Front cover image for Optimal inventory modeling of systems : multi-echelon techniques

Optimal inventory modeling of systems : multi-echelon techniques

Most books on inventory theory use the item approach to determine stock levels, ignoring the impact of unit cost, echelon location, and hardware indenture.
eBook, English, 2004
Kluwer Academic, Boston, 2004
1 online resource (xxx, 332 pages) : illustrations
9781402078651, 9781402078491, 9786610148356, 140207865X, 1402078498, 661014835X
70738431
Cover
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 CONSTANTDEMAND
2.6 STOCKLEVEL
2.7 ITEM PERFORMANCEMEASURES
2.8 SYSTEM PERFORMANCEMEASURES
2.9 SINGLE-SITEMODEL
2.10 MARGINALANALYSIS
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 FAILUREVERSUSWEAR-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 DIFFERENCES
5.16 AVAILABILITY DEGRADATIONDUE TO MAINTENANCE
5.17 AVAILABILITY FORMULAUNDERESTIMATES FOR AIRCRAFT
5.18 SUMMARY
5.19 PROBLEMS
6 MULTI-ECHELON, MULTI-INDENTURE MODELS WITH PERIODIC SUPPLY AND REDUNDANCY
6.1 SPACE STATION DESCRIPTIO
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