Introduction to Logistics Systems Management With Microsoft Excel and Python examples

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.

Gianpaolo Ghiani, Professor of Operations Research, University of Salento, Lecce, Italy.

Gilbert Laporte, Professor Emeritus, Department of Decision Sciences, HEC, Montréal, Canada.

Roberto Musmanno, Professor of Operations Research, University of Calabria, Italy.

Foreword xiii

Preface xv

Acknowledgements xvii

About the Authors xviii

List of Abbreviations xix

1 Introducing Logistics 1

1.1 Definition of Logistics 1

1.2 Logistics Systems 3

1.3 Supply Chains 5

1.3.1 Logistics Versus Supply Chain Management 5

1.3.2 A Taxonomy of Supply Chains 5

1.3.3 The Bullwhip Effect 6

1.4 Logistics Service Providers 8

1.5 Logistics in Service Organizations 9

1.5.1 Logistics in Solid Waste Management 9

1.5.2 Humanitarian Logistics 10

1.6 Case Studies 11

1.6.1 Apple 11

1.6.2 Adidas AG 13

1.6.3 Galbani 14

1.6.4 Pfizer 15

1.6.5 Amazon 18

1.6.6 FedEx 20

1.6.7 A.P. Moller-Maersk 21

1.6.8 Canadian Pacific Railway 23

1.7 Trends in Logistics 24

1.7.1 Reverse and Sustainable Logistics 24

1.7.2 E-commerce Logistics 26

1.7.3 City Logistics 28

1.8 Logistics Objectives and KPIs 30

1.8.1 Capital-related KPIs 30

1.8.2 Cost-related KPIs 31

1.8.3 Service Level-related KPIs 32

1.9 Logistics Management 36

1.9.1 Logistics Planning 37

1.9.2 Logistics Organizational Structures 37

1.9.3 Controlling 41

1.10 Data Analytics in Logistics 48

1.10.1 Descriptive Analytics 48

1.10.2 Predictive Analytics 49

1.10.3 Prescriptive Analytics 49

1.11 Segmentation Analysis 69

1.11.1 Customer Segmentation 69

1.11.2 Product Segmentation 70

1.12 Information Systems 73

1.13 Questions and Problems 75

2 Forecasting Logistics Data 83

2.1 Introduction 83

2.2 Qualitative Methods 84

2.3 Quantitative Methods 85

2.3.1 Explanatory Versus Extrapolation Methods 87

2.3.2 The Forecasting Process 87

2.4 Exploratory Data Analysis 88

2.4.1 The Univariate Case 88

2.4.2 Histograms 89

2.4.3 Boxplots 90

2.4.4 Time Series Plots 92

2.4.5 The Bivariate Case 92

2.4.6 Scatterplots 93

2.5 Data Preprocessing 93

2.5.1 Insertion of Missing Data 93

2.5.2 Detection of Outliers 95

2.5.3 Data Aggregation 96

2.5.4 Removing Calendar Variations 98

2.5.5 Deflating Monetary Time Series 99

2.5.6 Adjusting for Population Variations 101

2.5.7 Data Normalization 101

2.6 Classification of Time Series 102

2.7 Explanatory Methods 105

2.7.1 Forecasting with Regression 105

2.7.2 Multicollinearity 107

2.7.3 Categorical Predictors 107

2.7.4 Coefficient of Determination 108

2.7.5 Polynomial Regression 109

2.7.6 Linear–log, Log–linear and Log–log Regression Models 111

2.7.7 Underfitting and Overfitting 111

2.7.8 Forecasting with Machine Learning 113

2.8 Extrapolation Methods 118

2.8.1 Notation 118

2.8.2 Decomposition Method 119

2.8.3 Further Extrapolation Methods: the Constant-trend Case 127

2.8.4 Further Extrapolation Methods: the Linear-trend Case 132

2.8.5 Further Extrapolation Methods: the Seasonality Case 137

2.8.6 Further Extrapolation Methods: the Irregular Time Series Case 146

2.8.7 Further Extrapolation Methods: the Intermittent Time Series Case 148

2.9 Accuracy Measures 154

2.9.1 Calibration of the Parametrized Forecasting Methods 155

2.9.2 Selection of the Most Accurate Forecasting Method 157

2.10 Forecasting Control 158

2.10.1 Tracking Signal 158

2.10.2 Control Charts 159

2.11 Interval Forecasts 162

2.12 Case Study: Sales Forecasting at Shivoham 163

2.13 Case Study: Sales Forecasting at Orlea 164

2.14 Questions and Problems 165

3 Designing the Logistics Network 177

3.1 Introduction 177

3.2 Classification of Logistics Network Design Problems 178

3.3 The Number of Facilities in a Logistics System 181

3.4 Qualitative Versus Quantitative Location Methods 183

3.5 The Weighted Scoring Method 183

3.6 The Analytical Hierarchy Process 185

3.7 Single-commodity One-echelon Continuous Location Problems 190

3.8 Single-commodity Two-echelon Continuous Location Problems 197

3.9 Single-commodity One-echelon Discrete Location Problems 200

3.10 Single-commodity Two-echelon Discrete Location Problems 222

3.11 The Multi-commodity Case 226

3.12 Location-covering Problems 230

3.13 p-centre Problems 234

3.14 Data Aggregation 241

3.15 Location Models Under Uncertainty 244

3.15.1 A Stochastic Location–allocation Model 244

3.15.2 A Location-routing Model with Uncertain Demand 247

3.16 Case Study: Intermodal Container Depot Location at Hardcastle 251

3.17 Case Study: Location–Allocation Decisions at the Italian National Transplant Centre 254

3.18 Questions and Problems 256

4 Selecting the Suppliers 267

4.1 Introduction 267

4.2 Definition of the Set of Potential Suppliers 269

4.3 Definition of the Selection Criteria 270

4.4 Supplier Selection 274

4.5 Supplier Relationship Management Software 278

4.6 Case Study: the System for the Selection of Suppliers at Baxter 279

4.7 Case Study: the Supplier Selection at Onokar 282

4.8 Questions and Problems 284

5 Managing a Warehouse 290

5.1 Introduction 290

5.1.1 Warehouse Operations 290

5.1.2 Warehouse Functional Zones 292

5.1.3 Advantages of Warehousing 294

5.2 Types of Warehouses 294

5.2.1 Classification with Respect to the Position in the Logistics System 294

5.2.2 Classification with Respect to Ownership 296

5.2.3 Classification with Respect to Climate-control 297

5.2.4 Classification with Respect to the Level of Automation 297

5.3 Warehousing Costs 298

5.4 Unit Loads 300

5.4.1 Freight Classification 300

5.4.2 Unit Loads and Stock Keeping Units 301

5.4.3 Packaging 301

5.4.4 Palletized Unit Loads 302

5.4.5 Containerized Unit Loads 305

5.5 Storage Systems 307

5.5.1 Block Stacking 307

5.5.2 Pallet Racks 307

5.5.3 Shelves 311

5.5.4 Cabinet and Carousel Systems 313

5.6 Internal Transportation Systems 314

5.6.1 Manual Handling and Non-autonomous Vehicles 315

5.6.2 Automated Guided Vehicles 318

5.6.3 Stacker Cranes 320

5.6.4 Conveyors 321

5.7 Product Identification Systems 322

5.7.1 SKU Codes 322

5.7.2 Global Trade Item Numbers 323

5.7.3 Barcodes 323

5.7.4 QR Codes 325

5.7.5 Logistic Labels 325

5.7.6 Radio-frequency Identification 325

5.8 Warehouse Performance Measures 327

5.9 Warehouse Management Systems 333

5.10 Warehouse Design 335

5.10.1 Internal Transportation Technology Selection 336

5.10.2 Layout Design 337

5.10.3 Sizing of the Storage Zone 341

5.10.4 Sizing of the Receiving and Shipping Zones 348

5.10.5 Sizing of an AS/RS 349

5.10.6 Sizing a Vehicle-based Internal Transportation System 354

5.11 Storage Space Allocation 355

5.12 Inventory Management 360

5.12.1 Deterministic models 361

5.12.2 Stochastic Models 373

5.12.3 Selecting an Inventory Policy 380

5.12.4 Multiproduct Inventory Models 382

5.13 Crossdock Door Assignment Problem 387

5.14 Put-away and Order Picking Optimization 390

5.14.1 Parts-to-picker Systems 390

5.14.2 Picker-to-parts and AGV-based Systems 390

5.15 Load Consolidation 397

5.15.1 One-dimensional Bin Packing Problems 400

5.15.2 Two-dimensional Bin Packing Problems 403

5.15.3 Three-dimensional Bin Packing Problems 406

5.16 Case Study: Inventory Management at Wolferine 415

5.17 Case Study: Airplane Loading at FedEx 416

5.18 Questions and problems 418

6 Managing Freight Transportation 431

6.1 Introduction 431

6.2 Transportation Modes 431

6.2.1 Road Transportation 432

6.2.2 Water Transportation 434

6.2.3 Rail Transportation 437

6.2.4 Air Transportation 438

6.2.5 Pipeline Transportation 439

6.2.6 Intermodal Transportation 439

6.2.7 Comparison Among Transportation Modes 440

6.3 Freight Transportation Terminals 443

6.3.1 Port Terminals 444

6.3.2 Air Cargo Terminals 446

6.3.3 Rail Freight Terminals 448

6.3.4 Road Freight Terminals 449

6.4 Classification of Freight Transportation Management Problems 450

6.4.1 Long-haul Freight Transportation Management 450

6.4.2 Freight Transportation Terminal Management 451

6.4.3 Short-haul Freight Transportation Management 452

6.5 Transportation Management Systems 454

6.6 Freight Traffic Assignment Problems 455

6.6.1 Minimum-cost Flow Formulation 456

6.6.2 Linear Single-commodity Minimum-cost Flow Problems 458

6.6.3 Linear Multi-commodity Minimum-cost Flow Problems 465

6.7 Service Network Design Problems 471

6.8 Vehicle Allocation Problems 478

6.9 A Dynamic Driver Assignment Problem 481

6.10 Vehicle Fleet Composition 483

6.11 Shipment Consolidation 485

6.12 Vehicle Routing Problems 488

6.12.1 The Travelling Salesman Problem 491

6.12.2 The Node Routing Problem with Operational Constraints 506

6.12.3 The Node Routing and Scheduling Problem with Time Windows 519

6.12.4 Arc Routing Problems 530

6.12.5 Route Sequencing 540

6.13 Real-time Vehicle Routing Problems 541

6.14 Integrated Location and Routing Problems 543

6.15 Inventory Routing Problems 545

6.16 Case Study: Air Network Design at Intexpress 555

6.17 Case Study: Dynamic Vehicle-dispatching Problem with Pickups and Deliveries at eCourier 559

6.18 Questions and Problems 561

Index 572

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