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