PART 1：DECISION MAKING AND COMPUTERIZED SUPPORT

CHAPTER 1 Management Support Systems：An Overview

1.1 Opening Vignette：Decision Support at Roadway Package System

1.2 Managers and Decision Making

1.3 Managerial Decision Making and Informative System

1.4 Managers and Computerized Support

1.5 The Need for Computerized Decision Support and the Supporting Technologies

1.6 A Framework for Decision Support

1.7 The Concept of Decision Support Systems

1.8 Group Decision Support Systems

1.9 Executive Information （Support） Systems

1.10 Expert Systems

1.11 Artificial Neural Networks

1.12 Hybrid Support Systems

1.13 The Evolution and Attributes of Computerized Decision Aids

1.14 Plan of the Book

Case Application 1.1：Manufacturing and Marketing of Machine Devices

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

CHAPTER 2 Decision Making，Systems，Modeling，and Support

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

2.2 Introduction and Definitions

2.3 Systems

2.4 Models

2.5 The Modeling Process：A Preview

2.6 The Intelligence Phase

2.7 The Design Phase

2.8 The Choice Phase

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

2.10 The Implementation Phase

2.11 How Decisions Are Supported

2.12 Human Cognition and Decision Styles

2.13 The Decision Makers

PART 2：DECISION SUPPORT SYSTEMS

CHAPTER 3 Decision Support Systems：An Overview

3.1 Opening Vignette：Gotaas-Larsen Shipping Corp.

3.2 DSS Configurations

3.3 What Is a DSS？

3.4 Characteristics and Capabilities of DSS

3.5 Components of DSS

3.6 The Data Management Subsystem

3.7 The Model Management Subsystem

3.8 The Knowledge Management Subsystem

3.9 The User Interface （Dialog） Subsystem

3.10 The User

3.11 DSS Hardware

3.12 Distinguishing DSS from Management Science and MIS

3.13 Classifications of DSS

Case Application 3.1：Decision Support for Military Housing Managers

CHAPTER 4 Data Management：Warehousing，Access，and Visualization

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

4.2 Data Warehousing，Access，Analysis，and Visualization

4.3 The Nature and Sources of Data

4.4 Data Collection and Data Problems

4.5 The Internet and Commercial Database Services

4.6 Database Management Systems in DSS

4.7 Database Organization and Structure

4.8 Data Warehousing

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

4.10 Data Visualization and Multidimensionality

4.11 Intelligent Databases and Data Mining

4.12 The Big Picture

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

CHAPTER 5 Modeling and Analysis

5.1 Opening Vignette：Siemens Solar Industries Saves Millions by Simulation

5.2 Modeling for MSS

5.3 Static and Dynamic Models

5.4 Treating Certainty，Uncertainty，and Risk

5.5 Influence Diagrams

5.6 MSS Modeling in Spreadsheets

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

5.8 Optimization via Mathematical Programming

5.9 Heuristic Programming

5.10 Simulation

5.11 Multidimensional Modeling

5.12 Visual Spreadsheets

5.13 Financial and Planning Modeling

5.14 Visual Modeling and Simulation

5.15 Ready-made Quantitative Software Packages

5.16 Model Base Management

CHAPTER 6 Knowledge-based Decision Support and Artificial Intelligence

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

6.2 Concepts and Definitions

6.3 Artificial Intelligence versus Natural Intelligence

6.4 Knowledge in Artificial Intelligence

6.5 How Artificial Intelligence Differs from Conventional Computing

6.6 Does a Computer Really Think？

6.7 The Artificial Intelligence Field

6.8 Types of Knowledge-based Decision Support Systems

6.9 Intelligent Decision Support Systems

6.10 The Future of Artificial Intelligence

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

CHAPTER 7 User Interface and Decision Visualization Applications

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

7.2 User Interfaces：An Overview

7.3 Interface Modes （Styles）

7.4 Graphics

7.5 Multimedia and Hypermedia

7.6 Virtual Reality

7.7 Geographic Information Systems （GIS）

7.8 Natural Language Processing：An Overview

7.9 Natural Language Processing：Methods

7.10 Applications of Natural Language Processing and Software

7.11 Speech （Voice） Recognition and Understanding

7.12 Research on User Interfaces in MSS

Case Application 7.1：Nabisco Tracks Attendance Using Voice Technologies

CHAPTER 8 Constructing a Decision Support System and DSS Research

8.1 Opening Vignette：Hospital Healthcare Services Uses DSS

8.2 Introduction

8.3 Development Strategies

8.4 The DSS Development Process

8.5 The Development Process：Life Cycle versus Prototyping

8.6 Team-developed versus User-developed DSS

8.7 Team-developed DSS

8.8 End-user Computing and User-developed DSS

8.9 DSS Technology Levels and Tools

8.10 Selection of DSS Development Tools

8.11 Developing DSS

8.12 DSS Research Directions

8.13 The DSS of the Future

Case Application 8.1：Wesleyan University DSS for Student Financial Aid

Appendix 8-A：Prototyping

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

PART 3：COLLABORATION，COMMUNICATION，AND ENTERPRISE SUPPORT SYSTEMS

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

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

9.2 Networked Decision Support

9.3 The Internet：An Overview

9.4 Intranets

9.5 Data Access and Information Retrieval

9.6 Supporting Communication

9.7 Supporting Collaboration

9.8 Electronic Teleconferencing

9.9 Lotus Notes

9.10 Netscape Communicator

9.11 Electronic Commerce

9.12 Electronic Data Interchange

9.13 Ethical and Legal Issues on the Net

9.14 Telecommuting （Working at Home）

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

Case Application 9.2：General Mills Uses EDI

Appendix 9-A：Fundamentals of the Internet

CHAPTER 10 Group Decision Support Systems

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

10.2 Decision Making in Groups

10.3 Group Decision Support Systems

10.4 The Goal of GDSS and Its Technology Levels

10.5 The Technology of GDSS

10.6 The Decision （Electronic Meeting） Room

10.7 GDSS Software

10.8 Idea Generation

10.9 Negotiation Support Systems

10.10 The GDSS Meeting Process

10.11 Constructing a GDSS and the Determinants of Its Success

10.12 GDSS Research Challenges

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

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

Professional Group Decision Support Software

CHAPTER 11 Executive Information and Support Systems

11.1 Opening Vignette：The Executive Information System at Hertz Corporation

11.2 Executive Information Systems：Concepts and Definitions

11.3 Executives’ Role and Their Information Needs

11.4 Characteristics of EIS

11.5 Comparing EIS and MIS

11.6 Comparing and Integrating EIS and DSS

11.7 Hardware and Software

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

11.9 Enterprise EIS

11.10 EIS Implementation：Success or Failure

11.11 Including Soft Information in EIS

11.12 The Future of EIS and Research Issues

11.13 Organizational DSS

11.14 The Architecture of ODSS

11.15 Constructing an ODSS

11.16 ODSS Example：The Enlisted Force Management System

11.17 Implementing ODSS

PART 4：FUNDAMENTALS OF EXPERT SYSTEMS AND INTELLIGENT SYSTEMS

CHAPTER 12 Fundamentals of Expert Systems

12.1 Opening Vignette：CATS-1 at General Electric

12.2 Introduction

12.3 History of Expert Systems

12.4 Basic Concepts of Expert Systems

12.5 Structure of Expert Systems

12.6 The Human Element in Expert Systems

12.7 How Expert Systems Work

12.8 An Expert System at Work

12.9 Problem Areas Addressed by Expert Systems

12.10 Benefits of Expert Systems

12.11 Problems and Limitations of Expert Systems

12.12 Expert System Success Factors

12.13 Types of Expert Systems

12.14 Expert Systems and the Internet/intranets/Web

Case Application 12.1：Gate Assignment Display System

Case Application 12.2：Expert System in Construction

Appendix 12-A：Systems Cited in Chapter

Appendix 12-B：Classic Expert Systems

Appendix 12-C：Typical Expert System Applications

CHAPTER 13 Knowledge Acquisition and Validation

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

13.2 Knowledge Engineering

13.3 Scope of Knowledge

13.4 Difficulties in Knowledge Acquisition

13.5 Methods of Knowledge Acquisition：An Overview

13.6 Interviews

13.7 Tracking Methods

13.8 Observations and other Manual Methods

13.9 Expert-driven Methods

13.10 Repertory Grid Analysis

13.11 Supporting the Knowledge Engineer

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

13.13 Selecting an Appropriate Knowledge Acquisition Method

13.14 Knowledge Acquisition from Multiple Experts

13.15 Validation and Verification of the Knowledge Base

13.16 Analyzing，Coding，Documenting，and Diagramming

13.17 Numeric and Documented Knowledge Acquisition

13.18 Knowledge Acquisition and the Internet/intranets

13.19 Induction Table Example

CHAPTER 14 Knowledge Representation

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

14.2 Introduction

14.3 Representation in Logic and Other Schemas

14.4 Semantic Networks

14.5 Production Rules

14.6 Frames

14.7 Multiple Knowledge Representation

14.8 Experimental Knowledge Representations

14.9 Representing Uncertainty：An Overview

CHAPTER 15 Inferences，Explanations，and Uncertainty

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

15.2 Reasoning in Artificial Intelligence

15.3 Inferencing with Rules：Forward and Backward Chaining

15.4 The Inference Tree

15.5 Inferencing with Frames

15.6 Model-based Reasoning

15.7 Case-based Reasoning

15.8 Explanation and Metaknowledge

15.9 Inferencing with Uncertainty

15.10 Representing Uncertainty

15.11 Probabilities and Related Approaches

15.12 Theory of Certainty （Certainty Factors）

15.13 Qualitative Reasoning

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

Appendix 15-A：ES Shells and Uncertainty

CHAPTER 16 Building Expert Systems：Process and Tools

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

16.2 The Development Life Cycle

16.3 Phase Ⅰ：Project Initialization

16.4 Problem Definition and Needs Assessment

16.5 Evaluation of Alternative Solutions

16.6 Verification of an Expert System Approach

16.7 Consideration of Managerial Issues

16.8 Phase Ⅱ：System Analysis and Design

16.9 Conceptual Design

16.10 Development Strategy and Methodology

16.11 Selecting an Expert

16.12 Software Classification：Technology Levels

16.13 Building Expert Systems with Tools

16.14 Shells and Environments

16.15 Software Selection

16.16 Hardware Support

16.17 Feasibility Study

16.18 Cost-Benefit Analysis

16.19 Phase Ⅲ：Rapid Prototyping and a Demonstration Prototype

16.20 Phase Ⅳ：System Development

16.21 Building the Knowledge Base

16.22 Testing，Validating，Verifying，and Improving

16.23 Phase Ⅴ：Implementation

16.24 Phase Ⅵ：Postimplementation

16.25 Organizing the Development Team

16.26 The Future of Expert System Development Processes

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

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

PART 5 CUTTING-EDGE DECISION SUPPORT TECHNOLOGIES

CHAPTER 17 Neural Computing：The Basics

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

17.2 Machine Learning：An Overview

17.3 An Overview of Neural Computing

17.4 The Biology Analogy

17.5 Neural Network Fundamentals

17.6 Neural Network Application Development

17.7 Data Collection and Preparation

17.8 Neural Network Architecture

17.9 Neural Network Preparation

17.10 Training the Network

17.11 Learning Algorithms

17.12 Backpropagation

17.13 Testing

17.14 Implementation

17.15 Programming Neural Networks

17.16 Neural Network Hardware

17.17 Benefits of Neural Networks

17.18 Limitations of Neural Networks

17.19 Neural Networks and Expert Systems

17.20 Neural Networks for Decision Support

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

18.1 Opening Vignette：Applying Neural Computing to Marketing

18.2 Areas of ANN Applications：An Overview

18.3 Using ANNs for Credit Approval

18.4 Using ANNs for Bankruptcy Prediction

18.5 Stock Market Prediction System with Modular Neural Networks

18.6 Examples of Integrated ANNs and Expert Systems

18.7 Genetic Algorithms

18.8 Optimization Algorithms

18.9 Fuzzy Logic：Theory and Applications

18.10 Cross Fertilization Hybrids of Cutting-Edge Technologies

18.11 Data Mining and Knowledge Discovery in Databases

CHAPTER 19 Intelligent Agents and Creativity

19.1 Opening Vignettes：Examples of Intelligent Agents

19.2 Intelligent Agents：An Overview

19.3 Characteristics of Intelligent Agents

19.4 Why Intelligent Agents？

19.5 Classification and Types of Agents

19.6 Internet-based Software Agents

19.7 Electronic Commerce Agents

19.8 Other Agents，including Data Mining

19.9 Multiple Agents and Distributed AI

19.10 Software-Supported Creativity

19.11 Managerial Issues

CHAPTER 20 Implementing and Integrating Management Support Systems

20.1 Opening Vignette：INCA Expert Systems for the SWIFT Network

20.2 Implementation：An Overview

20.3 The Major Issues of Implementation

20.4 Implementation Strategies

20.5 What Is System Integration and Why Integrate？

20.6 Models of ES and DSS Integration

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

20.8 Intelligent Modeling and Model Management

20.9 Examples of Integrated Systems

20.10 Problems and Issues in Integration

Case Application 20.1：Urban Traffic Management

CHAPTER 21 Organizational and Societal Impacts of Management Support Systems

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

21.2 Introduction

21.3 Overview of Impacts

21.4 Organizational Structure and Related Areas

21.5 MSS Support to Business Process Reengineering

21.6 Personnel Management Issues

21.7 Impact on Individuals

21.8 Productivity，Quality，and Competitiveness

21.9 Decision Making and the Manager’s Job

21.10 Institutional Information Bases，Knowledge Bases，and Knowledge Management

21.11 Issues of Legality，Privacy，and Ethics

21.12 Intelligent Systems and Employment Levels

21.13 Other Societal Impacts

21.14 Managerial Implications and Social Responsibilities

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

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

GLOSSARY

INDEX