Introduction to Logistics Systems Management E-Book

Gianpaolo Ghiani

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Wiley Series in Operations Research and Management Science
• Sprache: Englisch

Seitenzahl: 1005

Veröffentlichungsjahr: 2022

Beschreibung

INTRODUCTION TO LOGISTICS SYSTEMS MANAGEMENT The updated new edition of the award-winning introductory textbook on logistics system management Introduction to Logistics Systems Management provides an in-depth introduction to the methodological aspects of planning, organization, and control of logistics for organizations in the private, public and non-profit sectors. Based on the authors' extensive teaching, research, and industrial consulting experience, this classic textbook is used in universities worldwide to teach students the use of quantitative methods for solving complex logistics problems. Fully updated and revised, the third edition places increased emphasis on the complexity and flexibility required by modern logistics systems. In this context, the extensive use of data, descriptive analytics, predictive models, and optimization techniques will be invaluable to support the decisions and actions of logistics and supply chain managers. Throughout the book, brand-new case studies and numerical examples illustrate how various methods can be used in industrial and service logistics to reduce costs and improve service levels. The book: * includes new models and techniques that have emerged over the past decade; * describes methodologies for logistics decision making, forecasting, logistics system design, procurement, warehouse management, and freight transportation management; * includes end-of-chapter exercises, Microsoft¯® Excel¯® files and Python¯® computer codes for each algorithm covered; * includes access to a companion website with additional exercises, links to video tutorials, and supplementary teaching material. To facilitate creation of course material, additional LaTeX source data containing the formulae, optimization models, tables and algorithms described in the book is available to instructors. Introduction to Logistics Systems Management, Third Edition remains an essential textbook for senior undergraduate and graduate students in engineering, computer science, and management science courses. It is also a highly useful reference for academic researchers and industry practitioners alike.

Leseprobe

Wiley Series in OPERATIONS RESEARCH AND MANAGEMENT SCIENCE

Advisory Editors • Optimization ModelsLawrence V. SnyderLehigh UniversityYa-xiang YuanChinese Academy of Sciences

Founding Series EditorJames J. CochranLouisiana Tech University

Operations Research and Management Science (ORMS) is a broad, interdisciplinary branch of applied mathematics concerned with improving the quality of decisions and processes and is a major component of the global modern movement towards the use of advanced analytics in industry and scientific research. The Wiley Series in Operations Research and Management Science features a broad collection of books that meet the varied needs of researchers, practitioners, policy makers, and students who use or need to improve their use of analytics. Reflecting the wide range of current research within the ORMS community, the Series encompasses application, methodology, and theory and provides coverage of both classical and cutting edge ORMS concepts and developments. Written by recognized international experts in the field, this collection is appropriate for students as well as professionals from private and public sectors including industry, government, and nonprofit organization who are interested in ORMS at a technical level. The Series is comprised of three sections: Decision and Risk Analysis; Optimization Models; and Stochastic Models.

Introduction to Logistics Systems Management

With Microsoft®Excel® and Python® Examples

Gianpaolo GhianiUniversity of Salento, Italy

Gilbert LaporteHEC, Montréal, CanadaUniversity of Bath, United Kingdom

Roberto MusmannoUniversity of Calabria, Italy

Third Edition

This edition first published 2022

© 2022 John Wiley & Sons Ltd

Edition History

2e © 2013 John Wiley & Sons Ltd

1e © 2004 John Wiley & Sons Ltd

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A catalogue record for this book is available from the Library of Congress

Hardback ISBN: 9781119789390; ePub ISBN: 9781119789406; ePDF ISBN: 9781119789413

Cover image: Courtesy of Roberto MusmannoCover design by Wiley

Set in 9.5/12.5pt STIXTwoText by Integra Software Services Pvt. Ltd, Pondicherry, India

To Laura, Allegra and Vittoria

To Ann, Cathy and Xavier

To Maria Carmela, Francesco and Andrea

Contents

Cover

Series page

Title page

Copyright

Dedication

Foreword

Preface

Acknowledgements

About the Authors

List of Abbreviations

1 Introducing Logistics

1.1 Definition of Logistics

1.2 Logistics Systems

1.3 Supply Chains

1.3.1 Logistics Versus Supply Chain Management

1.3.2 A Taxonomy of Supply Chains

1.3.3 The Bullwhip Effect

1.4 Logistics Service Providers

1.5 Logistics in Service Organizations

1.5.1 Logistics in Solid Waste Management

1.5.2 Humanitarian Logistics

1.6 Case Studies

1.6.1 Apple

1.6.2 Adidas AG

1.6.3 Galbani

1.6.4 Pfizer

1.6.5 Amazon

1.6.6 FedEx

1.6.7 A.P. Moller-Maersk

1.6.8 Canadian Pacific Railway

1.7 Trends in Logistics

1.7.1 Reverse and Sustainable Logistics

1.7.2 E-commerce Logistics

1.7.3 City Logistics

1.8 Logistics Objectives and KPIs

1.8.1 Capital-related KPIs

1.8.2 Cost-related KPIs

1.8.3 Service Level-related KPIs

1.9 Logistics Management

1.9.1 Logistics Planning

1.9.2 Logistics Organizational Structures

1.9.3 Controlling

1.10 Data Analytics in Logistics

1.10.1 Descriptive Analytics

1.10.2 Predictive Analytics

1.10.3 Prescriptive Analytics

1.11 Segmentation Analysis

1.11.1 Customer Segmentation

1.11.2 Product Segmentation

1.12 Information Systems

1.13 Questions and Problems

2 Forecasting Logistics Data

2.1 Introduction

2.2 Qualitative Methods

2.3 Quantitative Methods

2.3.1 Explanatory Versus Extrapolation Methods

2.3.2 The Forecasting Process

2.4 Exploratory Data Analysis

2.4.1 The Univariate Case

2.4.2 Histograms

2.4.3 Boxplots

2.4.4 Time Series Plots

2.4.5 The Bivariate Case

2.4.6 Scatterplots

2.5 Data Preprocessing

2.5.1 Insertion of Missing Data

2.5.2 Detection of Outliers

2.5.3 Data Aggregation

2.5.4 Removing Calendar Variations

2.5.5 Deflating Monetary Time Series

2.5.6 Adjusting for Population Variations

2.5.7 Data Normalization

2.6 Classification of Time Series

2.7 Explanatory Methods

2.7.1 Forecasting with Regression

2.7.2 Multicollinearity

2.7.3 Categorical Predictors

2.7.4 Coefficient of Determination

2.7.5 Polynomial Regression

2.7.6 Linear–log, Log–linear and Log–log Regression Models

2.7.7 Underfitting and Overfitting

2.7.8 Forecasting with Machine Learning

2.8 Extrapolation Methods

2.8.1 Notation

2.8.2 Decomposition Method

2.8.3 Further Extrapolation Methods: the Constant-trend Case

2.8.4 Further Extrapolation Methods: the Linear-trend Case

2.8.5 Further Extrapolation Methods: the Seasonality Case

2.8.6 Further Extrapolation Methods: the Irregular Time Series Case

2.8.7 Further Extrapolation Methods: the Intermittent Time Series Case

2.9 Accuracy Measures

2.9.1 Calibration of the Parametrized Forecasting Methods

2.9.2 Selection of the Most Accurate Forecasting Method

2.10 Forecasting Control

2.10.1 Tracking Signal

2.10.2 Control Charts

2.11 Interval Forecasts

2.12 Case Study: Sales Forecasting at Shivoham

2.13 Case Study: Sales Forecasting at Orlea

2.14 Questions and Problems

3 Designing the Logistics Network

3.1 Introduction

3.2 Classification of Logistics Network Design Problems

3.3 The Number of Facilities in a Logistics System

3.4 Qualitative Versus Quantitative Location Methods

3.5 The Weighted Scoring Method

3.6 The Analytical Hierarchy Process

3.7 Single-commodity One-echelon Continuous Location Problems

3.8 Single-commodity Two-echelon Continuous Location Problems

3.9 Single-commodity One-echelon Discrete Location Problems

3.10 Single-commodity Two-echelon Discrete Location Problems

3.11 The Multi-commodity Case

3.12 Location-covering Problems

3.13 p-centre Problems

3.14 Data Aggregation

3.15 Location Models Under Uncertainty

3.15.1 A Stochastic Location–allocation Model

3.15.2 A Location-routing Model with Uncertain Demand

3.16 Case Study: Intermodal Container Depot Location at Hardcastle

3.17 Case Study: Location–Allocation Decisions at the Italian National Transplant Centre

3.18 Questions and Problems

4 Selecting the Suppliers

4.1 Introduction

4.2 Definition of the Set of Potential Suppliers

4.3 Definition of the Selection Criteria

4.4 Supplier Selection

4.5 Supplier Relationship Management Software

4.6 Case Study: the System for the Selection of Suppliers at Baxter

4.7 Case Study: the Supplier Selection at Onokar

4.8 Questions and Problems

5 Managing a Warehouse

5.1 Introduction

5.1.1 Warehouse Operations

5.1.2 Warehouse Functional Zones

5.1.3 Advantages of Warehousing

5.2 Types of Warehouses

5.2.1 Classification with Respect to the Position in the Logistics System

5.2.2 Classification with Respect to Ownership

5.2.3 Classification with Respect to Climate-control

5.2.4 Classification with Respect to the Level of Automation

5.3 Warehousing Costs

5.4 Unit Loads

5.4.1 Freight Classification

5.4.2 Unit Loads and Stock Keeping Units

5.4.3 Packaging

5.4.4 Palletized Unit Loads

5.4.5 Containerized Unit Loads

5.5 Storage Systems

5.5.1 Block Stacking

5.5.2 Pallet Racks

5.5.3 Shelves

5.5.4 Cabinet and Carousel Systems

5.6 Internal Transportation Systems

5.6.1 Manual Handling and Non-autonomous Vehicles

5.6.2 Automated Guided Vehicles

5.6.3 Stacker Cranes

5.6.4 Conveyors

5.7 Product Identification Systems

5.7.1 SKU Codes

5.7.2 Global Trade Item Numbers

5.7.3 Barcodes

5.7.4 QR Codes

5.7.5 Logistic Labels

5.7.6 Radio-frequency Identification

5.8 Warehouse Performance Measures

5.9 Warehouse Management Systems

5.10 Warehouse Design

5.10.1 Internal Transportation Technology Selection

5.10.2 Layout Design

5.10.3 Sizing of the Storage Zone

5.10.4 Sizing of the Receiving and Shipping Zones

5.10.5 Sizing of an AS/RS

5.10.6 Sizing a Vehicle-based Internal Transportation System

5.11 Storage Space Allocation

5.12 Inventory Management

5.12.1 Deterministic models

5.12.2 Stochastic Models

5.12.3 Selecting an Inventory Policy

5.12.4 Multiproduct Inventory Models

5.13 Crossdock Door Assignment Problem

5.14 Put-away and Order Picking Optimization

5.14.1 Parts-to-picker Systems

5.14.2 Picker-to-parts and AGV-based Systems

5.15 Load Consolidation

5.15.1 One-dimensional Bin Packing Problems

5.15.2 Two-dimensional Bin Packing Problems

5.15.3 Three-dimensional Bin Packing Problems

5.16 Case Study: Inventory Management at Wolferine

5.17 Case Study: Airplane Loading at FedEx

5.18 Questions and problems

6 Managing Freight Transportation

6.1 Introduction

6.2 Transportation Modes

6.2.1 Road Transportation

6.2.2 Water Transportation

6.2.3 Rail Transportation

6.2.4 Air Transportation

6.2.5 Pipeline Transportation

6.2.6 Intermodal Transportation

6.2.7 Comparison Among Transportation Modes

6.3 Freight Transportation Terminals

6.3.1 Port Terminals

6.3.2 Air Cargo Terminals

6.3.3 Rail Freight Terminals

6.3.4 Road Freight Terminals

6.4 Classification of Freight Transportation Management Problems

6.4.1 Long-haul Freight Transportation Management

6.4.2 Freight Transportation Terminal Management

6.4.3 Short-haul Freight Transportation Management

6.5 Transportation Management Systems

6.6 Freight Traffic Assignment Problems

6.6.1 Minimum-cost Flow Formulation

6.6.2 Linear Single-commodity Minimum-cost Flow Problems

6.6.3 Linear Multi-commodity Minimum-cost Flow Problems

6.7 Service Network Design Problems

6.8 Vehicle Allocation Problems

6.9 A Dynamic Driver Assignment Problem

6.10 Vehicle Fleet Composition

6.11 Shipment Consolidation

6.12 Vehicle Routing Problems

6.12.1 The Travelling Salesman Problem

6.12.2 The Node Routing Problem with Operational Constraints

6.12.3 The Node Routing and Scheduling Problem with Time Windows

6.12.4 Arc Routing Problems

6.12.5 Route Sequencing

6.13 Real-time Vehicle Routing Problems

6.14 Integrated Location and Routing Problems

6.15 Inventory Routing Problems

6.16 Case Study: Air Network Design at Intexpress

6.17 Case Study: Dynamic Vehicle-dispatching Problem with Pickups and Deliveries at eCourier

6.18 Questions and Problems

Index

End User License Agreement

List of Tables

Chapter 01

Table 1.1 Geographical distribution of the...

Table 1.2 Features of some Pfizer...

Table 1.3 Timetable of the Maersk...

Table 1.4 Incidence of different cost...

Table 1.5 Annual estimate of sales...

Table 1.6 Historical assembly and transportation...

Table 1.7 Observed transportation times (in...

Table 1.8 List of performance measures...

Table 1.9 Performance measures for the...

Table 1.10 Normalized performance measures for...

Table 1.11 Performance measures for the...

Table 1.12 Profit (in €) corresponding...

Table 1.13 Values of profit (in...

Table 1.14 Expected value and standard...

Table 1.15 Production capacity, and overall...

Table 1.16 Demand, buying price, and...

Table 1.17 List of 10 parcels...

Table 1.18 Monthly volume and unit...

Table 1.19 Monthly costs of different...

Table 1.20 Annual revenue, average annual...

Table 1.21 ABC classification of the Blucker products

Table 1.22 cross analysis

Table 1.23 cross analysis of the Blucker products

Table 1.24 Observed data related to...

Table 1.25 Performance measures used by...

Table 1.26 Performance measure values for...

Table 1.27 Profit (in k€...

Table 1.28 Annual revenue (in €...

Table 1.29 Weekly quantity sold and...

Table 1.30 Monthly sales and average...

Chapter 02

Table 2.1 Main forecasts required by...

Table 2.2 Features of the main...

Table 2.3 Extract of the Elextronix...

Table 2.4 Measures of location and...

Table 2.5 Dataset used in the...

Table 2.6 Number of cars sold...

Table 2.7 Number of smart LED...

Table 2.8 Mean (in thousands of...

Table 2.9 Number of Sidol75 packages...

Table 2.10 Number of Sidol75 packages...

Table 2.11 Fresh orange consumption (in...

Table 2.12 Modified time series obtained...

Table 2.13 Annual Cavis sales and...

Table 2.14 Price index and annual...

Table 2.15 Annual number of Salus...

Table 2.16 Adjustment of the number...

Table 2.17 Sotam time series normalized...

Table 2.18 Total production and logistics...

Table 2.19 Combined trend-cyclical component...

Table 2.20 Trend

q

t

...

Table 2.21 Combined seasonal-random index...

Table 2.22 Seasonal index...

Table 2.23 Random index...

Table 2.24 Demand forecast...

Table 2.25 Weekly sales (in number...

Table 2.26 Forecasts of the weekly...

Table 2.27 Monthly administrative cost (in...

Table 2.28 Parameters

a

t

...

Table 2.29 Monthly air conditioning systems sold by Elna

Table 2.30 Time series

s

t

,...

Table 2.31 Time series

a

t

...

Table 2.32 Times series

s

t

,...

Table 2.33 Elna monthly...

Table 2.34 Time series...

Table 2.35 Parameters

a

t

...

Table 2.36 Time series of normalized...

Table 2.37 Forecasts over the next...

Table 2.38 Daily sales (in NZ$)...

Table 2.39 Time series...

Table 2.40 Daily sales of dog breed food...

Table 2.41 Time series...

Table 2.42 Auxiliary time series...

Table 2.43 Monthly sales of the...

Table 2.44 Time series...

Table 2.45 Seasonal indices...

Table 2.46 Parameters

a

t

...

Table 2.47 Normalized seasonal indices...

Table 2.48 Forecasts of the sales...

Table 2.49 Evaluation of the quality...

Table 2.50 MAE10(α) for the...

Table 2.51 Forecasting errors in the...

Table 2.52 Accuracy measures of the...

Table 2.53 Monthly sales of sportswear...

Table 2.54 Monthly sales of sportswear...

Table 2.55 Unit production costs, forecasts...

Table 2.56 Number of batteries sold...

Table 2.57 Car sales (number) in...

Table 2.58 Number of Hot Spot...

Table 2.59 Data for the forecasting...

Table 2.60 Aqua-Floor sales (in...

Table 2.61 Estimate of the annual...

Table 2.62 Number of 800xp slot...

Table 2.63 Time series

W

...

Table 2.64 Number of M5 trucks sold by...

Table 2.65 M5 sales improvement (in...

Table 2.66 Number of XC2100 printers...

Table 2.67 Forecasting errors...

Table 2.68 Monthly demand for lemons...

Table 2.69 Annual Juice sales and...

Table 2.70 Aldes exports (in $) of...

Table 2.71 Time series...

Table 2.72 Annual sales and number of...

Table 2.73 Quantity (in thousands of...

Table 2.74 Sales (in kl) of...

Table 2.75 Number of vans daily...

Table 2.76 Number of packages (three...

Chapter 03

Table 3.1 Weights associated with location...

Table 3.2 Scores received by the...

Table 3.3 Conversion scale of judgements...

Table 3.4

RI

values for different dimensions...

Table 3.5 Cartesian coordinates and average...

Table 3.6 Sequence of the water...

Table 3.7 Cartesian coordinates and average...

Table 3.8 Annual logistics costs (in...

Table 3.9 Cartesian coordinates (in km...

Table 3.10 Cartesian coordinates of coffee...

Table 3.11 Distances (in km) between...

Table 3.12 Fraction of the daily...

Table 3.13 Fraction of the daily...

Table 3.14 Fraction of the daily...

Table 3.15 Distances (in km) between...

Table 3.16 Fraction of the daily...

Table 3.17 Distances (in km) between...

Table 3.18 Average daily quantity (in...

Table 3.19 Average daily demand (in...

Table 3.20 Distances (in km) between...

Table 3.21 Average daily amount (in...

Table 3.22 Transportation costs (in €...

Table 3.23 Transportation costs (in €...

Table 3.24 Distances (in km) between...

Table 3.25 Minimum distances (in m...

Table 3.26 Vertices of the La...

Table 3.27 Traversal time (in minutes...

Table 3.28 Travel times (in minutes...

Table 3.29 Position of local centre...

Table 3.30 Average daily demand scenarios...

Table 3.31 Demand allocation scenarios in...

Table 3.32 Expected demand level (in...

Table 3.33 Summary of the results...

Table 3.34 Weights associated with the...

Table 3.35 Scores of the location...

Table 3.36 Cartesian coordinates and average...

Table 3.37 Cartesian coordinates and plastic...

Table 3.38 Cartesian coordinates of the...

Table 3.39 Distances (in km) between...

Table 3.40 Distances (in km) between...

Table 3.41 Daily fixed costs and...

Table 3.42 Demands (in hl per...

Table 3.43 Traversal time (in minutes...

Chapter 05

Table 5.1 Annual average labour expense...

Table 5.2 Main features of 20...

Table 5.3 Comparison of barcodes and...

Table 5.4 Parameters used for computing...

Table 5.5 Inventory levels of the...

Table 5.6 Features of the most...

Table 5.7 Order sizes and safety...

Table 5.8 SKU categories in the...

Table 5.9 Features of the SKUs...

Table 5.10 Distance between storage locations...

Table 5.11 Distance between storage locations...

Table 5.12 Optimal assignment of the...

Table 5.13

a

j

,

j

=1,..., 5,...

Table 5.14 Values of...

Table 5.15 Optimal assignment of SKUs...

Table 5.16 Daily sales and inventory...

Table 5.17 Features of the spare...

Table 5.18 classification of the spare...

Table 5.19 Distances (in m) between...

Table 5.20 Number of palletized unit...

Table 5.21 Weight of the parcels...

Table 5.22 Sorted list of parcels...

Table 5.23 Parcel-to-trip allocation...

Table 5.24 Features of the parcels...

Table 5.25 Parcels loaded at McMillan...

Table 5.26 Characteristics of the vehicles...

Table 5.27 Length and weight of...

Table 5.28 Section allocation to vehicles...

Table 5.29 Items to be loaded...

Table 5.30 Coordinates of the south...

Table 5.31 Technical features of the...

Table 5.32 Order size and safety...

Table 5.33 Features of the SKUs...

Table 5.34 Distance between the storage...

Table 5.35 Picking list in the...

Table 5.36 Euclidean distances (in m...

Table 5.37 Unit loads and the...

Table 5.38 Unit loads and the...

Table 5.39 List of the parcels...

Table 5.40 Dimensions of the items...

Chapter 04

Table 4.1 Supplier selection criteria according...

Table 4.2 Selection criteria for suppliers...

Table 4.3 Green selection criteria for...

Table 4.4 Score of the Ilax...

Table 4.5 Values associated with a...

Table 4.6 Potential suppliers and absolute...

Table 4.7 Performance and prices of...

Table 4.8 Evaluation grid of five...

Table 4.9 Evaluation criteria, weights and...

Table 4.10 Weekly capacity and unit...

Table 4.11 Score, supply risk, capacity...

Table 4.12 Minimum and maximum daily...

Table 4.13 Unit prices (in £...

Chapter 06

Table 6.1. Capacity of sample vehicles...

Table 6.2. LTL rates ($ per 100...

Table 6.3. Ramp space required for...

Table 6.4. Unit transportation costs (in...

Table 6.5. Demand (in tonnes), maximum...

Table 6.6. Average daily demand of...

Table 6.7. Variable costs and fixed...

Table 6.8. Travel times (in number...

Table 6.9. Distance (in miles) between...

Table 6.10. Optimal driver assignment of...

Table 6.11. Weekly number of vans...

Table 6.12. Annual transportation costs (in...

Table 6.13. Details of the orders...

Table 6.14. Routes of the owned...

Table 6.15. Optimal solution of the...

Table 6.16. Shortest path length (in...

Table 6.17. Distances (in km) between...

Table 6.18. Orders (in hectolitres) received...

Table 6.19. Distance (in km) between...

Table 6.20. Feasible routes in the...

Table 6.21. Order quantity received by...

Table 6.22. Feasible routes in the...

Table 6.23. Cost savings determined for...

Table 6.24. Travel times...

Table 6.25. Time windows for the...

Table 6.26. Number of required palletized...

Table 6.27. Fitness values...

Table 6.28. Fitness values...

Table 6.29. Fitness values...

Table 6.30. Schedule of the first...

Table 6.31. Schedule of the second...

Table 6.32. Schedule of the third...

Table 6.33. Daily truck service route...

Table 6.34. Logistics costs corresponding to...

Table 6.35. Transportation costs (in €...

Table 6.36. Tank capacity and the...

Table 6.37. Distances (in km) between...

Table 6.38. Initial inventory level, storage...

Table 6.39. Delivered quantities (in quintals...

Table 6.40. Inventory levels (in quintals...

Table 6.54. Distances (in km) between...

Table 6.41. JKL transportation rates for...

Table 6.42. Forecast transportation demand of...

Table 6.43. Forecast transportation demand of...

Table 6.44. Distances (in km) between...

Table 6.45. Date of departure and...

Table 6.46. Ranking value driver–...

Table 6.47. Travel time measures (in...

Table 6.48. Costs associated with the...

Table 6.49. Distances (in km) between...

Table 6.50. Daily average amount of...

Table 6.51. Distances (in km) between...

Table 6.52. Time windows for the...

Table 6.53. Travel times...

List of Illustrations

Chapter 01

Figure 1.1 Example of a...

Figure 1.2 Network representation of...

Figure 1.3 Supply chain taxonomy...

Figure 1.4 Supply chain of...

Figure 1.5 Logistics in a...

Figure 1.6 Apple iPhone supply...

Figure 1.7 Location of the...

Figure 1.8 Markets for Pfizer...

Figure 1.9 Pfizer supply chain...

Figure 1.10 Amazon’s...

Figure 1.11 FedEx’s...

Figure 1.12 Maersk’s...

Figure 1.13 Maersk’s...

Figure 1.14 Canadian Pacific Railway...

Figure 1.15 Graph representation of...

Figure 1.16 Magnitude of logistics...

Figure 1.17 Plot of the...

Figure 1.18 Functional-type organizational...

Figure 1.19 Functional-type organizational...

Figure 1.20 Divisional-type organizational...

Figure 1.21 Elifly matrix organizational...

Figure 1.22 Weekly observations measuring...

Figure 1.23 Control panel by...

Figure 1.24 Taxonomy of analytics...

Figure 1.25 Graphical illustration of...

Figure 1.26 Representation of the...

Figure 1.27 80–20...

Figure 1.28 Interaction of ERP...

Figure 1.29 Costs and service...

Chapter 02

Figure 2.1 Density histogram of...

Figure 2.2 Boxplots of the...

Figure 2.3 Plot of the...

Figure 2.4 A 2D scatterplot...

Figure 2.5 A 2D scatterplot...

Figure 2.7 Plot of an...

Figure 2.8 Plot of a...

Figure 2.9 Plot of a...

Figure 2.10 Plot of an...

Figure 2.11 Life cycle of...

Figure 2.12 3D scatterplot of...

Figure 2.13 Scatterplot of the...

Figure 2.14 Scatterplot of the...

Figure 2.15 Scatterplot of the...

Figure 2.16 Scatterplot of the...

Figure 2.17 Scatterplot of the...

Figure 2.18 An artificial neuron...

Figure 2.19 Two activation functions...

Figure 2.20 Other activation functions...

Figure 2.21 Scatterplot of the...

Figure 2.22 Single-layer feedforward...

Figure 2.23 Single-layer feedforward...

Figure 2.24 3D scatterplot of...

Figure 2.25 Plot of the...

Figure 2.26 Plot of the...

Figure 2.27 Linear trend (in...

Figure 2.28 Plot of the...

Figure 2.29 Plot of the...

Figure 2.30 Plot of the...

Figure 2.31 Plot of the...

Figure 2.33 Plot of the...

Figure 2.32 Plot of the...

Figure 2.34 Plot of the...

Figure 2.35 Plot of...

Figure 2.36 Plot of...

Figure 2.37 Plot of...

Figure 2.38 Plot of...

Figure 2.39 Plot of the...

Figure 2.40 Plot of the...

Figure 2.41 Plot of monthly...

Figure 2.42 Plot of the...

Figure 2.43 Plot of the...

Figure 2.44 Monthly sales of...

Figure 2.45 Plot of the...

Figure 2.46 Plot of the...

Figure 2.47 Visual examination of...

Figure 2.48 Plot of the...

Figure 2.49 Plot of the...

Chapter 03

Figure 3.1 An example of...

Figure 3.2 The optimal location...

Figure 3.3 A logistics system...

Figure 3.4 Transportation, inventory, facility...

Figure 3.5 Location of villages...

Figure 3.6 Location of the...

Figure 3.7 Location of the...

Figure 3.8 Representation of the...

Figure 3.9 Plot of a...

Figure 3.10 LB and UB...

Figure 3.11 LB, UB,...

Figure 3.12 Computation of the...

Figure 3.13 Travel time...

Figure 3.14 Determination of the...

Figure 3.15 Location problem in...

Figure 3.16 Time...

Figure 3.17 Freight consolidation at...

Figure 3.18 Transfer of containers...

Figure 3.19 Location of the...

Chapter 04

Figure 4.1 The Kraljic matrix...

Figure 4.2 Hierarchical diagram of...

Chapter 05

Figure 5.1 Warehouse inbound and...

Figure 5.2 Main functional zones...

Figure 5.3 A crossdock.

Figure 5.4 Typical breakdown of...

Figure 5.5 An example of...

Figure 5.6 (a) Palletized unit...

Figure 5.7 (a) An EPAL...

Figure 5.8 Two ways of...

Figure 5.9 An intermodal container...

Figure 5.10 Loading of one...

Figure 5.11 Block stacking of...

Figure 5.12 Pallet racks. Reproduced...

Figure 5.13 Racks with narrow...

Figure 5.14 Racks with large...

Figure 5.15 (a) Selective racks...

Figure 5.16 Drive-through racks...

Figure 5.17 (a) Replenishment and...

Figure 5.18 A cantilever rack...

Figure 5.19 Live pallet racks...

Figure 5.20 Sliding racks. Reproduced...

Figure 5.21 (a) Live shelves...

Figure 5.22 Multi-level shelving...

Figure 5.23 (a) Vertical storage...

Figure 5.24 Hand trucks with...

Figure 5.25 (a) Hand-pallet...

Figure 5.26 Stand-on pallet...

Figure 5.27 (a) Forklift truck...

Figure 5.28 (a) Low-tier...

Figure 5.29 An AGV. Reproduced...

Figure 5.30 A fleet of...

Figure 5.31 (a) A stacker...

Figure 5.32 A circuit of...

Figure 5.33 (a) SKU code...

Figure 5.34 (a) Laser barcode...

Figure 5.35 An example of...

Figure 5.36 (a) RFID tag...

Figure 5.37 (a) RFID hand...

Figure 5.38 The Conkret warehouse...

Figure 5.39 Architecture of a...

Figure 5.40 Common crossdock layouts...

Figure 5.41 An -shaped crossdock...

Figure 5.42 Flow-through warehouse...

Figure 5.43 -shaped warehouse layout...

Figure 5.44 -shaped warehouse layout...

Figure 5.45 -shaped layout of...

Figure 5.46 Fishbone layout of...

Figure 5.47 Inventory level of...

Figure 5.48 Inventory level of...

Figure 5.49 Warehouse layout chosen...

Figure 5.50 Storage zone of...

Figure 5.51 (a) A single...

Figure 5.52 Storage zone of...

Figure 5.53 Inventory level as...

Figure 5.54 Inventory level as...

Figure 5.55 Average costs as...

Figure 5.56 Inventory level as...

Figure 5.57 Inventory level as...

Figure 5.58 Reorder point....

Figure 5.59 Plot of the production...

Figure 5.60 Plot of the...

Figure 5.61 Reorder point policy...

Figure 5.62 Reorder cycle policy...

Figure 5.63 (s, S) policy...

Figure 5.64 Inventory level as...

Figure 5.65 Layout of a...

Figure 5.66 Graph...

Figure 5.67 Routing of a picker in...

Figure 5.68 Layout of the...

Figure 5.69 Picking route in...

Figure 5.70 Computation of the...

Figure 5.71 Picking route in...

Figure 5.72 Determination of routes...

Figure 5.73 Picking route in...

Figure 5.74 Picking route in...

Figure 5.75 Layers of items...

Figure 5.76 Parcels loaded into...

Figure 5.77 Sections generated at...

Figure 5.78 Examples of extreme...

Figure 5.79 Vertices (open circles...

Figure 5.80 Loading item 9...

Figure 5.81 Loading item 1...

Figure 5.82 Full loading plan...

Figure 5.83 Percentage of success...

Figure 5.84 Decoor storage zone...

Figure 5.85 Inventory level (in...

Figure 5.86 Inventory level (in...

Figure 5.87 Inventory level (in...

Figure 5.88 Inventory level (in...

Figure 5.89 Storage zone layout...

Figure 5.90 Pallet rack of...

Figure 5.91 Storage zone layout...

Chapter 06

Figure 6.1 (a) TL and...

Figure 6.2 Hub-and-spoke...

Figure 6.3 Main long combination...

Figure 6.4 A typical intra...

Figure 6.5 Fuel consumption (in...

Figure 6.6 (a) Panamax vessel...

Figure 6.7 Some of the...

Figure 6.8 A cargo plane...

Figure 6.9 The Druzhba pipeline...

Figure 6.10 Cost of road...

Figure 6.11 Transportation rates for...

Figure 6.12 Layout of a...

Figure 6.13 (a) Quay crane...

Figure 6.14 Layout of an...

Figure 6.15 Layout of a...

Figure 6.16 Freight delivery routes...

Figure 6.17 Pickup or delivery...

Figure 6.18 A static representation...

Figure 6.19 Dynamic network representation...

Figure 6.20 A spanning tree...

Figure 6.21 Dummy directed graph...

Figure 6.22 Graph representation of...

Figure 6.23 Optimal solution of...

Figure 6.24 Graph representation of...

Figure 6.25 Graph representation of...

Figure 6.26 Two alternative service...

Figure 6.27 Graph representation of...

Figure 6.28 An example of...

Figure 6.29 A road network...

Figure 6.30 (a) A mixed...

Figure 6.31 Vehicle routing with...

Figure 6.32 A graph representation...

Figure 6.33 A graph representation...

Figure 6.34 A graph representation...

Figure 6.35 Feasible Hamiltonian circuit...

Figure 6.36 Minimum-cost spanning...

Figure 6.37 Feasible Hamiltonian cycle...

Figure 6.38 Eulerian undirected multi...

Figure 6.39 Hamiltonian cycle provided...

Figure 6.40 A feasible 3...

Figure 6.41 Hamiltonian cycle for...

Figure 6.42 Optimal solution of...

Figure 6.43 Feasible solution of...

Figure 6.44 Infeasible Hamiltonian cycle...

Figure 6.45 Computation of saving...

Figure 6.46 Merging (a) two...

Figure 6.47 Solution provided by...

Figure 6.48 A mixed Eulerian...

Figure 6.49 A directed graph...

Figure 6.50 Bipartite directed graph...

Figure 6.51 Least-cost Eulerian...

Figure 6.52 Graph representation used...

Figure 6.53 Least-cost Eulerian...

Figure 6.54 A mixed graph...

Figure 6.55 Connected components induced...

Figure 6.56 Least-cost Eulerian...

Figure 6.57 Directed graph...

Figure 6.59 Directed graph...

Figure 6.60 Undirected graph...

Figure 6.61 Even and connected...

Figure 6.62 Optimal vehicle routes...

Figure 6.63 Vehicle routes serving...

Figure 6.64 Possible freight routes...

Figure 6.65 Multi-graph representation...

Guide

Cover

Series page

Title page

Copyright

Dedication

Table of Contents

Foreword

Preface

Acknowledgments

About the Authors

List of Abbreviations

Begin Reading

Index

End User License Agreement

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Foreword

Logistics is concerned with the organization, movement, and storage of material and people. The term logistics was first used by the military to describe the activities associated with maintaining a fighting force in the field and, in its narrowest sense, describes the housing of troops. Over the years the meaning of the term has been gradually generalized to cover business and service activities. The domain of logistics activities is providing the customers of the system with the right product, in the right place, at the right time. This ranges from providing the necessary subcomponents for manufacturing, having inventory on the shelf of a retailer, to having the right amount and type of blood available for hospital surgeries. A fundamental characteristic of logistics is its holistic, integrated view of all the activities that it encompasses. So, while procurement, warehouse management and distribution are all important components, logistics is concerned with the integration of these and other activities to provide the time and space value to the system or corporation.

Excess global capacity in most types of industry has generated intense competition. At the same time, the availability of alternative products has created a very demanding type of customer, who insists on the instantaneous availability of a continuous stream of new models. So the providers of logistics activities are asked to do more transactions, in smaller quantities, in less time, for less cost, and with greater accuracy. New trends such as mass customization will only intensify these demands. The accelerated pace and greater scope of logistics operations has made planning-as-usual impossible.

Even with the increased number and speed of activities, the annual expenses associated with logistics activities in the United States have held constant for the last several years, around ten per cent of the Gross Domestic Product. Given the significant amounts of money involved and the increased operational requirements, the management of logistics systems has gained widespread attention from practitioners and academic researchers alike. To maximize the value in a logistics system, a large variety of planning decisions has to be made, ranging from the simple warehouse-floor choice of which item to pick next to fulfill a customer order to the corporate-level decision to build a new manufacturing plant. Logistics management supports the full range of those decisions related to the design and operation of logistics systems.

There exists a vast amount of literature, software packages, decision support tools and design algorithms that focus on isolated components of the logistics system or isolated planning in the logistics systems. In the last two decades, several companies have developed enterprise resource planning (ERP) systems in response to the need of global corporations to plan their entire supply chain. In their initial implementations, the ERP systems were primarily used for the recording of transactions rather than for the planning of resources on an enterprise-wide scale. Their main advantage was to provide consistent, up-to-date and accessible data to the enterprise. In recent years, the original ERP systems have been extended with advanced planning systems (APS). The main function of APS is for the first time the planning of enterprise-wide resources and actions. This implies a coordination of the plans among several organizations and geographically dispersed locations.

So, while logistics management requires an integrated, holistic approach, their treatment in courses and books tends to be either integrated and qualitative or mathematical and very specific. This book bridges the gap between those two approaches. It provides a comprehensive and modelling-based treatment of the logistics processes. The major components of logistics systems—storage and distribution—are each examined in detail. For each topic the problem is defined, models and solution algorithms are presented that support computer-assisted decision-making, and numerous application examples are provided. Each chapter is concluded with case studies that illustrate the application of the models and algorithms in practice. Because of its rigorous mathematical treatment of real-world management problems in logistics, the book will provide a valuable resource to graduate and senior undergraduate students and practitioners who are trying to improve logistics operations and satisfy their customers.

Marc Goetschalckx

Georgia Institute of Technology

Atlanta, United States

Preface

In the last few decades, and in particular during the pandemic and post-pandemic eras, logistics has become pivotal in the global economy. The exponential growth of e-commerce has forced companies to manage multi-channel strategies to supply their markets. The “new normal” requires that consumer requirements at multiple locations be met in a timely fashion with a variety of transportation modes. This level of service can be accomplished by setting up and managing complex and flexible logistics systems that can adapt easily and economically to unexpected circumstances. Such complexity and flexibility is made possible by advanced information technology solutions, capable of ensuring the sustainability of logistics processes as well as their efficiency. In this context, the extensive use of data, descriptive analytics, predictive models and optimization techniques prove to be invaluable to assist the decisions and actions of logistics and supply chain managers.

This book grew out of a number of undergraduate and graduate courses on logistics that we have taught to engineering, computer science and management science students. The goal of these courses is to give students a solid understanding of the analytical tools necessary to reduce costs and improve service level in logistics systems. The lack of a suitable book had forced us in the past to make use of a number of monographs and scientific papers which tend to be beyond the level of most students. We therefore committed ourselves to developing a quantitative book written at a level accessible to most students.

In 2004 we published with Wiley a book entitled “Introduction to Logistics Systems Planning and Control”, which was widely used in several universities around the world. The 2004 edition of the book received the “Roger-Charbonneau” award from HEC Montréal, as the best pedagogical book of the year. In 2013 we published a revised edition entitled “Introduction to Logistics Systems Management”, in which more emphasis was put on the organizational context in which logistics systems operate. After the success of the first two editions, both in terms of number of copies sold and the very positive feedback received from readers, we accepted Wiley’s invitation to prepare the current third edition entitled “Introduction to Logistics Systems Management, with Excel and Python”.

This edition deeply revises the content of the two previous editions in several respects. First, it covers new organizational concepts and techniques that have recently emerged in the field of logistics and were not covered in the previous editions. In addition, new numerical examples and problems have been added to each chapter. A further novelty is that their solutions are illustrated in great detail by using a spreadsheet in Microsoft Excel and a Python code, giving the reader the opportunity to replicate step by step the modelling and solution approach adopted in the book. In this way, readers can verify their understanding of each concept before moving on to the next one.

The book targets both academic and practitioner audiences. On the academic side, it should be appropriate for advanced undergraduate and graduate courses in logistics and supply chain management. It should also serve as a methodological reference for consultants and industry practitioners. We make the assumption that the reader is familiar with the basics of Operations Research and Statistics, and we provide a balanced treatment of forecasting, logistics system design, procurement, warehouse management, and freight transportation management.

Gianpaolo Ghiani ([email protected])

Gilbert Laporte ([email protected])

Roberto Musmanno ([email protected])

Teaching Material and Website

The book’s website,

http://www.wiley.com/go/logistics_systems_management3e,

completely renewed, provides readers, among others, with:

the

Microsoft Excel

spreadsheets and

Python

source files used in the solution of the problems presented in the boxes;

the data of the numerical problems proposed at the end of each chapter.

For the latter problems, instructors can obtain, upon request from the authors, the Microsoft Excel spreadsheets and the Python source files used in their solution. They can also get the LaTeX source files containing the formulae, optimization models, tables and algorithms described in the book, as well as the pdf files of the figures. In this way, they can easily compose their own LaTeX course slides.

Acknowledgements

The publication of this book was made possible in part thanks to the financial contribution of the Laboratory of Technologies for Simulation and Optimization (TESEO), Department of Mechanical, Energy and Management Engineering, University of Calabria.

The authors wish to acknowledge the reviewers and all the individuals who have helped to produce this book, in particular Annarita De Maio for her scientific and technical support.

About the Authors

Gianpaolo Ghiani is Professor of Operations Research at University of Salento (Italy), where he teaches Automated Planning, Decision Support Systems, Business Analytics, and Logistics courses at the Department of Engineering for Innovation.

Gilbert Laporte is professor emeritus at HEC Montréal (Canada), professor at the University of Bath (United Kingdom), and adjunct professor at Molde University College (Norway). His main research interests lie in the field of Distribution Management.

Roberto Musmanno is Professor of Operations Research at University of Calabria (Italy), where he teaches, among others, a Logistics course at the Department of Mechanical, Energy and Management Engineering.

List of Abbreviations

1-BP

one-bin packing

1PL

first-party logistics

2-BP

two-bin packing

2PL

second-party logistics

3-BP

three-bin packing

3PL

third-party logistics

4PL

fourth-party logistics

5PL

fifth-party logistics

AGV

automated guided vehicle

AH

air hub

AHP

analytical hierarchy process

ANN

artificial neural network

AP

assignment problem

APM

A.P. Moller-Maersk

ARP

arc routing problem

AS/RS

automated storage and retrieval system

ASIN

Amazon standard identification number

ATO

assembly to order

ATSP

asymmetric travelling salesman problem

B2B

business to business

B2C

business to consumer

BF

best fit

BFD

best-fit decreasing

BFGS

Broyden–Fletcher–Goldfarb–Shanno

C2C

cash-to-cash

CDC

central distribution centre

CL

carload

CL-NRP

node routing problem with capacity and length constraints

C-NRP

node routing problem with capacity constraints

COFC

container on flatcar

COT

cut-off time

CPFR

collaborative forecasting and replenishment program

CPL

capacitated plant location

CPP

Chinese postman problem

CPR

Canadian Pacific Railway

CRM

customer relationship management

CRP

continuous replenishment program

C-VRP

vehicle routing problem with capacity constraints

CWC

Central Warehousing Corporation

DC

distribution centre

DDAP

dynamic driver assignment problem

DWT

deadweight

EAN

European article number

EDA

exploratory data analysis

EDI

electronic data interchange

EFC

e-fulfillment centre

ELC

European logistics centre

EOQ

economic order quantity

EPAL

European Pallet Association

EPP

European Pallet Pool

ERP

enterprise resource planning

ETO

engineering to order

EVPI

expected value of perfect information

FAA

Federal Aviation Administration

FBF

finite best fit

FCNDP

fixed charge network design problem

FDA

Food and Drug Administration

FEU

forty-foot equivalent unit

FF

first fit

FFD

first fit decreasing

FFF

finite first fit

FIFO

first-in, first-out

FR

fill rate

GIS

geographic information system

GMA

Grocery Manufacturers Association

GMROI

gross margin return on investment

GPS

global positioning system

GSE

ground servicing equipment

GTIN

global trade item number

GVW

gross vehicle weight

H&M

Hennes & Mauritz

IATA

International Air Transport Association

ICP

inbound crossdocking point

ICT

information and communication technology

IDOS

inventory days of supply

IoT

Internet of Things

IP

integer programming

IRP

inventory routing problem

ISBN

International Standard Book Number

ISO

International Organization for Standardization

IT

inventory turnover

KPI

key performance indicator

LB

lower bound

LFCNDP

linear fixed-charge network design problem

LIFO

last-in, first-out

LMCFP

linear single-commodity minimum-cost flow problem

LMMCFP

linear multi-commodity minimum-cost flow problem

LNG

liquefied natural gas

LP

linear programming

LPG

liquefied petroleum gas

LSP

logistics service provider

LTL

less-than-truckload

MAE

mean absolute error

MAPE

mean absolute percentage error

MCTE

multi-commodity two-echelon location problem

MDP

material decoupling point

MFC

material flow control

MIP

mixed integer programming

MIS

management information system

MMCFP

multi-commodity minimum-cost flow problem

MMR

mass market retailer

MRO

maintenance, repair, and overhaul

MRP

material requirements planning

MS

r

TP

minimum spanning

r

-tree problem

MSE

mean squared error

MTO

make to order

MTS

make to stock

NOOS

never out of stock

NPV

net present value

NRP

node routing problem

OCT

order-cycle time

PLC

programmable logic controller

POR

perfect order rate

PRC

pneumatic refuse collection

QR

quick response

RDC

regional distribution centre

RFID

radio-frequency identification

RMG

rail-mounted gantry

RNG

pseudo-random number generator

Ro-Ro

roll-on/roll-off

RPP

rural postman problem

RTG

rubber-tired gantry

RTSP

road travelling salesman problem

S/R

storage and retrieval

SC

set covering

SC

shipment centre

SC/AS

shipment centre/air stop

SCM

supply chain management

SC-NRP

set covering, node routing problem

SCOE

single-commodity one-echelon

SCTE

single-commodity two-echelon

SHAS

special handling at source

SKU

stock keeping unit

SLA

service level agreement

SMA

selling and market area

SNDP

service network design problem

SPL

simple plant location

SQI

supplier quality index

SRM

supplier relationship management

SSCC

serial shipping container code

SSE

sum of squared errors

STSP

symmetric travelling salesman problem

SVM

support vectors machine

TAP

traffic assignment problem

TEU

twenty-foot equivalent unit

TL

truckload

TMS

transportation management system

TOFC

trailer on flatcar

TS

tabu search

TSP

travelling salesman problem

TW-NRSP

node routing and scheduling problem with time windows

UB

upper bound

ULCC

ultra-large crude carrier

ULCV

ultra-large container vessel

ULD

unit load device

UPC

universal product code

VAP

vehicle allocation problem

VIMS

visual interactive modelling system

VLCC

very large crude carrier

VMI

vendor-managed inventory

VRP

vehicle routing problem

VRDP

vehicle routing and dispatching problem

VRPMT

vehicle routing problem with multiple trips

VRPPD

vehicle routing problem with pickups and deliveries

VRSP

vehicle routing and scheduling problem

WCS

warehouse control system

WMS

warehouse management system

WORM

write-once-read-many

ZIO

zero inventory ordering

1 Introducing Logistics

1.1 Definition of Logistics

According to a widespread definition, logistics is the discipline that studies, in an organization (such as a private company, a public administration, a non-profit association, a military corps), the management and implementation of the operations concerning the flow of tangible goods (materials, food and medical supplies, refuse, equipment, weapons, etc.) from their sources (suppliers, mines, crop fields, etc.) to their points of utilization or consumption or disposal (retailers, landfills, army units, etc.) to meet the objectives of the organization. To this end, logistics requires the collection, integration, and processing of data from several sources in order to plan, organize, and control activities such as material handling, production, packaging, warehousing, and distribution.

The words “logic” and “logistics” both come from the Greek term lógos, which means, among other things, “order”. However, while “logic” derives directly from Greek, “logistics” first passed into Middle French as “logis”, meaning “lodging”, and then into English.

Defence Logistics. The origins of logistics are of a strictly military nature. In fact, the discipline arose as the study of methodologies to guarantee the correct supply of troops with victuals, ammunitions, fuel, etc. Indeed it was the Babylonians, in the distant twentieth century BCE, who first created a military corps specialized in the supply, storage and delivery of soldiers’ equipment. The relevance of logistics became apparent during the American Revolutionary War (1775–1783) when the lack of adequate supplies for the 12 000 British soldiers overseas during the first six years devastated the troop morale and contributed to their final defeat. In modern times, logistics played a key role in World War II where it helped the Allied powers to succeed. In modern times, the key concept in defence logistics is that of supply chain, defined as the set of processes, infrastructure, equipment and personnel ensuring that a specific vehicle or weapon is fully functioning in the theatre of operations.

Industrial Logistics. Only in the twentieth century, were logistics principles and techniques extended to manufacturing companies. In industrial logistics, a supply chain resembles a military one and is defined as the network of organizations (suppliers, carriers, logistics providers, wholesalers, and retailers, etc.), resources, activities, and information built around a company to produce and distribute a specific product to a specific market. Here, the goal of logistics is to manage the flow of materials and information from the extraction, harvesting or purchase of raw materials and components up to the delivery of the finished products to customers. In this sector, logistics activities are traditionally classified depending on their location with respect to the production and distribution processes. In particular, procurement logistics comes before the manufacturing process and consists of supplying raw materials and components to support the company’s production plan. Internal logistics is about material handling and storage in production plants in order to feed production lines and the subsequent product packaging and shipment. Finally, distribution logistics falls after the production plants and before the market, and aims to supply sales points or customers. In this framework, procurement logistics and distribution logistics are collectively called externallogistics.

Service Logistics. Logistics issues are also increasingly present in the service sector, for example in postal services, in urban solid waste collection, in the post-sales activities of manufacturing companies as well as in humanitarian organizations. Logistics service providers (LSPs), performing transportation or warehousing activities for other organizations, including manufacturing companies, also fall into this category.

Integrated Logistics and Logistics Alliances. Logistics activities may be carried out entirely by a specific function of the organization (see Section 1.5 for details). Otherwise, they may be jointly performed by multiple departments of the organization such as production, marketing, etc. (integrated logistics) or even in collaboration with different partner organizations (logistics alliances). Logistics alliances can be implemented in two different forms. The efficiency-oriented approach relies on contracts of a strictly operative nature that do not modify the organization’s own strategy but simply tend to create synergies or economies of scale with the primary objective of minimizing costs. On the other hand, in the differentiation-oriented approach the company tries to forge exclusive alliances with some partners, not replicable by competitors, to generate an added value with respect to the competition.

An efficiency-oriented logistics alliance was implemented by SkyTeam, the second global airline alliance in the world, that in 2021 counted 19 members (Aeroflot, Aerolíneas Argentinas, Aeroméxico, Air Europa, Air France, Ita Airways, China Airlines, China Eastern, Czech Airlines, Delta Airlines, Garuda Indonesia, Kenya Airways, KLM Royal Dutch Airlines, Korean Air, Middle East Airlines, Saudi Arabian Airlines, TAROM Romanian Air Transport, Vietnam Airlines, and Xiamen Airlines). The alliance allows the collaboration among airlines in different forms: creating synergies in timetable design and ticket pricing, sharing information about customers, operating ground services, managing frequent flyer programmes, and airplane maintenance. In addition, customers of the SkyTeam airlines can benefit from a larger number of flights, with more destinations and connections as well as a larger number of lounges located within the network’s airports. In 2021 SkyTeam transported about 675 million passengers over 15 500 daily flights reaching about 1000 destinations in 170 countries. The cargo branches of 11 of the 19 air companies cited above have also signed a strategic alliance, called SkyTeam Cargo, for freight transportation. The members of SkyTeam Cargo share airplanes and cargo buildings (see Section 6.3.2) located in 76 air cargo terminals worldwide (e.g., the cargo building located in the Vienna Airport is shared among China Airlines Cargo, Korean Air Cargo and Aerflot Cargo).

An example of a differentiation-oriented logistics alliance has been set up in 2019 between Unilever, a global Dutch–British consumer goods company owning the Algida ice cream brand, and Ferrero, a world-renowned Italian manufacturer of branded chocolate and confectionery products, including the Kinder brand of chocolate products and Nutella. The agreement concerned the launch of a new Kinder Ice Cream (whose recipe was created by Ferrero), produced and distributed by Unilever in various European markets (Germany, France, Italy, Austria, etc.). The partnership has clear mutual benefits. Ferrero may take advantage of Unilever’s experience in the ice cream sector to take its Kinder brand to new attractive markets, without the cost of investing in a frozen food supply chain. On the other hand, Unilever may take advantage from the Kinder brand power to increase its sales and enlarge its product portfolio.

1.2 Logistics Systems

A logistics system is a set of interacting infrastructures, equipment, and human resources whose objective is, as a whole, the execution of all the functional activities determining the flow of materials among a number of facilities. Facilities may be plants, warehouses, landfills, sorting centres, air, and ground hubs where either production or assembly, disposal, consolidation, storage, packaging, distribution, etc. is carried out.