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PART 1：DECISION MAKING AND COMPUTERIZED SUPPORT 1

CHAPTER 1 Management Support Systems：An Overview 3

1.1 Opening Vignette：Decision Support at Roadway Package System 3

1.2 Managers and Decision Making 5

1.3 Managerial Decision Making and Informative System 6

1.4 Managers and Computerized Support 9

1.5 The Need for Computerized Decision Support and the Supporting Technologies 9

1.6 A Framework for Decision Support 11

1.7 The Concept of Decision Support Systems 13

1.8 Group Decision Support Systems 15

1.9 Executive Information （Support） Systems 17

1.10 Expert Systems 17

1.11 Artificial Neural Networks 18

1.12 Hybrid Support Systems 19

1.13 The Evolution and Attributes of Computerized Decision Aids 20

1.14 Plan of the Book 23

Case Application 1.1：Manufacturing and Marketing of Machine Devices 28

Appendix 1-A：Computer-Based Information Systems in a Personnel Department 30

CHAPTER 2 Decision Making，Systems，Modeling，and Support 32

2.1 Opening Vignette：How to Invest $1，000，000 32

2.2 Introduction and Definitions 33

2.3 Systems 34

2.4 Models 38

2.5 The Modeling Process：A Preview 39

2.6 The Intelligence Phase 42

2.7 The Design Phase 43

2.8 The Choice Phase 52

2.9 Evaluation：Multiple Goals，Sensitivity Analysis，What-If，and Goal Seeking 55

2.10 The Implementation Phase 59

2.11 How Decisions Are Supported 60

2.12 Human Cognition and Decision Styles 62

2.13 The Decision Makers 63

PART 2：DECISION SUPPORT SYSTEMS 71

CHAPTER 3 Decision Support Systems：An Overview 73

3.1 Opening Vignette：Gotaas-Larsen Shipping Corp. 73

3.2 DSS Configurations 74

3.3 What Is a DSS？ 75

3.4 Characteristics and Capabilities of DSS 77

3.5 Components of DSS 78

3.6 The Data Management Subsystem 79

3.7 The Model Management Subsystem 82

3.8 The Knowledge Management Subsystem 85

3.9 The User Interface （Dialog） Subsystem 85

3.10 The User 87

3.11 DSS Hardware 88

3.12 Distinguishing DSS from Management Science and MIS 88

3.13 Classifications of DSS 90

Case Application 3.1：Decision Support for Military Housing Managers 104

CHAPTER 4 Data Management：Warehousing，Access，and Visualization 108

4.1 Opening Vignette：Data Warehousing and DSS at Group Health Cooperative 108

4.2 Data Warehousing，Access，Analysis，and Visualization 110

4.3 The Nature and Sources of Data 111

4.4 Data Collection and Data Problems 113

4.5 The Internet and Commercial Database Services 113

4.6 Database Management Systems in DSS 116

4.7 Database Organization and Structure 117

4.8 Data Warehousing 121

4.9 OLAP：Data Access and Mining，Querying，and Analysis 125

4.10 Data Visualization and Multidimensionality 130

4.11 Intelligent Databases and Data Mining 132

4.12 The Big Picture 135

Case Application 4.1：Data Warehousing at the Canadian Imperial Bank of Commerce 141

CHAPTER 5 Modeling and Analysis 145

5.1 Opening Vignette：Siemens Solar Industries Saves Millions by Simulation 146

5.2 Modeling for MSS 147

5.3 Static and Dynamic Models 149

5.4 Treating Certainty，Uncertainty，and Risk 150

5.5 Influence Diagrams 150

5.6 MSS Modeling in Spreadsheets 152

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

5.8 Optimization via Mathematical Programming 158

5.9 Heuristic Programming 161

5.10 Simulation 163

5.11 Multidimensional Modeling 167

5.12 Visual Spreadsheets 170

5.13 Financial and Planning Modeling 171

5.14 Visual Modeling and Simulation 173

5.15 Ready-made Quantitative Software Packages 178

5.16 Model Base Management 180

CHAPTER 6 Knowledge-based Decision Support and Artificial Intelligence 197

6.1 Opening Vignette：A Knowledge-based DSS in a Chinese Chemical Plant 197

6.2 Concepts and Definitions 199

6.3 Artificial Intelligence versus Natural Intelligence 201

6.4 Knowledge in Artificial Intelligence 202

6.5 How Artificial Intelligence Differs from Conventional Computing 204

6.6 Does a Computer Really Think？ 205

6.7 The Artificial Intelligence Field 206

6.8 Types of Knowledge-based Decision Support Systems 214

6.9 Intelligent Decision Support Systems 215

6.10 The Future of Artificial Intelligence 218

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

CHAPTER 7 User Interface and Decision Visualization Applications 227

7.1 Opening Vignette：Geographic Information System at Dallas Area Rapid Transit 227

7.2 User Interfaces：An Overview 228

7.3 Interface Modes （Styles） 231

7.4 Graphics 233

7.5 Multimedia and Hypermedia 235

7.6 Virtual Reality 240

7.7 Geographic Information Systems （GIS） 243

7.8 Natural Language Processing：An Overview 247

7.9 Natural Language Processing：Methods 248

7.10 Applications of Natural Language Processing and Software 251

7.11 Speech （Voice） Recognition and Understanding 252

7.12 Research on User Interfaces in MSS 257

Case Application 7.1：Nabisco Tracks Attendance Using Voice Technologies 263

CHAPTER 8 Constructing a Decision Support System and DSS Research 266

8.1 Opening Vignette：Hospital Healthcare Services Uses DSS 266

8.2 Introduction 267

8.3 Development Strategies 268

8.4 The DSS Development Process 269

8.5 The Development Process：Life Cycle versus Prototyping 272

8.6 Team-developed versus User-developed DSS 274

8.7 Team-developed DSS 275

8.8 End-user Computing and User-developed DSS 276

8.9 DSS Technology Levels and Tools 279

8.10 Selection of DSS Development Tools 281

8.11 Developing DSS 283

8.12 DSS Research Directions 283

8.13 The DSS of the Future 286

Case Application 8.1：Wesleyan University DSS for Student Financial Aid 291

Appendix 8-A：Prototyping 294

Appendix 8-B：Specific Tactics of Different Quality Control Approaches Aimed at Reducing the Risk of User-developed DSS 296

PART 3：COLLABORATION，COMMUNICATION，AND ENTERPRISE SUPPORT SYSTEMS 297

CHAPTER 9 Networked Decision Support：The Internet，intranets，and Collaborative Technologies 299

9.1 Opening Vignette：J.P. Morgan Combines intranet and Notes 300

9.2 Networked Decision Support 302

9.3 The Internet：An Overview 303

9.4 Intranets 304

9.5 Data Access and Information Retrieval 307

9.6 Supporting Communication 308

9.7 Supporting Collaboration 311

9.8 Electronic Teleconferencing 317

9.9 Lotus Notes 319

9.10 Netscape Communicator 322

9.11 Electronic Commerce 323

9.12 Electronic Data Interchange 329

9.13 Ethical and Legal Issues on the Net 331

9.14 Telecommuting （Working at Home） 333

Case Application 9.1：Cushman and Wakefield Uses an intranet for Decision Support 340

Case Application 9.2：General Mills Uses EDI 341

Appendix 9-A：Fundamentals of the Internet 344

CHAPTER 10 Group Decision Support Systems 348

10.1 Opening Vignette：Quality Improvement Teams at the IRS of Manhattan 348

10.2 Decision Making in Groups 350

10.3 Group Decision Support Systems 352

10.4 The Goal of GDSS and Its Technology Levels 354

10.5 The Technology of GDSS 356

10.6 The Decision （Electronic Meeting） Room 358

10.7 GDSS Software 360

10.8 Idea Generation 365

10.9 Negotiation Support Systems 366

10.10 The GDSS Meeting Process 368

10.11 Constructing a GDSS and the Determinants of Its Success 368

10.12 GDSS Research Challenges 372

Case Application 10.1：Chevron Pipe Line Evaluates Critical Business Processes with a GDSS 380

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

Professional Group Decision Support Software 384

CHAPTER 11 Executive Information and Support Systems 386

11.1 Opening Vignette：The Executive Information System at Hertz Corporation 387

11.2 Executive Information Systems：Concepts and Definitions 388

11.3 Executives’ Role and Their Information Needs 390

11.4 Characteristics of EIS 394

11.5 Comparing EIS and MIS 398

11.6 Comparing and Integrating EIS and DSS 399

11.7 Hardware and Software 403

11.8 EIS，Data Access，Data Warehousing，OLAP，Multidimensional Analysis，Presentation，and the Web 405

11.9 Enterprise EIS 411

11.10 EIS Implementation：Success or Failure 412

11.11 Including Soft Information in EIS 415

11.12 The Future of EIS and Research Issues 417

11.13 Organizational DSS 420

11.14 The Architecture of ODSS 421

11.15 Constructing an ODSS 423

11.16 ODSS Example：The Enlisted Force Management System 424

11.17 Implementing ODSS 425

PART 4：FUNDAMENTALS OF EXPERT SYSTEMS AND INTELLIGENT SYSTEMS 437

CHAPTER 12 Fundamentals of Expert Systems 439

12.1 Opening Vignette：CATS-1 at General Electric 439

12.2 Introduction 440

12.3 History of Expert Systems 441

12.4 Basic Concepts of Expert Systems 443

12.5 Structure of Expert Systems 446

12.6 The Human Element in Expert Systems 449

12.7 How Expert Systems Work 450

12.8 An Expert System at Work 452

12.9 Problem Areas Addressed by Expert Systems 454

12.10 Benefits of Expert Systems 455

12.11 Problems and Limitations of Expert Systems 460

12.12 Expert System Success Factors 461

12.13 Types of Expert Systems 462

12.14 Expert Systems and the Internet/intranets/Web 465

Case Application 12.1：Gate Assignment Display System 472

Case Application 12.2：Expert System in Construction 474

Appendix 12-A：Systems Cited in Chapter 476

Appendix 12-B：Classic Expert Systems 477

Appendix 12-C：Typical Expert System Applications 480

CHAPTER 13 Knowledge Acquisition and Validation 482

13.1 Opening Vignette：American Express Improves Approval Selection with Machine Learning 483

13.2 Knowledge Engineering 483

13.3 Scope of Knowledge 485

13.4 Difficulties in Knowledge Acquisition 488

13.5 Methods of Knowledge Acquisition：An Overview 491

13.6 Interviews 493

13.7 Tracking Methods 496

13.8 Observations and other Manual Methods 497

13.9 Expert-driven Methods 498

13.10 Repertory Grid Analysis 500

13.11 Supporting the Knowledge Engineer 502

13.12 Machine Learning：Rule Induction，Case-based Reasoning，Neural Computing，and Intelligent Agents 505

13.13 Selecting an Appropriate Knowledge Acquisition Method 510

13.14 Knowledge Acquisition from Multiple Experts 512

13.15 Validation and Verification of the Knowledge Base 514

13.16 Analyzing，Coding，Documenting，and Diagramming 517

13.17 Numeric and Documented Knowledge Acquisition 518

13.18 Knowledge Acquisition and the Internet/intranets 519

13.19 Induction Table Example 521

CHAPTER 14 Knowledge Representation 533

14.1 Opening Vignette：Pitney Bowes Expert System Diagnoses Repair Problems and Saves Millions 533

14.2 Introduction 534

14.3 Representation in Logic and Other Schemas 534

14.4 Semantic Networks 537

14.5 Production Rules 539

14.6 Frames 542

14.7 Multiple Knowledge Representation 547

14.8 Experimental Knowledge Representations 549

14.9 Representing Uncertainty：An Overview 550

CHAPTER 15 Inferences，Explanations，and Uncertainty 558

15.1 Opening Vignette：Konica Automates a Help Desk with Case-based Reasoning 558

15.2 Reasoning in Artificial Intelligence 559

15.3 Inferencing with Rules：Forward and Backward Chaining 561

15.4 The Inference Tree 566

15.5 Inferencing with Frames 568

15.6 Model-based Reasoning 569

15.7 Case-based Reasoning 571

15.8 Explanation and Metaknowledge 578

15.9 Inferencing with Uncertainty 582

15.10 Representing Uncertainty 583

15.11 Probabilities and Related Approaches 585

15.12 Theory of Certainty （Certainty Factors） 586

15.13 Qualitative Reasoning 589

Case Application 15.1：Compaq QuickSource：Using Case-based Reasoning for Problem Determination 597

Appendix 15-A：ES Shells and Uncertainty 601

CHAPTER 16 Building Expert Systems：Process and Tools 602

16.1 Opening Vignette：The Logistics Management System （LMS） at IBM 603

16.2 The Development Life Cycle 604

16.3 Phase Ⅰ：Project Initialization 604

16.4 Problem Definition and Needs Assessment 605

16.5 Evaluation of Alternative Solutions 606

16.6 Verification of an Expert System Approach 607

16.7 Consideration of Managerial Issues 608

16.8 Phase Ⅱ：System Analysis and Design 609

16.9 Conceptual Design 609

16.10 Development Strategy and Methodology 609

16.11 Selecting an Expert 611

16.12 Software Classification：Technology Levels 612

16.13 Building Expert Systems with Tools 616

16.14 Shells and Environments 616

16.15 Software Selection 617

16.16 Hardware Support 621

16.17 Feasibility Study 621

16.18 Cost-Benefit Analysis 621

16.19 Phase Ⅲ：Rapid Prototyping and a Demonstration Prototype 624

16.20 Phase Ⅳ：System Development 627

16.21 Building the Knowledge Base 628

16.22 Testing，Validating，Verifying，and Improving 629

16.23 Phase Ⅴ：Implementation 630

16.24 Phase Ⅵ：Postimplementation 632

16.25 Organizing the Development Team 634

16.26 The Future of Expert System Development Processes 635

Case Application 16.1：State of Washington’s Department of Labor 641

Appendix 16-A：How to Build a Knowledge Base （Rule-based） System 644

PART 5 CUTTING-EDGE DECISION SUPPORT TECHNOLOGIES 647

CHAPTER 17 Neural Computing：The Basics 649

17.1 Opening Vignette：Maximizing the Value of the John Deere ＆ Co.Pension Fund 650

17.2 Machine Learning：An Overview 651

17.3 An Overview of Neural Computing 652

17.4 The Biology Analogy 653

17.5 Neural Network Fundamentals 654

17.6 Neural Network Application Development 661

17.7 Data Collection and Preparation 663

17.8 Neural Network Architecture 663

17.9 Neural Network Preparation 664

17.10 Training the Network 666

17.11 Learning Algorithms 666

17.12 Backpropagation 669

17.13 Testing 670

17.14 Implementation 670

17.15 Programming Neural Networks 671

17.16 Neural Network Hardware 671

17.17 Benefits of Neural Networks 672

17.18 Limitations of Neural Networks 673

17.19 Neural Networks and Expert Systems 674

17.20 Neural Networks for Decision Support 676

CHAPTER 18 Neural Computing Applications，Genetic Algorithms，Fuzzy Logic，and Hybrid Intelligent Systems 685

18.1 Opening Vignette：Applying Neural Computing to Marketing 685

18.2 Areas of ANN Applications：An Overview 687

18.3 Using ANNs for Credit Approval 688

18.4 Using ANNs for Bankruptcy Prediction 693

18.5 Stock Market Prediction System with Modular Neural Networks 695

18.6 Examples of Integrated ANNs and Expert Systems 698

18.7 Genetic Algorithms 700

18.8 Optimization Algorithms 705

18.9 Fuzzy Logic：Theory and Applications 706

18.10 Cross Fertilization Hybrids of Cutting-Edge Technologies 709

18.11 Data Mining and Knowledge Discovery in Databases 711

CHAPTER 19 Intelligent Agents and Creativity 720

19.1 Opening Vignettes：Examples of Intelligent Agents 720

19.2 Intelligent Agents：An Overview 722

19.3 Characteristics of Intelligent Agents 723

19.4 Why Intelligent Agents？ 725

19.5 Classification and Types of Agents 727

19.6 Internet-based Software Agents 732

19.7 Electronic Commerce Agents 736

19.8 Other Agents，including Data Mining 738

19.9 Multiple Agents and Distributed AI 743

19.10 Software-Supported Creativity 749

19.11 Managerial Issues 754

CHAPTER 20 Implementing and Integrating Management Support Systems 763

20.1 Opening Vignette：INCA Expert Systems for the SWIFT Network 763

20.2 Implementation：An Overview 764

20.3 The Major Issues of Implementation 767

20.4 Implementation Strategies 775

20.5 What Is System Integration and Why Integrate？ 777

20.6 Models of ES and DSS Integration 778

20.7 Integrating EIS，DSS，and ES，and Global Integration 782

20.8 Intelligent Modeling and Model Management 786

20.9 Examples of Integrated Systems 789

20.10 Problems and Issues in Integration 797

Case Application 20.1：Urban Traffic Management 803

CHAPTER 21 Organizational and Societal Impacts of Management Support Systems 810

21.1 Opening Vignette：Police Department Uses Neural Networks to Assess Employees 810

21.2 Introduction 811

21.3 Overview of Impacts 813

21.4 Organizational Structure and Related Areas 814

21.5 MSS Support to Business Process Reengineering 817

21.6 Personnel Management Issues 820

21.7 Impact on Individuals 821

21.8 Productivity，Quality，and Competitiveness 822

21.9 Decision Making and the Manager’s Job 823

21.10 Institutional Information Bases，Knowledge Bases，and Knowledge Management 824

21.11 Issues of Legality，Privacy，and Ethics 826

21.12 Intelligent Systems and Employment Levels 830

21.13 Other Societal Impacts 832

21.14 Managerial Implications and Social Responsibilities 834

Case Application 21.1：Xerox Reengineers its $3 Billion Purchasing Processwith Graphical Modeling and Simulation 842

APPENDIX A：Student Project：Frazee Paint，Inc.：An Example of a Student-developed DSS 847

GLOSSARY 853

INDEX 873