-A：Human Problem Solving：An Information Processing Approach （The Newell-Simon Model）224

CHAPTER 7 User Interface and Decision Visualization Applications227

7.1 Opening Vignette：Geographic Information System at Dallas Area Rapid Transit227

7.2 User Interfaces：An Overview228

7.3 Interface Modes （Styles）231

7.4 Graphics233

7.5 Multimedia and Hypermedia235

7.6 Virtual Reality240

7.7 Geographic Information Systems （GIS）243

7.8 Natural Language Processing：An Overview247

7.9 Natural Language Processing：Methods248

7.10 Applications of Natural Language Processing and Software251

7.11 Speech （Voice） Recognition and Understanding252

7.12 Research on User Interfaces in MSS257

Case Application 7.1：Nabisco Tracks Attendance Using Voice Technologies263

CHAPTER 8 Constructing a Decision Support System and DSS Research266

8.1 Opening Vignette：Hospital Healthcare Services Uses DSS266

8.2 Introduction267

8.3 Development Strategies268

8.4 The DSS Development Process269

8.5 The Development Process：Life Cycle versus Prototyping272

8.6 Team-developed versus User-developed DSS274

8.7 Team-developed DSS275

8.8 End-user Computing and User-developed DSS276

8.9 DSS Technology Levels and Tools279

8.10 Selection of DSS Development Tools281

8.11 Developing DSS283

8.12 DSS Research Directions283

8.13 The DSS of the Future286

Case Application 8.1：Wesleyan University DSS for Student Financial Aid291

Appendix 8-A：Prototyping294

Appendix 8-B：Specific Tactics of Different Quality Control Approaches Aimed at Reducing the Risk of User-developed DSS296

PART 3：COLLABORATION，COMMUNICATION，AND ENTERPRISE SUPPORT SYSTEMS297

CHAPTER 9 Networked Decision Support：The Internet，intranets，and Collaborative Technologies299

9.1 Opening Vignette：J.P. Morgan Combines intranet and Notes300

9.2 Networked Decision Support302

9.3 The Internet：An Overview303

9.4 Intranets304

9.5 Data Access and Information Retrieval307

9.6 Supporting Communication308

9.7 Supporting Collaboration311

9.8 Electronic Teleconferencing317

9.9 Lotus Notes319

9.10 Netscape Communicator322

9.11 Electronic Commerce323

9.12 Electronic Data Interchange329

9.13 Ethical and Legal Issues on the Net331

9.14 Telecommuting （Working at Home）333

Case Application 9.1：Cushman and Wakefield Uses an intranet for Decision Support340

Case Application 9.2：General Mills Uses EDI341

Appendix 9-A：Fundamentals of the Internet344

CHAPTER 10 Group Decision Support Systems348

10.1 Opening Vignette：Quality Improvement Teams at the IRS of Manhattan348

10.2 Decision Making in Groups350

10.3 Group Decision Support Systems352

10.4 The Goal of GDSS and Its Technology Levels354

10.5 The Technology of GDSS356

10.6 The Decision （Electronic Meeting） Room358

10.7 GDSS Software360

10.8 Idea Generation365

10.9 Negotiation Support Systems366

10.10 The GDSS Meeting Process368

10.11 Constructing a GDSS and the Determinants of Its Success368

10.12 GDSS Research Challenges372

Case Application 10.1：Chevron Pipe Line Evaluates Critical Business Processes with a GDSS380

Appendix 10-A：Team Expert Choice （TEAMEC） for Windows：384

Professional Group Decision Support Software384

CHAPTER 11 Executive Information and Support Systems386

11.1 Opening Vignette：The Executive Information System at Hertz Corporation387

11.2 Executive Information Systems：Concepts and Definitions388

11.3 Executives’ Role and Their Information Needs390

11.4 Characteristics of EIS394

11.5 Comparing EIS and MIS398

11.6 Comparing and Integrating EIS and DSS399

11.7 Hardware and Software403

11.8 EIS，Data Access，Data Warehousing，OLAP，Multidimensional Analysis，Presentation，and the Web405

11.9 Enterprise EIS411

11.10 EIS Implementation：Success or Failure412

11.11 Including Soft Information in EIS415

11.12 The Future of EIS and Research Issues417

11.13 Organizational DSS420

11.14 The Architecture of ODSS421

11.15 Constructing an ODSS423

11.16 ODSS Example：The Enlisted Force Management System424

11.17 Implementing ODSS425

PART 4：FUNDAMENTALS OF EXPERT SYSTEMS AND INTELLIGENT SYSTEMS437

CHAPTER 12 Fundamentals of Expert Systems439

12.1 Opening Vignette：CATS-1 at General Electric439

12.2 Introduction440

12.3 History of Expert Systems441

12.4 Basic Concepts of Expert Systems443

12.5 Structure of Expert Systems446

12.6 The Human Element in Expert Systems449

12.7 How Expert Systems Work450

12.8 An Expert System at Work452

12.9 Problem Areas Addressed by Expert Systems454

12.10 Benefits of Expert Systems455

12.11 Problems and Limitations of Expert Systems460

12.12 Expert System Success Factors461

12.13 Types of Expert Systems462

12.14 Expert Systems and the Internet/intranets/Web465

Case Application 12.1：Gate Assignment Display System472

Case Application 12.2：Expert System in Construction474

Appendix 12-A：Systems Cited in Chapter476

Appendix 12-B：Classic Expert Systems477

Appendix 12-C：Typical Expert System Applications480

CHAPTER 13 Knowledge Acquisition and Validation482

13.1 Opening Vignette：American Express Improves Approval Selection with Machine Learning483

13.2 Knowledge Engineering483

13.3 Scope of Knowledge485

13.4 Difficulties in Knowledge Acquisition488

13.5 Methods of Knowledge Acquisition：An Overview491

13.6 Interviews493

13.7 Tracking Methods496

13.8 Observations and other Manual Methods497

13.9 Expert-driven Methods498

13.10 Repertory Grid Analysis500

13.11 Supporting the Knowledge Engineer502

13.12 Machine Learning：Rule Induction，Case-based Reasoning，Neural Computing，and Intelligent Agents505

13.13 Selecting an Appropriate Knowledge Acquisition Method510

13.14 Knowledge Acquisition from Multiple Experts512

13.15 Validation and Verification of the Knowledge Base514

13.16 Analyzing，Coding，Documenting，and Diagramming517

13.17 Numeric and Documented Knowledge Acquisition518

13.18 Knowledge Acquisition and the Internet/intranets519

13.19 Induction Table Example521

CHAPTER 14 Knowledge Representation533

14.1 Opening Vignette：Pitney Bowes Expert System Diagnoses Repair Problems and Saves Millions533

14.2 Introduction534

14.3 Representation in Logic and Other Schemas534

14.4 Semantic Networks537

14.5 Production Rules539

14.6 Frames542

14.7 Multiple Knowledge Representation547

14.8 Experimental Knowledge Representations549

14.9 Representing Uncertainty：An Overview550

CHAPTER 15 Inferences，Explanations，and Uncertainty558

15.1 Opening Vignette：Konica Automates a Help Desk with Case-based Reasoning558

15.2 Reasoning in Artificial Intelligence559

15.3 Inferencing with Rules：Forward and Backward Chaining561

15.4 The Inference Tree566

15.5 Inferencing with Frames568

15.6 Model-based Reasoning569

15.7 Case-based Reasoning571

15.8 Explanation and Metaknowledge578

15.9 Inferencing with Uncertainty582

15.10 Representing Uncertainty583

15.11 Probabilities and Related Approaches585

15.12 Theory of Certainty （Certainty Factors）586

15.13 Qualitative Reasoning589

Case Application 15.1：Compaq QuickSource：Using Case-based Reasoning for Problem Determination597

Appendix 15-A：ES Shells and Uncertainty601

CHAPTER 16 Building Expert Systems：Process and Tools602

16.1 Opening Vignette：The Logistics Management System （LMS） at IBM603

16.2 The Development Life Cycle604

16.3 Phase Ⅰ：Project Initialization604

16.4 Problem Definition and Needs Assessment605

16.5 Evaluation of Alternative Solutions606

16.6 Verification of an Expert System Approach607

16.7 Consideration of Managerial Issues608

16.8 Phase Ⅱ：System Analysis and Design609

16.9 Conceptual Design609

16.10 Development Strategy and Methodology609

16.11 Selecting an Expert611

16.12 Software Classification：Technology Levels612

16.13 Building Expert Systems with Tools616

16.14 Shells and Environments616

16.15 Software Selection617

16.16 Hardware Support621

16.17 Feasibility Study621

16.18 Cost-Benefit Analysis621

16.19 Phase Ⅲ：Rapid Prototyping and a Demonstration Prototype624

16.20 Phase Ⅳ：System Development627

16.21 Building the Knowledge Base628

16.22 Testing，Validating，Verifying，and Improving629

16.23 Phase Ⅴ：Implementation630

16.24 Phase Ⅵ：Postimplementation632

16.25 Organizing the Development Team634

16.26 The Future of Expert System Development Processes635

Case Application 16.1：State of Washington’s Department of Labor641

Appendix 16-A：How to Build a Knowledge Base （Rule-based） System644

PART 5 CUTTING-EDGE DECISION SUPPORT TECHNOLOGIES647

CHAPTER 17 Neural Computing：The Basics649

17.1 Opening Vignette：Maximizing the Value of the John Deere ＆ Co.Pension Fund650

17.2 Machine Learning：An Overview651

17.3 An Overview of Neural Computing652

17.4 The Biology Analogy653

17.5 Neural Network Fundamentals654

17.6 Neural Network Application Development661

17.7 Data Collection and Preparation663

17.8 Neural Network Architecture663

17.9 Neural Network Preparation664

17.10 Training the Network666

17.11 Learning Algorithms666

17.12 Backpropagation669

17.13 Testing670

17.14 Implementation670

17.15 Programming Neural Networks671

17.16 Neural Network Hardware671

17.17 Benefits of Neural Networks672

17.18 Limitations of Neural Networks673

17.19 Neural Networks and Expert Systems674

17.20 Neural Networks for Decision Support676

CHAPTER 18 Neural Computing Applications，Genetic Algorithms，Fuzzy Logic，and Hybrid Intelligent Systems685

18.1 Opening Vignette：Applying Neural Computing to Marketing685

18.2 Areas of ANN Applications：An Overview687

18.3 Using ANNs for Credit Approval688

18.4 Using ANNs for Bankruptcy Prediction693

18.5 Stock Market Prediction System with Modular Neural Networks695

18.6 Examples of Integrated ANNs and Expert Systems698

18.7 Genetic Algorithms700

18.8 Optimization Algorithms705

18.9 Fuzzy Logic：Theory and Applications706

18.10 Cross Fertilization Hybrids of Cutting-Edge Technologies709

18.11 Data Mining and Knowledge Discovery in Databases711

CHAPTER 19 Intelligent Agents and Creativity720

19.1 Opening Vignettes：Examples of Intelligent Agents720

19.2 Intelligent Agents：An Overview722

19.3 Characteristics of Intelligent Agents723

19.4 Why Intelligent Agents？725

19.5 Classification and Types of Agents727

19.6 Internet-based Software Agents732

19.7 Electronic Commerce Agents736

19.8 Other Agents，including Data Mining738

19.9 Multiple Agents and Distributed AI743

19.10 Software-Supported Creativity749

19.11 Managerial Issues754

CHAPTER 20 Implementing and Integrating Management Support Systems763

20.1 Opening Vignette：INCA Expert Systems for the SWIFT Network763

20.2 Implementation：An Overview764

20.3 The Major Issues of Implementation767

20.4 Implementation Strategies775

20.5 What Is System Integration and Why Integrate？777

20.6 Models of ES and DSS Integration778

20.7 Integrating EIS，DSS，and ES，and Global Integration782

20.8 Intelligent Modeling and Model Management786

20.9 Examples of Integrated Systems789

20.10 Problems and Issues in Integration797

Case Application 20.1：Urban Traffic Management803

CHAPTER 21 Organizational and Societal Impacts of Management Support Systems810

21.1 Opening Vignette：Police Department Uses Neural Networks to Assess Employees810

21.2 Introduction811

21.3 Overview of Impacts813

21.4 Organizational Structure and Related Areas814

21.5 MSS Support to Business Process Reengineering817

21.6 Personnel Management Issues820

21.7 Impact on Individuals821

21.8 Productivity，Quality，and Competitiveness822

21.9 Decision Making and the Manager’s Job823

21.10 Institutional Information Bases，Knowledge Bases，and Knowledge Management824

21.11 Issues of Legality，Privacy，and Ethics826

21.12 Intelligent Systems and Employment Levels830

21.13 Other Societal Impacts832

21.14 Managerial Implications and Social Responsibilities834

Case Application 21.1：Xerox Reengineers its $3 Billion Purchasing Processwith Graphical Modeling and Simulation842

APPENDIX A：Student Project：Frazee Paint，Inc.：An Example of a Student-developed DSS847

GLOSSARY853

INDEX873