Introduction to Distribution Logistics

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  • Format: Hardcover
  • Copyright: 2013-08-12
  • Publisher: Wiley-Interscience
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unique introduction to distribution logistics that focuses on both quantitative modeling and practical business issuesIntroduction to Distribution Logistics presents a complete and balanced treatment of distribution logistics by covering both applications and the required theoretical background, therefore extending its reach to practitioners and students in a range of disciplines such as management, engineering, mathematics, and statistics. The authors emphasize the variety and complexity of issues and sub-problems surrounding distribution logistics as well as the limitations and scope of applicability of the proposed quantitative tools. Throughout the book, readers are provided with the quantitative approaches needed to handle real-life management problems, and areas of study include:Supply chain managementNetwork design and transportationDemand forecastingInventory control in single- and multi-echelon systemsIncentives in the supply chainVehicle routingComplete with extensive appendices on probability and statistics as well as mathematical programming, Introduction to Distribution Logistics is a valuable text for distribution logistics courses at both the advanced undergraduate and beginning graduate levels in a variety of disciplines, and prior knowledge of production planning is not assumed. The book also serves as a useful reference for practitioners in the fields of applied mathematics and statistics, manufacturing engineering, business management, and operations research. The book's related Web site includes additional sections and numerical illustrations.

Author Biography

Paolo Brandimarte is Full Professor of Quantitative Methods for Finance and Logistics at Politecnico di Torino in Italy. He is the author of several publications, including six books, on the application of optimization and simulation to diverse areas such as production management, telecommunications, and finance. Dr. Brandimarte has extensive teaching experience in engineering and economics faculties, including master's- and PhD-level courses.

Giulio Zotteri, PhD, is Associate Professor in the School of Industrial Engineering at Politecnico di Torino in Italy. Dr. Zotteri's research interests include demand management and forecasting, inventory control, and operations management.

Table of Contents

Supply chain management
What do we mean by logistics?
Plan of the chapter
Structure of production/distribution networks
Competition factors, cost drivers, and strategy
Competition factors
Cost drivers
The role of inventories
A classical model: Economic Order Quantity
Cycle vs. capacity-induced stock
Dealing with uncertainty
Setting safety stocks
A two-stage decision process: Production planning in an assemble-to-order environment
Inventory deployment
Physical flows and transportation
Time horizons and hierarchical levels
Decision approaches
Information flows and decision rights
Quantitative models and methods
For further reading
Network Design and Transportation
The role of intermediate nodes in a distribution network
The risk pooling effect: reducing the uncertainty level
The role of transit points in transportation optimization
Location and flow optimization models
The transportation problem
The minimum cost flow problem
The plant location problem
Putting it all together
Models involving nonlinear costs
For Further Reading
The variable to be predicted
The forecasting process
Metrics for forecast errors
The Mean Error
Mean Absolute Deviation
Root Mean Square Error
Mean Percentage Error and Mean Absolute Percentage Error
U Theil's statistic
Using metrics of forecasting accuracy
A classification of forecasting methods
Moving Average
The demand model
The algorithm
Setting the parameters
Drawbacks and limitations
Simple exponential smoothing
The demand model
The algorithm
Setting the parameter
Drawbacks and limitations
Exponential Smoothing with Trend
The demand model
The algorithm
Setting the parameters
Drawbacks and limitations 125
Exponential smoothing with seasonality
The demand model
The algorithm
Setting the parameters
Drawbacks and limitations
Smoothing with seasonality and trend
The demand model
The algorithm
Simple linear regression
Setting up data for regression
Forecasting new products
The Delphi method and the committee process
Lancaster model: forecasting new products through products features
The early sales model
The Bass model
Limitations and drawbacks
Inventory management with Deterministic Demand
Economic Order Quantity
Robustness of EOQ model
Case of LT > 0: the (Q,R) model
Case of finite replenishment rate
Multi-item EOQ
The case of shared ordering costs
The multi-item case with a constraint on ordering capacity
Case of nonlinear costs
The case of variable demand with known variability
Inventory control: the stochastic case
The newsvendor problem
Extensions of the Newsvendor problem
Multi-period pro
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