Optimal inventory modeling of systems : multi-echelon techniques
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Bibliographic Information
Optimal inventory modeling of systems : multi-echelon techniques
（International series in operations research & management science, 72）
Kluwer Academic, 2004
2nd ed
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Includes bibliographical references and index
Description and Table of Contents
Description
Most books on inventory theory use the item approach to determine stock levels, ignoring the impact of unit cost, echelon location, and hardware indenture. Optimal Inventory Modeling of Systems is the first book to take the system approach to inventory modeling. The result has been dramatic reductions in the resources to operate many systems - fleets of aircraft, ships, telecommunications networks, electric utilities, and the space station.
Although only four chapters and appendices are totally new in this edition, extensive revisions have been made in all chapters, adding numerous worked-out examples. Many new applications have been added including commercial airlines, experience gained during Desert Storm, and adoption of the Windows interface as a standard for personal computer models.
Table of Contents
Dedication. List of Figures. List of Tables. List of Variables. Preface. Acknowledgements. 1: Introduction. 1.1. Chapter Overview. 1.2. The System Approach. 1.3. The Item Approach. 1.4. Repairable vs. Consumable Items. 1.5. 'Physics' of the Problem. 1.6. Multi-Item Optimization. 1.7. Multi-Echelon Optimization. 1.8. Multi-Indenture Optimization. 1.9. Field Test Experience. 1.10. The Item Approach Revisited. 1.11. The System Approach Revisited. 1.12. Summary. 1.13. Problems. 2: Single-Site Inventory Model For Repairable Items. 2.1. Chapter Overview. 2.2. Mean and Variance. 2.3. Poisson Distribution and Notation. 2.4. Palm's Theorem. 2.5. Justification of Independent Repair Times and Constant Demand. 2.6. Stock Level. 2.7. Item Performance Measures. 2.8. System Performance Measures. 2.9. Single-Site Model. 2.10. Marginal Analysis. 2.11. Convexity. 2.12. Mathematical Solution of Marginal Analysis. 2.13. Separability. 2.14. Availability. 2.15. Summary. 2.16. Problems. 3: Metric: A Multi-Echelon Model. 3.1. Chapter Overview. 3.2. METRIC Model Assumptions. 3.3. METRIC Theory. 3.4. Numerical Example. 3.5. Convexification. 3.6. Summary of the METRIC Optimization Procedure. 3.7. Availability. 3.8. Summary. 3.9. Problems. 4: Demand Processes And Demand Prediction. 4.1. Chapter Overview. 4.2. Poisson Process. 4.3. Negative Binomial Distribution. 4.4. Multi-Indenture Problem. 4.5. Multi-Indenture Example. 4.6. Variance of the Number of Units in the Pipeline. 4.7. Multi-Indenture Example Revisited. 4.8. Demand Rates that Vary with Time. 4.9. Bayesian Analysis. 4.10. Objective Bayes. 4.11. Bayesian Analysis in the Case of Initial Estimate Data. 4.12. James-Stein Estimation. 4.13. James-Stein Estimation Experiment. 4.14. Comparison of Bayes and James-Stein. 4.15. Demand Prediction Experiment Design. 4.16. Demand Prediction Experiment Results. 4.17. Random Failure versus Wear-out Processes. 4.18. Goodness-of-Fit Tests. 4.19. Summary. 4.20. Problems. 5: Vari-METRIC: A Multi-Echelon, Multi-Indenture Model. 5.1. Chapter Overview. 5.2. Mathematical Preliminary: Multi-Echelon Theory. 5.3. Definitions. 5.4. Demand Rates. 5.5. Mean and Variance for the Number of LRUs in Depot Repair. 5.6. Mean and Variance for the Number of SRUs in Base Repair or Resupply. 5.7. Mean and Variance for the Number of LRUs in Base Repair or Resupply. 5.8. Availability. 5.9. Optimization. 5.10. Generalization of the Resupply Time Assumptions. 5.11. Generalization of the Poisson Demand Assumption. 5.12. Common Items. 5.13. Consumable and Partially Repairable Items. 5.14. Numerical Example. 5.15. Item Criticality Diff
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