PART Ⅰ: DECISION MAKING AND COMPUTERIZED SUPPORT 1
CHAPTER 1 Management Support Systems: An Overview 2
1.1 Opening Vignette: Decision Support at Roadway Package System 3
1.2 Managers and Decision Making 4
1.3 Managerial Decision Making and Information Systems 6
1.4 Managers and Computerized Support 8
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 Support Systems 15
1.9 Executive Information （Support） Systems 16
1.10 Expert Systems and Intelligent Agents 17
1.11 Artificial Neural Networks 18
1.12 Knowledge Management Systems 19
1.13 Supporting Enterprise Resources Planning and Supply Chain Management 19
1.14 Hybrid Support Systems 20
1.15 The Evolution and Attributes of Computerized Decision Aids 21
1.16 Plan of This Book 24
Case Application 1.1 Manufacturing and Marketing of Machine Devices 29
CHAPTER 2 Decision Making, Systems, Modeling, and Support 30
2.1 Opening Vignette: How to Invest $10 Million 30
2.2 Decision Making: Introduction and Definitions 32
2.3 Systems 34
2.4 Models 38
2.5 A Preview of the Modeling Process 39
2.6 Decision Making: The Intelligence Phase 42
2.7 Decision Making: The Design Phase 45
2.8 Decision Making: The Choice Phase 57
2.9 Evaluation: Multiple Goals, Sensitivity Analysis, What-If, and Goal Seeking 60
2.10 Decision Making: The Implementation Phase 67
2.11 How Decisions Are Supported 68
2.12 Alternative Decision-Making Models 70
2.13 Personality Types, Gender, Human Cognition, and DecisionStyles 73
2.14 The Decision Makers 77
Case Application 2.1 Clay Process Planning at IMERYS: A ClassicalCase of Decision Making—Part 1 85
Case Application 2.2 Clay Process Planning at IMERYS: A ClassicalCase of Decision Making—Part 2 86
Case Application 2.3 Key Grip Uses the Analytical Hierarchy ProcessApproach to Select Film Projects 89
PART Ⅱ: DECISION SUPPORT SYSTEMS 93
CHAPTER 3 Decision Support Systems: An Overview 94
3.1 Opening Vignette: Evaluating the Quality of Journals inHong Kong 94
3.2 DSS Configurations 96
3.3 What Is a DSS? 96
3.4 Characteristics and Capabilities of DSS 98
3.5 Components of DSS 100
3.6 The Data Management Subsystem 101
3.7 The Model Management Subsystem 104
3.8 The Knowledge-Based Management Subsystem 107
3.9 The User Interface （Dialog） Subsystem 107
3.10 The User 109
3.11 DSS Hardware 110
3.12 Distinguishing DSS from Management Science and MIS 110
3.13 DSS Classifications 113
3.14 The Big Picture 120
Case Application 3.1 Decision Support for Military HousingManagers 125
CHAPTER 4 Data Warehousing, Access, Analysis, Mining, andVisualization 128
4.1 Opening Vignette: OBI Makes the Best Out of the DataWarehouse 128
4.2 Data Warehousing, Access, Analysis, and Visualization 130
4.3 The Nature and Sources of Data 131
4.4 Data Collection, Problems, and Quality 132
4.5 The Internet and Commercial Database Services 134
4.6 Database Management Systems in DSS 136
4.7 Database Organization and Structures 136
4.8 Data Warehousing 141
4.9 OLAP: Data Access, Querying, and Analysis 146
4.10 Data Mining 148
4.11 Data Visualization and Multidimensionality 152
4.12 Geographic Information Systems and Virtual Reality 154
4.13 Business Intelligence and the Web 158
4.14 The Big Picture 159
CHAPTER 5 Modeling and Analysis 165
5.1 Opening Vignette: DuPont Simulates Rail Transportation System and Avoids Costly Capital Expense 166
5.2 Modeling for MSS 167
5.3 Static and Dynamic Models 170
5.4 Treating Certainty, Uncertainty, and Risk 171
5.5 Influence Diagrams 172
5.6 MSS Modeling in Spreadsheets 176
5.7 Decision Analysis of a Few Alternatives （Decision Tables and Decision Trees） 178
5.8 Optimization via Mathematical Programming 182
5.9 Heuristic Programming 186
5.10 Simulation 189
5.11 Multidimensional Modeling —OLAP 192
5.12 Visual Interactive Modeling and Visual Interactive Simulation 198
5.13 Quantitative Software Packages—OLAP 201
5.14 Model Base Management 203
Case Application 5.1 Procter & Gamble （P&G） Blends Models, Judgment, and GIS to Restructure the Supply Chain 214
Case Application 5.2 Scott Homes Constructs an Expert Choice Multicriteria Model-Based DSS for Selecting a Mobile Home Supplier 217
Case Application 5.3 Clay Process Planning at IMERYS: A Classical Case of Decision Making 221
CHAPTER 6 Decision Support System Development 224
6.1 Opening Vignette: Osram Sylvania Thinks Small, Strategizes Big—Develops the InfoNet HR Portal System 224
6.2 Introduction to DSS Development 227
6.3 The Traditional System Development Life Cycle 229
6.4 Alternate Development Methodologies 235
6.5 Prototyping: The DSS Development Methodology 237
6.6 DSS Technology Levels and Tools 240
6.7 DSS Development Platforms 241
6.8 DSS Development Tool Selection 243
6.9 Team-Developed DSS 244
6.10 End User-Developed DSS 245
6.11 Developing DSS: Putting the System Together 248
6.12 DSS Research Directions and the DSS of the Future 249
Case Application 6.1 Clay Process at IMERYS: A Classical Case of Decision Making 254
PART Ⅲ: COLLABORATION, COMMUNICATION, ENTERPRISEDECISION SUPPORT SYSTEMS, AND KNOWLEDGEMANAGEMENT 259
CHAPTER 7 Collaborative Computing Technologies: Group SupportSystems 260
7.1 Opening Vignette: Chrysler SCORES with Groupware 261
7.2 Group Decision Making, Communication, and Collaboration 263
7.3 Communication Support 264
7.4 Collaboration Support: Computer-Supported CooperativeWork 266
7.5 Group Support Systems 271
7.6 Group Support Systems Technologies 275
7.7 GroupSystems 276
7.8 The GSS Meeting Process 278
7.9 Distance Learning 280
7.10 Creativity and Idea Generation 287
7.11 GSS and Collaborative Computing Issues and Research 292
Case Application 7.1 WELCOM Way to Share Ideas in a WorldForum 301
Case Application 7.2 Pfizer’s Effective and Safe CollaborativeComputing Pill 302
CHAPTER 8 Enterprise Decision Support Systems 304
8.1 Opening Vignette: Pizzeria Uno’s Enterprise System Makes theDifference 305
8.2 Enterprise Systems: Concepts and Definitions 306
8.3 The Evolution of Executive and Enterprise Information Systems 306
8.4 Executives’ Roles and Their Information Needs 309
8.5 Characteristics and Capabilities of Executive Support Systems 310
8.6 Comparing and Integrating EIS and DSS 314
8.7 EIS, Data Access, Data Warehousing, OLAP, MultidimensionalAnalysis, Presentation, and the Web 317
8.8 Including Soft Information in Enterprise Systems 320
8.9 Organizational DSS 321
8.10 Supply and Value Chains and Decision Support 322
8.11 Supply Chain Problems and Solutions 327
8.12 Computerized Systems: MRP, ERP and SCM 330
8.13 Frontline Decision Support Systems 335
8.14 The Future of Executives and Enterprise Support Systems 337
CHAPTER 9 Knowledge Management 344
9.1 Opening Vignette: Knowledge Management Gives Mitre a SharperEdge 344
9.2 Introduction to Knowledge Management 346
9.3 Knowledge 349
9.4 Organizational Learning and Organizational Memory 352
9.5 Knowledge Management 356
9.6 The Chief Knowledge Officer 365
9.7 Knowledge Management Development 366
9.8 Knowledge Management Methods, Technologies, and Tools 370
9.9 Knowledge Management Success 375
9.10 Knowledge Management and Artificial Intelligence 381
9.11 Electronic Document Management 382
9.12 Knowledge Management Issues and the Future 383
Case Application 9.1 Chrysler’s New Know-Mobiles 390
Case Application 9.2 Knowledge the Chevron Way 392
PART Ⅳ: FUNDAMENTALS OF INTELLIGENT SYSTEMS 395
CHAPTER 10 Knowledge-Based Decision Support: Artificial Intelligenceand Expert Systems 396
10.1 Opening Vignette: A Knowledge-Based DSS in a Chinese ChemicalPlant 397
10.2 Concepts and Definitions of Artificial Intelligence 398
10.3 Artificial Intelligence Versus Natural Intelligence 401
10.4 The Artificial Intelligence Field 402
10.5 Types of Knowledge-Based Decision Support Systems 406
10.6 Basic Concepts of Expert Systems 407
10.7 Structure of Expert Systems 410
10.8 The Human Element in Expert Systems 413
10.9 How Expert Systems Work 414
10.10 Example of an Expert System Consultation 415
10.11 Problem Areas Addressed by Expert Systems 417
10.12 Benefits of Expert Systems 420
10.13 Problems and Limitations of Expert Systems 423
10.14 Expert System Success Factors 424
10.15 Types of Expert Systems 425
10.16 Expert Systems and the Internet/Intranets/Web 428
Case Application 10.1 Gate Assignment Display System 436
CHAPTER 11 Knowledge Acquisition and Validation 437
11.1 Opening Vignette: American Express Improves Approval Selection with Machine Learning 438
11.2 Knowledge Engineering 438
11.3 Scope of Knowledge 441
11.4 Difficulties in Knowledge Acquisition 444
11.5 Methods of Knowledge Acquisition: An Overview 447
11.6 Interviews 449
11.7 Tracking Methods 451
11.8 Observations and Other Manual Methods 453
11.9 Expert-Driven Methods 454
11.10 Repertory Grid Analysis 456
11.11 Supporting the Knowledge Engineer 458
11.12 Machine Learning: Rule Induction, Case-Based Reasoning, Neural Computing, and Intelligent Agents 461
11.13 Selecting an Appropriate Knowledge Acquisition Method 467
11.14 Knowledge Acquisition from Multiple Experts 468
11.15 Validation and Verification of the Knowledge Base 470
11.16 Analyzing, Coding, Documenting, and Diagramming 472
11.17 Numeric and Documented Knowledge Acquisition 473
11.18 Knowledge Acquisition and the Internet/Intranets 474
11.19 Induction Table Example 476
CHAPTER 12 Knowledge Representation 484
12.1 Opening Vignette: An Intelligent System Manages Ford’s Assembly Plants 484
12.2 Introduction 485
12.3 Representation in Logic and Other Schemas 485
12.4 Semantic Networks 490
12.5 Production Rules 491
12.6 Frames 494
12.7 Multiple Knowledge Representation 499
12.8 Experimental Knowledge Representations 501
12.9 Representing Uncertainty: An Overview 503
CHAPTER 13 Inference Techniques 509
13.1 Opening Vignette: Konica Automates a Help Desk with Case-Based Reasoning 509
13.2 Reasoning in Artificial Intelligence 510
13.3 Inferencing with Rules: Forward and Backward Chaining 512
13.4 The Inference Tree 517
13.5 Inferencing with Frames 519
13.6 Model-Based Reasoning 520
13.7 Case-Based Reasoning 522
13.8 Explanation and Metaknowledge 530
13.9 Inferencing with Uncertainty 534
13.10 Representing Uncertainty 535
13.11 Probabilities and Related Approaches 537
13.12 Theory of Certainty （Certainty Factors） 538
13.13 Approximate Reasoning Using Fuzzy Logic 541
Case Application 13.1 Compaq QuickSource: Using Case-Based Reasoning for Problem Determination 548
CHAPTER 14 Intelligent Systems Development 550
14.1 Opening Vignette: Development of an Expert System to Detect Insider Stock Trades 550
14.2 Prototyping: The Expert System Development Life Cycle 552
14.3 Phase Ⅰ: Project Initialization 555
14.4 Phase Ⅱ: System Analysis and Design 564
14.5 Software Classification: ES Technology Levels 567
14.6 Building Expert Systems with Tools 571
14.7 Shells and Environments 571
14.8 Software Selection 573
14.9 Hardware 576
14.10 Phase Ⅲ: Rapid Prototyping and a DemonstrationPrototype 576
14.11 Phase Ⅳ: System Development 578
14.12 Phase Ⅴ:Implementation 583
14.13 Phase Ⅵ: Postimplementation 585
14.14 The Future of Expert System Development Processes 589
Appendix 14-A Developing a Small （Rule-Based） Expert System for Wine Selection 597
Case Application 14.1 The Development of the Logistics Management System （LMS） at IBM 598
PART Ⅴ: ADVANCED INTELLIGENT SYSTEMS 601
CHAPTER 15 Neural Computing: The Basics 602
15.1 Opening Vignette: Household Financial’s Vision Speeds Loan Approvals with Neural Networks 603
15.2 Machine Learning 605
15.3 Neural Computing 606
15.4 The Biology Analogy 607
15.5 Neural Network Fundamentals 609
15.6 Neural Network Application Development 614
15.7 Data Collection and Preparation 616
15.8 Neural Network Architecture 616
15.9 Neural Network Preparation 619
15.10 Training the Network 619
15.11 Learning Algorithms 620
15.12 Backpropagation 622
15.13 Testing 623
15.14 Implementation 624
15.15 Neural Network Software 625
15.16 Neural Network Hardware 626
15.17 Neural Network Development Examples 627
15.18 The Self-Organizing Map: An Alternative Neural Network Architecture 632
15.19 Benefits of Neural Networks 634
15.20 Limitations of Neural Networks 636
15.21 Neural Networks and Expert Systems 636
15.22 Neural Networks for Decision Support 638
Case Application 15.1 Maximizing the Value of the John Deere & Company Pension Fund 646
CHAPTER 16 Neural Computing Applications, and Advanced Artifiicial Intelligent Systems and Applications 648
16.1 Opening Vignette: New York City’s Public Housing Authority Gets Warm and Fuzzy 649
16.2 Overview of ANN Application Areas 650
16.3 Credit Approval with Neural Networks 651
16.4 Bankruptcy Prediction with Neural Networks 656
16.5 Stock Market Prediction System with Modular Neural Networks 658
16.6 Integrated ANNs and Expert Systems 661
16.7 Genetic Algorithms 664
16.8 Optimization Algorithms 671
16.9 Fuzzy Logic 672
16.10 Qualitative Reasoning 676
16.11 Intelligent Systems Integration 678
16.12 Data Mining and Knowledge Discovery in Databases 681
CHAPTER 17 Intelligent Software Agents and Creativity 688
17.1 Opening Vignettes: Examples of Intelligent Agents 688
17.2 Intelligent Agents: An Overview 690
17.3 Characteristics of Agents 692
17.4 Single Task 693
17.5 Why Intelligent Agents? 694
17.6 Classification and Types of Agents 696
17.7 Internet-Based Software Agents 699
17.8 Electronic Commerce Agents 703
17.9 Other Agents, Including Data Mining, User Interface, and Interactive, Believable Agents 708
17.10 Distributed AI, Multiagents, and Communities of Agents 714
17.11 DSS Agents 719
17.12 Managerial Issues 721
PART Ⅵ: IMPLEMENTATION, INTEGRATION, AND IMPACTS 727
CHAPTER 18 Implementing and Integrating Management SupportSystems 728
18.1 Opening Vignette: INCA Expert Systems for the SWIFTNetwork 728
18.2 Implementation: An Overview 730
18.3 The Major Issues of Implementation 733
18.4 Implementation Strategies 741
18.5 What Is System Integration and Why Integrate? 744
18.6 Generic Models of MSS Integration 746
18.7 Models of ES and DSS Integration 748
18.8 Integrating EIS, DSS, and ES, and Global Integration 751
18.9 Intelligent DSS 755
18.10 Intelligent Modeling and Model Management 757
18.11 Examples of Integrated Systems 760
18.12 Problems and Issues in Integration 768
Case Application 18.1 Urban Traffic Management 774
CHAPTER 19 Impacts of Management Support Systems 776
19.1 Opening Vignette: Police Department Uses Neural Networks to Assess Employees 776
19.2 Introduction 777
19.3 Overview of Impacts 778
19.4 Organizational Structure and Related Areas 780
19.5 MSS Support to Business Process Reengineering 782
19.6 Personnel Management Issues 786
19.7 Impact on Individuals 787
19.8 Impacts on Productivity, Quality, and Competitiveness 788
19.9 Decision Making and the Manager’s Job 789
19.10 Issues of Legality, Privacy, and Ethics 790
19.11 Intelligent Systems and Employment Levels 793
19.12 Internet Communities 795
19.13 Other Societal Impacts 796
19.14 Managerial Implications and Social Responsibilities 798
19.15 The Future of Management Support Systems 799
Case Application 19.1 Xerox Reengineers Its $3 Billion Purchasing Process with Graphical Modeling and Simulation 806
Glossary 807
References 821
Index 851