Optimal Inventory Modeling of Systems: Multi-Echelon Techniques (Hardcover, 2, 2004)

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