Introduction to Distribution Logistics
by Brandimarte, Paolo; Zotteri, GiulioEdition: 1st
Format: Hardcover
Pub. Date: 2013-08-12
Publisher(s): Wiley-Interscience
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Summary
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
| Preface | |
| 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 | |
| Strategy | |
| 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 | |
| References | |
| 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 | |
| References | |
| Forecasting | |
| Introduction | |
| 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 | |
| ME%, MAD%, RMSE% | |
| 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 | |
| Initialization | |
| Drawbacks and limitations | |
| Exponential Smoothing with Trend | |
| The demand model | |
| The algorithm | |
| Setting the parameters | |
| Initialization | |
| Drawbacks and limitations 125 | |
| Exponential smoothing with seasonality | |
| The demand model | |
| The algorithm | |
| Setting the parameters | |
| Initialization | |
| Drawbacks and limitations | |
| Smoothing with seasonality and trend | |
| The demand model | |
| The algorithm | |
| Initialization | |
| 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 | |
| References | |
| Inventory management with Deterministic Demand | |
| Introduction | |
| 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 | |
| References | |
| Inventory control: the stochastic case | |
| Introduction | |
| The newsvendor problem | |
| Extensions of the Newsvendor problem | |
| Multi-period pro | |
| Table of Contents provided by Publisher. All Rights Reserved. |
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