Optimal Inventory Modeling of Systems: Multi-Echelon Techniques: 72 - Rilegato

Sherbrooke, Craig C.

Rilegato

ISBN 10: 1402078498 ISBN 13: 9781402078491

Casa editrice: Kluwer Academic Pub, 2004

Riassunto

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.

Su questo libro

Recensione:

About the first edition:
"This book is a remarkable review and summary of nearly 30 years work on applied inventory theory. The book is a model of clarity and coherence. Even those concerned with other problem domains may benefit from the distilled wisdom it offers."
(Interfaces - Professor Steve New, University of Manchester)
"A large number of solved numerical examples help with the understanding of the models and mathematics used. Undoubtedly, a book of such integrity deserves a place on the shelf of any person, library or organization whose interests lie in the domain of inventory theory and its application to complex systems."
(Logistics Spectrum - Professor Mirce Knezevic, Exeter University)
About the second edition:
"In the second edition, the basics remain the same and should be considered essential knowledge for logisticians and system managers. Sherbrooke has spent his career solving real inventory problems. Practical examples help the reader understand critical concepts like marginal analysis, expected backorders, cost-availability curves, optimization, and analytical versus simulation based models. In Optimal Inventory Modeling of Systems, Sherbrooke tells us how we (public and private sector managers) can better understand and act on the critical trade-offs between cost and system availability. This reference text should be on your bookshelf."
(George T. Babbitt, General, USAF (Retired), Formerly Commander, Air Force Material Command; Director, Defense Logistics Agency)

Contenuti:

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

Su questa edizione

• Editore: Kluwer Academic Pub
• Data di pubblicazione: 2004
• ISBN 10: 1402078498
• ISBN 13: 9781402078491
• Rilegatura: Copertina rigida
• Numero edizione: 2
• Numero di pagine: 332