1. Edition - August 2007
105.- Euro
2007. 588 Pages, Hardcover
ISBN-10: 0-471-75044-1
ISBN-13: 978-0-471-75044-4 - John Wiley & Sons






Sample Chapter

Short description
Introduction to Distribution Logistics 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.

From the contents
Preface.

1. Supply chain management.

1.1 What do we mean by logistics?

1.2 Structure of production/distribution networks.

1.3 Competition factors, cost drivers, and strategy.

1.4 The role of inventories.

1.5 Dealing with uncertainty.

1.6 Physical flows and transportation.

1.7 Time horizons and hierarchical levels.

1.8 Decision approaches.

1.9 Information flows and decision rights.

1.10 Quantitative models and methods.

1.11 For further reading.

References.

2. Network Design and Transportation.

2.1 The role of intermediate nodes in a distribution network.

2.2 Location and flow optimization models.

2.3 Models involving nonlinear costs.

2.4 For Further Reading.

References.

3. Forecasting.

3.1 Introduction.

3.2 The variable to be predicted.

3.3 Metrics for forecast errors.

3.4 A classification of forecasting methods.

3.5 Moving Average.

3.6 Simple exponential smoothing.

3.7 Exponential Smoothing with Trend.

3.8 Exponential smoothing with seasonality.

3.9 Smoothing with seasonality and trend.

3.10 Simple linear regression.

3.11 Forecasting new products.

3.12 The Bass model.

References.

4. Inventory management with Deterministic Demand.

4.1 Introduction.

4.2 Economic Order Quantity.

4.3 Robustness of EOQ model.

4.4 Case of LT > 0: the (Q,R) model.

4.5 Case of finite replenishment rate.

4.6 Multi-item EOQ.

4.7 Case of nonlinear costs.

4.8 The case of variable demand with known variability.

References.

5. Inventory control: the stochastic case.

5.1 Introduction.

5.2 The newsvendor problem.

5.3 Multi-period problems.

5.4 Fixed quantity: the (Q,R) model.

5.5 Periodic review: S and (s, S) policies.

5.6 The S policy.

5.7 The (s, S) policy.

References.

6. Managing inventories in multiechelon supply chains.

6.1 Introduction.

6.2 Managing multi-echelon chains: Installation vs. Echelon Stock.

6.3 Coordination in the supply chain: the Bullwhip effect.

6.4 A linear distribution chain with two echelons and certain demand.

6.5 Arborescent chain with two echelons: transit point with uncertain demand.

6.6 A two echelon supply chain in case of stochastic demand.

References.

7. Incentives in the supply chain.

7.1 Introduction.

7.2 Decisions on price: double marginalization.

7.3 Decision on price in a competitive environment.

7.4 Decision on inventories: the Newsvendor problem.

7.5 Decision on effort to produce and sell the product.

7.6 Concluding remarks.

References.

8. Vehicle Routing.

8.1 Network routing problems.

8.2 Solution methods for symmetric TSP.

8.3 Solution methods for basic VRP.

8.4 Additional features of real-life VRP.

8.5 Final remarks.

8.6 For further reading.

References.

Appendix A: A Quick Tour of Probability and Statistics.

A.1 Sample space, events, and probability.

A.2 Conditional probability and independence.

A.3 Discrete random variables.

A.4 Continuous random variables.

A.5 Jointly distributed random variables.

A.6 Independence, covariance, and conditional expectation.

A.7 Stochastic processes.

A.8 Parameter estimation.

A.9 Hypothesis testing.

the mean of two populations.

A.10 Simple linear regression.

A.11 For further reading.

References.

Appendix B: An even Quicker Tour in Mathematical Programming.

B.1 Role and limitations of optimization models.

B.2 Optimization models.

B.3 Convex sets and functions.

B.4 Nonlinear programming.

B.5 Linear programming.

B.6 Integer linear programming.

B.7 Elements of multi-objective optimization.

B.8 For further reading.

References.

Index.