Read more
Informationen zum Autor Gianpaolo Ghiani, Professor of Operations Research, University of Salento, Lecce, Italy. Gilbert Laporte, Professor Emeritus, Department of Decision Sciences, HEC, Montréal, Canada and University of Bath, United Kingdom. Roberto Musmanno, Professor of Operations Research, University of Calabria, Italy. Klappentext INTRODUCTION TO LOGISTICS SYSTEMS MANAGEMENTThe updated new edition of the award-winning introductory textbook on logistics system managementIntroduction 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(r) Excel(r) files and Python(r) 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. Zusammenfassung INTRODUCTION TO LOGISTICS SYSTEMS MANAGEMENTThe updated new edition of the award-winning introductory textbook on logistics system managementIntroduction 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-chapte...
List of contents
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 Foreca
