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Handbook of Computational Intelligence in Manufacturing and Production Management

Book Description

"During the last two decades, computer and information technologies have forced great changes in the ways businesses manage operations in meeting the desired quality of products and services, customer demands, competition, and other challenges.

The Handbook of Computational Intelligence in Manufacturing and Production Management focuses on new developments in computational intelligence in areas such as forecasting, scheduling, production planning, inventory control, and aggregate planning, among others. This comprehensive collection of research provides cutting-edge knowledge on information technology developments for both researchers and professionals in fields such as operations and production management, Web engineering, artificial intelligence, and information resources management."

Table of Contents

  1. Copyright
  2. Foreword
  3. Preface
    1. ORGANIZATION OF THE BOOK
    2. REFERENCES
  4. Acknowledgment
  5. List of Reviewers
  6. I. Computational Intelligence Methodologies
    1. I. Heuristics and Metaheuristics for Solving Scheduling Problems
      1. ABSTRACT
      2. INTRODUCTION
      3. OBJECTIVES OF SCHEDULING
      4. FORMULATION OF FLOWSHOP AND JOBSHOP SCHEDULING PROBLEM
      5. OVERVIEW OF SCHEDULING ON MAKESPAN CRITERION
      6. OVERVIEW OF SCHEDULING ON TOTAL FLOWTIME CRITERION
      7. SCHEDULING USING GENETIC ALGORITHMS
      8. SCHEDULING USING SIMULATED ANNEALING
      9. SCHEDULING USING ARTIFICIAL IMMUNE SYSTEMS
        1. The vertebrate Immune System
        2. Artificial Immune System
        3. Application of AIS to Scheduling Problems
      10. DISCUSSION
      11. CONCLUSION
      12. FUTURE RESEARCH DIRECTIONS
      13. REFERENCES
      14. ADDITIONAL READINGS
    2. II. Solving Machine Loading Problem of FMS: An Artificial Intelligence (AI) Based Random Search Optimization Approach
      1. ABSTRACT
      2. MACHINE LOADING PROBLEM IN FMS
        1. Historical Background of the Loading Problem
        2. Mathematical Modeling
          1. Subscripts
          2. Parameters
          3. Decision Variables
          4. Objective Function and Constraints
      3. AI BASED RANDOM SEARCH ALGORITHMS
        1. Genetic Algorithm
        2. Ant Colony Optimization
        3. Simulated Annealing
        4. Artificial Immune System
        5. Tabu Search
      4. TEST BED
      5. CONCLUSION
      6. FUTURE RESEARCH DIRECTIONS
      7. REFERENCES
      8. ADDITIONAL READINGS
      9. APPENDIX
        1. 1. Genetic Algorithm
          1. Step 1: Initialize the Control Parameters
          2. Step 2: Initialization
          3. Step 3: Evaluation
          4. Step 4: Crossover
          5. Step 5: Mutation
          6. Step 6: Evaluation and Selection
          7. Step 7: Stopping Criteria and Results
        2. 2. Ant Colony Optimization
          1. Step 1: Initialize the Control Parameters
          2. Step 2: Initialization
          3. Step 3
          4. Step 4
          5. Step 5: Node selection
          6. Step 6
          7. Step 7: Updating
          8. Step 8: Output:
        3. 3. Artificial Immune Algorithm
          1. Step 1: Initialization
          2. Step 2: Fitness Evaluation
          3. Step 3: Proliferation
          4. Step 4: Hypermutation
          5. Step 5: Selection
        4. 4. Simulated Annealing
          1. Step 1: Initialization
          2. Step 2: Exploration
          3. Step 3: Exploitation
        5. 5. Tabu Search
          1. Step 1: Generation of Initial Feasible Solution
          2. Step 2: Neighborhood Generation
          3. Step 3: Aspiration Level
          4. Step 4: Stopping Criteria
    3. III. Computational Intelligence in the Financial Functions of Industrial Firms
      1. ABSTRACT
      2. INTRODUCTION
      3. LITERATURE REVIEW
      4. THE GENERIC MODEL
        1. The Internal RDBMS Systems
        2. The External Financial Information systems
      5. CORPORATE FINANCIAL INFORMATION SYSTEM: PROCESSING BOX AND QUANTITATIVE METHODS OF ANALYSIS
      6. CONCLUSION AND FUTURE TRENDS: MANAGERIAL DECISION
      7. FUTURE RESEARCH DIRECTIONS
      8. REFERENCES
      9. ADDITIONAL READING
    4. IV. Fuzzy Sets and Analytical Hierarchical Process for Manufacturing Process Choice
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. THE AHP AND FUZZY SETS: GENERAL DESCRIPTIONS
      5. THE PROPOSED AHP MODEL
        1. Impact of Planning Horizon (Level 1)
        2. Decision-Makers (Level 2)
        3. Objectives and Criteria (Levels 3 and 4)
          1. Product Cost (C)
          2. Product Quality (Q)
          3. Responsiveness (R)
          4. Operators Skills (O)
          5. Inventory (I)
        4. Alternatives (Level 5)
      6. FUZZY SETS AND THE AHP MODEL
        1. The Fuzzy Preference Scale
      7. A CASE STUDY
        1. Synthesis of Criteria Using Mean Values
        2. Sensitivity Analysis Inside the Fuzzy Domain
          1. Process Choice Assessment
          2. Process Choice with Respect to Responsiveness
      8. CONCLUSION
      9. FUTURE RESEARCH DIRECTIONS
      10. REFERENCES
      11. ADDITIONAL READING
    5. V. Computational Intelligence Approach on a Deterministic Production–Inventory Control Model with Shortages
      1. ABSTRACT
      2. INTRODUCTION
      3. MODEL DESCRIPTION
        1. Assumptions
        2. Notations
      4. MATHEMATICAL MODEL FORMULATION AND SOLUTION
        1. Case 1: t1 ≤ μ
          1. Optimization Problem
        2. Case 2: [t1 ≥ μ]
          1. Optimization Problem
      5. COMPUTATIONAL INTELLIGENCE APPROACH
        1. Genetic Algorithms
          1. Why Genetic Algorithms in Production-Inventory Control Model?
          2. Development of Genetic Algorithms
          3. Parameters of Genetic Algorithm
          4. Chromosome Representation
          5. Initialization
          6. Evaluation Function
          7. Selection
          8. Crossover Operation
            1. Different Steps of Crossover Operation
          9. Mutation Operation
          10. Elitism
          11. Termination
        2. Simulated Annealing
          1. Stepwise Procedure of Simulated Annealing
      6. NUMERICAL ILLUSTRATION
      7. SENSITIVITY ANALYSIS
      8. CONCLUSION
      9. FUTURE RESEARCH DIRECTIONS
      10. REFERENCES
      11. ADDITIONAL READING
    6. VI. Condition Monitoring Using Computational Intelligence
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. FEATURE EXTRACTION
        1. Fractal Dimension
          1. Box-Counting Dimension
          2. Multiscale Fractal Dimension (MFD)
        2. Mel-Frequency Cepstral Coefficients (MFCCs)
        3. Kurtosis
      5. CLASSIFICATION SYSTEM
        1. Support Vector Machine (SVM)
        2. Hidden Markov Model (HMM)
        3. Gaussian Mixture Models (GMM)
        4. Extension Neural Network (ENN)
      6. PROPOSED ARCHITECTURE
      7. RESULTS AND DISCUSSION
        1. Vibration Data
        2. Results
      8. CONCLUSION
      9. FUTURE RESEARCH DIRECTION
      10. REFERENCES
      11. ADDITIONAL READINGS
    7. VII. Demand Forecasting of Short Life Span Products: Issues, Challenges, and Use of Soft Computing Techniques
      1. ABSTRACT
      2. INTRODUCTION
      3. EXISTING TOOLS
        1. Typical Data Mining Software
        2. SIMForecaster: Existing Forecasting Tool
        3. Integration of Technologies
        4. Key Capabilities
        5. Some Success Stories
      4. CASE STUDIES
        1. Electronics Components Industry
        2. Perishable Goods Industry
        3. Entertainment and Gaming Industry
      5. RECOMMENDATIONS: USE OF SOFT COMPUTING
        1. Neural Networks
          1. Binary Neural Networks (BNNs)
          2. Elman's Simple Recurrent Neural Network
          3. Bidirectional Recurrent Neural Networks (BRNNs)
          4. Pollastri's Bidirectional Recurrent Neural Networks (PBRNNs)
          5. Segmented Memory Recurrent Neural Networks (SMRNNs)
          6. Long Term Dependencies: Limitations of RNNs
          7. Long Short-Term Memory (LSTM) Networks
        2. Small World Theory
        3. Evolutionary Computing Techniques
        4. Genetic Algorithms
        5. Theory of Memes
      6. FUTURE RESEARCH TRENDS
      7. REFERENCES
      8. ADDITIONAL READING
    8. VIII. Introduction to Data Mining and its Applications to Manufacturing
      1. ABSTRACT
      2. INTRODUCTION
      3. DATA MINING DEFINITIONS AND THE DATA MINING PROCESS
        1. Goal of Data Mining
        2. Data Mining Definitions
        3. Disciplines Contributing to Data Mining
        4. The Data Mining Process
        5. The Need for Data Integration
        6. Data Mining Data Models
      4. MAIN CATEGORIES OF DATA MINING TASKS
        1. Concept Description
        2. Association Rule Mining
        3. Classification and Prediction
        4. Cluster Analysis
        5. A Brief Overview of Decision Trees, Neural Networks and Bayesian Networks
          1. Decision Trees
          2. Neural Networks
          3. Bayesian Networks
      5. EXAMPLES OF DATA MINING APPLICATIONS IN MANUFACTURING
      6. DATA MINING AND DATA WAREHOUSING
        1. Operational Transaction Needs vs. Analytical Needs
        2. A Definition of a Data Warehouse
        3. Data Mining and OLAP
        4. The Evolution of Quality Management in Manufacturing
          1. First Generation
          2. Second Generation
          3. Third Generation
      7. FUTURE TRENDS
        1. The Growing Interest in Data Mining
        2. Data Mining Vendors
          1. Data Mining Tools Integrated With Data Base Management Systems
          2. Data Mining Tools Integrated With Statistical Analysis Tools
          3. Data Mining Tools Embedded Into Business Applications
          4. Specialized Data Mining Vendors
      8. CONCLUSION
      9. FUTURE RESEARCH DIRECTIONS
      10. REFERENCES
      11. ADDITIONAL READING
    9. IX. Evolutionary Computing in Engineering Design
      1. ABSTRACT
      2. INTRODUCTION
      3. TAXONOMY OF ENGINEERING DESIGN OPTIMISATION
      4. EVOLUTIONARY COMPUTING
      5. OVERVIEW OF EVOLUTIONARY COMPUTING APPLICATIONS IN ENGINEERING DESIGN
        1. EC in Aerospace Engineering
        2. EC in Chemical Engineering
        3. Materials Engineering
        4. Systems Engineering
      6. EVOLUTIONARY COMPUTING APPLICATION TRENDS IN THE LAST 12 YEARS
      7. CHALLENGES IN DESIGN OPTIMISATION
      8. LIMITATIONS OF EVOLUTIONARY COMPUTING
      9. FUTURE RESEARCH DIRECTIONS
      10. CONCLUSION
      11. ACKNOWLEDGMENT
      12. REFERENCES
      13. ADDITIONAL READING
  7. II. Supply Chain and Decision Support Systems
    1. X. Towards a Methodology for Monitoring and Analyzing the Supply Chain Behavior
      1. ABSTRACT
      2. INTRODUCTION
      3. LITERATURE BACKGROUND
      4. METHODOLOGY
        1. Supply Chain Environment
        2. Behavior Monitoring Module
          1. Definition of Decision, State, and Input Vectors
          2. SD Model of the Supply Chain
          3. Behavior Mode Classification
          4. Feedforward Neural Network Training
        3. Eigenvalue and Elasticity Analysis
          1. Eigenvalue Analysis
          2. Elasticity Analysis
          3. Sensitivity Analysis and Optimization
      5. CASE STUDY: LSMC'S SUPPLY CHAIN
        1. The Environment
        2. The Behavior Monitoring Module
          1. Definition of the Input Vector
          2. LSMC's SD Supply Chain Model
          3. Behavior Mode Classification
          4. Feedforward Neural Network Training
          5. Detecting Undesired Behavior
          6. Eigenvalue & Elasticity Analysis of Supply Chain Behavior
          7. Sensitivity Analysis/Optimization
      6. CONCLUSION
      7. FUTURE RESEARCH DIRECTIONS
      8. REFERENCES
      9. ADDITIONAL READING
    2. XI. Decision Support System for Project Selection
      1. ABSTRACT
      2. INTRODUCTION
      3. METHODOLOGY
      4. LITERATURE ON PROJECT SELECTION
      5. AN AHP-BASED APPROACH TO EVALUATE PROJECT
      6. APPLICATION
        1. Project Description
        2. Customary Project Evaluation Processes of Petroleum Pipelines
        3. Proposed Project Evaluation and Selection Model
        4. Technical Factors
          1. Pipeline Length
          2. Operability
          3. Maintainability
          4. Approachability
          5. Constructability
        5. Environmental Factors
        6. Socio-Economic Factors
          1. Planning Stage
          2. Construction Stage
          3. Effect of Employment Generation
          4. Effect of Construction Activity
          5. Operation Stage
        7. Project Selection Model
        8. Results and Findings
        9. Validation of the Model
        10. Financial Analysis
      7. SUMMARY AND CONCLUSION
      8. FUTURE RESEARCH DIRECTIONS
      9. REFERENCES
      10. ADDITIONAL READING
    3. XII. Modeling and Coordination of Dynamic Supply Networks
      1. ABSTRACT
      2. INTRODUCTION
      3. DYNAMIC SUPPLY NETWORKS
      4. SYSTEM DYNAMICS
      5. DYNAMIC NETWORK PROCESS
        1. Example 1
      6. DYNAMIC GROUP-ALOP PROCEDURE
      7. SUPPLIER SELECTION PROBLEM
        1. Example 2
      8. THREE LAYER MODELING FRAMEWORK
      9. CONCLUSION
      10. FUTURE RESEARCH DIRECTIONS
      11. ACKNOWLEDGMENT
      12. REFERENCES
      13. ADDITIONAL READING
    4. XIII. Modeling with System Archetypes: A Case Study
      1. ABSTRACT
      2. INTRODUCTION
      3. METHODOLOGY
      4. CAUSAL LOOP ANALYSIS AND RESULTS: APPLYING THE SYSTEM ARCHETYPES
      5. CHANGES IN VOLUME OF PATIENT REFERRALS
      6. GAPS IN AVAILABILITY OF HOSPITAL BEDS
      7. ARCHETYPE MODEL REFERENCE BEHAVIOR
      8. SCENARIO ANALYSIS
      9. IMPLICATIONS AND CONCLUSION
      10. FUTURE DIRECTIONS
      11. REFERENCES
      12. ADDITIONAL READING
    5. XIV. Integrated Manufacturing Applications and Management Decision Making: Putting the P Back into ERP
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
        1. Management Support Systems and ERP
        2. Organisation Design Configuration and ERP
        3. Decision Making and ERP
      4. RESEARCH APPROACH
      5. ISSUES, CONTROVERSIES, AND PROBLEMS
        1. Data
        2. Granularity of Data and Metadata Logic
        3. Data Latency and the Technical Infrastructure
      6. ORGANISATION AND APPLICATIONS
        1. Goal Focus and Key Performance Indicators (KPIs)
        2. Information Latency and the Complexity of the Business Model
      7. KEY MANAGEMENT DECISIONS AND ROLE OF ERP
        1. D0 What to Build?
        2. D1 What to Buy?
        3. D2 What Has Been Built?
        4. D4 What to Stop Building?
        5. D5 What has Been Delivered?
        6. D6 What to Allocate and Ship?
      8. LESSONS LEARNED FROM FIELD WORK
      9. CONCLUSION
      10. FUTURE RESEARCH DIRECTIONS
      11. REFERENCES
      12. ADDITIONAL READING
    6. XV. Planning and Deployment of Dynamic Web Technologies for Supporting E–Business
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
        1. Choices to be Made
        2. Financial Matters
      4. COMPARING THE OPTIONS
        1. Web Applications Development
        2. Web Server Software Options
        3. Operating System Considerations
      5. SELECTING THE HARDWARE
        1. Another Alternative to Consider: Outsourcing
        2. A Summary
      6. CONCLUDING REMARKS
      7. FUTURE RESEARCH DIRECTIONS
      8. REFERENCES
      9. RECOMMENDED READINGS
    7. XVI. Web-Based Decision Support System: Concept and Issues
      1. ABSTRACT
      2. INTRODUCTION
      3. GENERAL ARCHITECTURE OF CONVENTIONAL WEB-BASED DSS
      4. BASIC TECHNOLOGIES USED IN WEB-BASED DSS
        1. HTML and XML
        2. CGI Scripts
        3. Embedded Scripting
        4. Reusable Components
      5. FEW EXAMPLES OF WEB-BASED DECISION SUPPORT SYSTEMS
        1. GDSI: A Web-based DSS for Effective Use of Clinical Practice Guidelines (Douglas, Rouse, Ko, & Niland, 2004)
        2. Long-Term Hydrological Impact Assessment (L-THIA) (Bernard, Choi, Harbor, & Pandey, 2003)
        3. Stockpoint-Stockfinder (Stockfinder, 2006)
        4. Bayer Corporation's Web-Based DSS Tool
      6. EVOLVING CONCEPT: APPLICATION OF INTELLIGENT SOFTWARE AGENTS IN BUILDING WEB-BASED DSS
      7. ISSUES IN WEB-BASED DECISION SUPPORT SYSTEM (DSS)
      8. A PROTOTYPE WEB-BASED DSS FOR A LARGE RIVER SYSTEM
      9. BRISS
        1. The Model Base of BRISS
      10. CONCLUSION
      11. FUTURE RESEARCH DIRECTIONS
      12. REFERENCES
      13. ADDITIONAL READING
  8. III. Applications in Manufacturing and Production Management
    1. XVII. Independent Component Analysis and its Applications to Manufacturing Problems
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. BASIC THEORY OF ICA
        1. Assumptions of ICA Model
        2. Selection of Objective Function
          1. Likelihood and Network Entropy
          2. Nengentropy and Approximations of Negentropy
          3. Mutual Information and Kullback-Leibler Divergence
          4. Nonlinear Cross-Correlations
          5. Objective Function of KICA
      5. ALGORITHMS FOR ICA
        1. Processing of the Data
        2. Algorithms of ICA
          1. Jutten-Herault Algorithm
          2. Algorithms for Maximum Likelihood or Infomax Estimation
          3. Nonlinear PCA Algorithm
          4. The Fast ICA Algorithm
          5. Tensor-Based Algorithms
          6. Kernel ICA Algorithm
          7. Constrained ICA Algorithm
      6. APPLICATIONS
        1. Mineral Resources Prediction
          1. Gold Ore Bodies Localization Prognosis of Peripheral
          2. Polymetallic Mines Localization Prognosis
        2. Extracting the Information of Remote Sensing Imagery
      7. CONCLUSION
      8. FUTURE RESEARCH DIRECTIONS
      9. ACKNOWLEDGMENT
      10. REFERENCES
      11. ADDITIONAL READING
    2. XVIII. Swarm Intelligence in Production Management and Engineering
      1. ABSTRACT
      2. INTRODUCTION
        1. General Characteristics of Swarm Intelligence Algorithms
        2. Ant Colony Systems: An Overview
          1. The ACO Algorithm
          2. Solving the Classical TSP Problem by ACO
        3. The Particle Swarm Optimisation (PSO)
        4. The PSO Algorithm
      3. PSO FOR ROBOT MOTION PLANNING
      4. APPLICATION OF PSO IN PID TUNING
      5. ANT COLONY SYSTEMS IN VEHICLE ROUTING AND ADVANCED LOGISTIC PROBLEMS
        1. Logistic Chain and the Work Flow Loop in a Company
      6. CONCLUSION
      7. FUTURE RESEARCH DIRECTION
      8. REFERENCES
      9. ADDITIONAL READINGS
    3. XIX. Artificial Neural Network and Metaheuristic Strategies: Emerging Tools for Metal Cutting Process Optimization
      1. ABSTRACT
      2. INTRODUCTION
        1. Inferential Modelling of Grinding Process
        2. Optimal Solution Techniques for Grinding Process
      3. BACKGROUND
        1. Artificial Neural Network Models
        2. Determination of Optimal or Near-Optimal Cutting Condition(s)
          1. Metaheuristic Strategies
          2. Genetic Algorithm (GA)
          3. Simulated Annealing (SA)
          4. Tabu Search (TS)
      4. ISSUES, CONTROVERSIES, PROBLEM AND SOLUTION METHODOLOGY
        1. Problem Formulation
          1. Inputs and In-Process Variables for Inferential Modelling
          2. Desirability Functions
          3. Composite Desirability or Dimensionality Reduction Strategy
          4. Process Constraint(s) or Requirement(s)
          5. Formulation of Objective Criteria
          6. Solution Methodology
          7. Error Back Propagation-Based ANN (BPNN)
          8. Real-Coded Genetic Algorithm (RGA)
          9. Simulated Annealing (SA)
        2. Tabu Search (TS)
      5. CASE EXAMPLE
        1. BPNN Algorithm-Based Grinding Process Model
        2. Metaheuristic Approach
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. ACKNOWLEDGMENT
      9. REFERENCES
      10. ADDITIONAL READING
    4. XX. Intelligent Laser Scanning of 3D Surfaces Using Optical Camera Data
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. OPTICAL CAMERA DATA INTEGRATION
      5. OUR INVESTIGATION
      6. IMAGE PROCESSING FOR EDGE DETECTION
      7. VECTORISATION AND SCAN REGION DEVELOPMENT
        1. Algorithm A
        2. Algorithm B
      8. CALIBRATION
      9. RESULTS
      10. DISCUSSION
      11. CONCLUSION
      12. ACKNOWLEDGMENT
      13. FUTURE RESEARCH DIRECTIONS
      14. REFERENCES
      15. ADDITIONAL READING
    5. XXI. Using Data Mining for Forecasting Data Management Needs
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN THRUST OF THE CHAPTER: ISSUES, CONTROVERSIES, PROBLEMS
        1. Issues: Data Used in Research
        2. Issues: Data Mining Tools Used
        3. Problems and Results: Data Mining for Forecasting Data Management Needs
          1. Result 1a: Data Mining Using BioDiscovery GeneSight® for Forest Cover Type Data
          2. Result 1b: Data Mining Using BioDiscovery GeneSight® for Human Lung Microarray Data
          3. Result 2a: Data Mining Using Megaputer PolyAnalyst® 5.0 for Forest Cover Type Data
          4. Result 2b: Data Mining Using Mega-puter PolyAnalyst® 5.0 for Human Lung Microarray Data
          5. Result 3a: Data Mining Using SAS® Enterprise MinerTM for Forest Cover Type Data
          6. Result 3b: Data Mining Using SAS® Enterprise MinerTM for Human Lung Microarray Data
          7. Result 4a: Data Mining Using NeuralWare Predict® for Forest Cover Type Data
          8. Result 4b: Data Mining Using NeuralWare Predict® for Human Lung Microarray Data
      5. CONCLUSION
      6. FUTURE RESEARCH DIRECTIONS
      7. ACKNOWLEDGMENT
      8. REFERENCES
      9. ADDITIONAL READINGS
    6. XXII. Supply Network Planning Models Using Enterprise Resource Planning Systems
      1. ABSTRACT
      2. INTRODUCTION TO ERP SYSTEMS
        1. Evolution of ERP Systems
          1. ERP Systems
          2. Customer Relationship Management Systems
          3. Supply Chain Management Systems
          4. Supplier Relationship Management Systems
          5. Product Lifecycle Management Systems
      3. ERP VENDORS
      4. MANUFACTURING PROCESSES
        1. Planning
        2. Execution
        3. Collaboration
      5. ERP SYSTEMS AND MANUFACTURING PROCESSES
        1. Supply Chain Management Systems
        2. Product Lifecycle Management Systems
      6. CASE STUDY
        1. Task 1: Defining the Supply Network Planning Model Agents
          1. Task 1a: Defining the Supply Chain Model
          2. Task 1 b: Analysis of Supply Network Model
          3. Task 1c: Validation and Implementation of Supply Network Planning Process
        2. Task 2: Model Evaluation and Improvement
          1. Task 2a: Definition of Performance Measures
          2. Task 2b: Evaluation and Improvement of Performance Measures
        3. Task 3: Implementation of Supply Chain Network Planning Model in ERP Environment
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
      9. REFERENCES
      10. ADDITIONAL READING
    7. XXIII. Modeling and Analysis for Production Performance: Analysis of U.S. Manufacturing Companies
      1. ABSTRACT
      2. INTRODUCTION
      3. SYSTEM DYNAMICS MODELING
        1. Understanding Causal Relations
        2. Modeling Characteristics
        3. SD Overview Of Manufacturing System
        4. Qualitative Analysis Of Policy Options
          1. Option 1: Growth through Investment
          2. Option 2: Growth through Improvement in Manufacturing Practices
          3. Option 3: Growth through Competitiveness
        5. Lessons Learned From System Dynamics Modeling
      4. QUANTITATIVE SURVEY APPROACH
        1. Research Method
        2. Results Analysis
        3. Process Management Practices vs. Product Quality
        4. Organizational Culture vs. Financial Performance
        5. Technology Management Practices vs. Financial Performance
        6. Organizational Strategy vs. Product and Process Innovation
        7. Lessons Learned from the Survey
      5. DISCUSSION
      6. FUTURE RESEARCH DIRECTIONS
      7. REFERENCES
      8. ADDITIONAL READING
  9. About the Contributors