Introduction to Distribution Logistics (Statistics in Practice) (Hardcover)

"Valuable text for distribution logistics course at both the advanced undergraduate and beginning graduate levels in a variety of disciplines." (Mathematical Reviews 2008)

Product Description
This text presents the basics of distribution logistics (DL) in both a qualitative and quantitative manner so as to reach out to a multitude of reader backgrounds. Devoid of solid quantitative books in the marketplace, this book fills a gap. The authors do not encourage the undiscriminating use of sophisticated models and algorithms to the detriment of intuition and common sense. The emphases throughout the book are on the variety and complexity of issues and sub problems surrounding DL and their limitations and scope of applicability. The context in which a firm operates, its strategic positioning, and the managerial levers that decision makers may act upon represent key discussions and provide a unified approach to the subject matter.

Contents
Preface . . . xaaa
1 Supply Chain Management 1
1.1 What do we mean by logistics? 1
1.1.1 Plan of the chapter 4
1.3 Competition factors, cost drivers, and strategy 9
1.3.1 Competition factors 9
1.3.2 Cost drivers 12
1.3.3 Strategy 16
1.4 The role of inventories 18
1.4.1 A classical model: Economic order
1.4.2 Capacity-induced stock 25
1.5 Dealing with uncertainty 26
1.5.1 Setting safety stocks 27
1.5.2 A two-stage decision process: Production
planning in a n assemble-to-order
environment 30
1.5.3 Inventory deployment 39
2.2 Structure of production/distribution networks 6
quantity 19
V
vi CONTENTS
1.6 Physical flows and transportation 40
1.7 Information flows and decision rights 41
1.8 Time horizons and hierarchical levels 42
1.9 Decision approaches 44
1.10 Quantitative models and methods 48
1.11 For further reading 50
References 51
2 Network Design and Transportation
2.1 The role of intermediate nodes in a distribution
network
2.1.1 The risk pooling eflect: reducing the
2.1.2
Location and flow Optimization models
2.2.1 The transportation problem
2.2.2
2.2.3 The plant location problem
2.2.4 Putting it all together
2.3 Models involving nonlinear costs
W.2.4 Continuous-space location models
W.2.5 Retail-store location models
2.6 For Further Reading
uncertainty level
The role of distribution centers and transit
points in transportation optimization
2.2
The minimum cost flow problem
References
3 Forecasting
3.1 Introduction
3.2
3.3 Metrics for forecast errors
The variable to be predicted
3.2.1 The forecasting process
3.3.1 The Mean Error
3.3.2 Mean Absolute Deviation
3.3.3 Root Mean Square Error
3.3.4
3.3.5 ME%, MAD%, RMSE%
3.3.6 Theil’s U statistic
3.3.7
Mean Percentage Error and Mean
Absolute Percentage Error
Using metrics of forecasting accuracy
3.4
3.5 Moving Average
A classification of forecasting methods
3.5.1 The demand model
3.5.2 The algorithm
3.5.3 Setting the parameter
3.5.4 Drawbacks and limitations
3.6.1 The demand model
3.6.2 The algorithm
3.6.3 Setting the parameter
3.6.4 In,itialization
3.6.5 Drawbacks and limitations
3.7.1 The demand model
3.7.2 The algorithm
3.7.3 Setting the parameters
3.7.4 Initialization
3.7.5 Drawbacks and limitations
3.8 Exponential smoothing with seasonality
3.8.1 The demand model
3.8.2 The algorithm
3.8.3 Setting the parameters
3.8.4 Initialization
3.8.5 Drawbacks and limitations
Smoothing with seasonality and trend
3.9.1 The demand model
3.9.2 The algorithm
3.9.3 Initialization
3.10 Simple linear regression
3.10.1 Setting up data for regression
W.3.11 Forecasting models based on multiple regression
3.12 Forecasting demand for new products
3.12.1 The Delphi method and the committee
process
3.12.2 Lancaster model: forecasting new products
through product features
3.12.3 The early sales model
3.13.1 Limitations and drawbacks
3.6 Simple exponential smoothing
3.7 Exponential Smoothing with Trend
3.9
3.13 The Bass model
References 185
4 Inventory Management with Deterministic Demand
4.1 Introduction
4.2 Economic Order Quantity
4.3 Robustness of EOQ model
4.4
4.5
4.6 Multi-item EOQ
Case of LT > 0: the (Q1 R) model
Case of finite replenishment rate
4.6.1
4.6.2
The case of shared ordering costs
The multi-item case with a constraint o n
ordering capacity
4.7 Case of nonlinear costs
4.8 The case of variable demand with known
variability
Ref e re n ce s
5 Inventory Control: The Stochastic Case
5.1 Introduction
5.2 The newsvendor problem
5.3 Multi-period problems
5.4
5.2.1
Fixed quantity: the (Ql R) model
5.4.1
Extensions of the newsvendor problem
Optimization of the (Q,R) model in case
the stockout cost depends on the size of
the stockout
(Q,R) system: case of constraint o n the
type II service level
(Q, R) system: case of constraint o n type I
service level
5.4.2
5.4.3
5.5 Periodic review: S and ( s , S )p olicies
5.6 The S policy
5.7 The ( s , S ) policy
S.5.8 Optimization of the (Q,R) model when the cost
of a stockout depends on the occurrence of a
stockout
References
6 Managing Inventories in Multiechelon Supply Chains 303
6.1 Introduction 303
CONTENTS ix
6.2 Managing multiechelon chains: Installation us.
Echelon Stock
6.2.1
Coordination in the supply chain: the Bullwhip
ejfect
A linear distribution chain with two echelons and
certain demand
Arborescent chain: transit point with uncertain
demand
A two-echelon supply chain in case of stochastic
demand
References
Features of Installation and Echelon Stock
logics
7 Incentives in the Supply Chain
7.1 Introduction
7.2 Decisions on price: double marginalization
7.2.1 The first best solution: the vertically
integrated firm
7.2.2 The vertically disintegrated case:
independent manufacturer and retailer
7.2.3 A way out: designing incentive schemes
Decision on price in a competitive environment
7.3.1 The vertically disintegrated supply chain:
independent manufacturer and retailer.
Decision on inventories: the newsvendor problem
7.4.1 The first best solution: the vertically
7.4.2 The vertically disintegrated case:
7.4.3 A way out: designing incentives and
Decision on eflort to produce and sell the product
7.5.1 The first best solution: the vertically
7.5.2 The vertically disintegrated case:
integrated firm
independent manufacturer and retailer
reallocating decision rights
7.5
integrated firm
independent retailer and manufacturer
A way out: designing incentive schemes.
The case of eflorts both at the upstream
and downstream stage
7.6 Concluding remarks
A.9 Hypothesis testing
A.9.1
A.9.2
An example of a nonparametric test: the
chi-square test
Testing hypotheses about the difference in
the mean of two populations
A.10.1 Best fitting b y least squares
A.lU.2 Analyzing properties of regression
A. 10.3 Confidence intervals and hypothesis
A . 10.4 Performance measures for linear
A. 10.5 Verification of the underlying assumptions
A.lU.6 Using linear regression to estimate
A.10 Simple linear regression
estimators
testing for regression estimators
regression
nonlinear relationships
W.A .1 1 Multiple linear regression
A. 12 For further reading
References
Appendix B An Even Quicker Tour in Mathematical
Role and limitations of optimization models
Programming
B.1
B.2 Optimization models
B.3 Convex sets and functions
B.4 Nonlinear programming
B.4.l The case of inequality constraints
~ . 4 . 2 An economic interpretation of Lagrange
mu l tip 1 i e rs : shadow prices
B.5 Linear programming
B. 6 Integer linear programming
B.6.1 Branch and bound methods
B. 6.2
Elements of multiobjectizie optimization
References
Model building an integer programming
B. 7
B.8 For further reading
Index