Inventory Management in Multi-Echelon Networks: On the Optimization of Reorder Points
by Christopher Grob (Author)

About the Author
Christopher Grob works in after sales supply chain management at a major German automotive company. He is responsible for the functional development of inventory planning systems for the spare parts business. He is an expert in the field of inventory management.

About this book
Inventory Management in Multi-Echelon Networks presents methods to plan inventory in distribution networks.  By holistically looking at the supply chain, it shows how safety stocks across all echelons can be optimized if inventory of all levels is taken into consideration. The gap between the existence of advanced inventory planning methods and their low penetration in the industry was the motivation for this book. Christopher Grob develops essential algorithms that companies can use for network inventory planning and highlights achievable implementation benefits. The work of the author was inspired by the needs of an after sales supply chain of a large automotive company. This company supplies customers all over the world with spare parts and operates a distribution network with more than 100 warehouses. This supply chain faces two particular challenges: demand is highly uncertain and customers expect a high service level.

Table of contents
1 Introduction
    1.1 Literature review
2 Inventory management
    2.1 Basic concepts
        2.1.1 Supply chain structures
        2.1.2 Inventory
        2.1.3 Customer response to stock-outs
        2.1.4 Lead time
        2.1.5 Demand
        2.1.6 The order quantity
        2.1.7 Order policies
        2.1.8 Service level definitions
        2.1.9 Cost structure
    2.2 Single-echelon systems
        2.2.1 Stochastic Lead Time Demand
        2.2.2 Insights on the inventory position and the inventory level
        2.2.3 Calculating the fill rate for some demand distributions
        2.2.4 Determining optimal reorder points
3 Multi-echelon distribution networks
    3.1 Lead time demand and fill rate at non-local warehouses
        3.1.1 Mean and variance of lead time demand
        3.1.2 Fitting a distribution and calculating the fill rate
    3.2 Wait time approximation
        3.2.1 METRIC-type approximation
        3.2.2 Kiesmuller et al. approximation
        3.2.3 Berling and Farvid approximation
        3.2.4 Negative Binomal approximation
    3.3 Summary of assumptions and classification within the literature
4 Multi-echelon optimization
    4.1 The 2-echelon case
        4.1.1 Problem formulation
        4.1.2 The general algorithm
        4.1.3 Initialization
        4.1.4 Checking and refining breakpoints
        4.1.5 Adding new breakpoints
        4.1.6 Heuristic to speed up the algorithm by introducing additional step size constraints
    4.2 The n-echelon case
        4.2.1 Wait time calculation in n-echelons
        4.2.2 Structure of the optimization problem
        4.2.3 Optimization algorithm
        4.2.4 Step size heuristic
5 Experimental results
    5.1 Set-up of simulation
    5.2 Comparison of wait time approximations
        5.2.1 Random data
        5.2.2 Real-world data
        5.2.3 Summary of comparison
    5.3 2-echelon results
        5.3.1 Structure of the optimal solution
        5.3.2 Optimization and simulation using real-world data
        5.3.3 Performance of the algorithm
        5.3.4 Analysis of heuristics
    5.4 n-echelon results
        5.4.1 Set-up of optimization and input data
        5.4.2 Structure of the solution
6 Real-world application
    6.1 Selection of suitable wait time approximations
    6.2 Selection of optimization algorithms and heuristics
    6.3 Key takeaways for implementation in real world applications
7 Conclusion

Series: AutoUni - Schriftenreihe (Book 128)
Length: 140 pages
Publisher: Springer; 1st ed. 2019 edition (September 28, 2018)
Language: English
ISBN-10: 3658233745
ISBN-13: 978-3658233747