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| data science in logistics: Data Science for Supply Chain Forecasting Nicolas Vandeput, 2021-03-22 Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting, Second Edition contends that a true scientific method which includes experimentation, observation, and constant questioning must be applied to supply chains to achieve excellence in demand forecasting. This second edition adds more than 45 percent extra content with four new chapters including an introduction to neural networks and the forecast value added framework. Part I focuses on statistical traditional models, Part II, on machine learning, and the all-new Part III discusses demand forecasting process management. The various chapters focus on both forecast models and new concepts such as metrics, underfitting, overfitting, outliers, feature optimization, and external demand drivers. The book is replete with do-it-yourself sections with implementations provided in Python (and Excel for the statistical models) to show the readers how to apply these models themselves. This hands-on book, covering the entire range of forecasting—from the basics all the way to leading-edge models—will benefit supply chain practitioners, forecasters, and analysts looking to go the extra mile with demand forecasting. |
| data science in logistics: Big Data Analytics in Supply Chain Management Iman Rahimi, Amir H. Gandomi, Simon James Fong, M. Ali Ülkü, 2020-12-20 In a world of soaring digitization, social media, financial transactions, and production and logistics processes constantly produce massive data. Employing analytical tools to extract insights and foresights from data improves the quality, speed, and reliability of solutions to highly intertwined issues faced in supply chain operations. From procurement in Industry 4.0 to sustainable consumption behavior to curriculum development for data scientists, this book offers a wide array of techniques and theories of Big Data Analytics applied to Supply Chain Management. It offers a comprehensive overview and forms a new synthesis by bringing together seemingly divergent fields of research. Intended for Engineering and Business students, scholars, and professionals, this book is a collection of state-of-the-art research and best practices to spur discussion about and extend the cumulant knowledge of emerging supply chain problems. |
| data science in logistics: Data Science for Transport Charles Fox, 2018-02-27 The quantity, diversity and availability of transport data is increasing rapidly, requiring new skills in the management and interrogation of data and databases. Recent years have seen a new wave of 'big data', 'Data Science', and 'smart cities' changing the world, with the Harvard Business Review describing Data Science as the sexiest job of the 21st century. Transportation professionals and researchers need to be able to use data and databases in order to establish quantitative, empirical facts, and to validate and challenge their mathematical models, whose axioms have traditionally often been assumed rather than rigorously tested against data. This book takes a highly practical approach to learning about Data Science tools and their application to investigating transport issues. The focus is principally on practical, professional work with real data and tools, including business and ethical issues. Transport modeling practice was developed in a data poor world, and many of our current techniques and skills are building on that sparsity. In a new data rich world, the required tools are different and the ethical questions around data and privacy are definitely different. I am not sure whether current professionals have these skills; and I am certainly not convinced that our current transport modeling tools will survive in a data rich environment. This is an exciting time to be a data scientist in the transport field. We are trying to get to grips with the opportunities that big data sources offer; but at the same time such data skills need to be fused with an understanding of transport, and of transport modeling. Those with these combined skills can be instrumental at providing better, faster, cheaper data for transport decision- making; and ultimately contribute to innovative, efficient, data driven modeling techniques of the future. It is not surprising that this course, this book, has been authored by the Institute for Transport Studies. To do this well, you need a blend of academic rigor and practical pragmatism. There are few educational or research establishments better equipped to do that than ITS Leeds. - Tom van Vuren, Divisional Director, Mott MacDonald WSP is proud to be a thought leader in the world of transport modelling, planning and economics, and has a wide range of opportunities for people with skills in these areas. The evidence base and forecasts we deliver to effectively implement strategies and schemes are ever more data and technology focused a trend we have helped shape since the 1970's, but with particular disruption and opportunity in recent years. As a result of these trends, and to suitably skill the next generation of transport modellers, we asked the world-leading Institute for Transport Studies, to boost skills in these areas, and they have responded with a new MSc programme which you too can now study via this book. - Leighton Cardwell, Technical Director, WSP. From processing and analysing large datasets, to automation of modelling tasks sometimes requiring different software packages to talk to each other, to data visualization, SYSTRA employs a range of techniques and tools to provide our clients with deeper insights and effective solutions. This book does an excellent job in giving you the skills to manage, interrogate and analyse databases, and develop powerful presentations. Another important publication from ITS Leeds. - Fitsum Teklu, Associate Director (Modelling & Appraisal) SYSTRA Ltd Urban planning has relied for decades on statistical and computational practices that have little to do with mainstream data science. Information is still often used as evidence on the impact of new infrastructure even when it hardly contains any valid evidence. This book is an extremely welcome effort to provide young professionals with the skills needed to analyse how cities and transport networks actually work. The book is also highly relevant to anyone who will later want to build digital solutions to optimise urban travel based on emerging data sources. - Yaron Hollander, author of Transport Modelling for a Complete Beginner |
| data science in logistics: Data Science for Supply Chain Forecast Nicolas Vandeput, 2018-11-12 Data Science for Supply Chain Forecast Data Science for Supply Chain Forecast is a book for practitioners focusing on data science and machine learning; it demonstrates how both are closely interlinked in order to create an advanced forecast for supply chain. As one will discover in this book, artificial intelligence (AI) & machine learning (ML) are not simply a question of coding skills. Using data science in order to solve a problem requires a scientific mindset more than coding skills. The story behind these models is one of experimentation, of observation and of constant questioning; a true scientific method must be applied to supply chain. In the data science field as well as that of the supply chain, simple questions do not come with simple answers. In order to resolve these questions, one needs to be both a scientist as well as to use the correct tools. In this book, we will discuss both. Is this Book for me? This book has been written for supply chain practitioners, forecasters and analysts who are looking to go the extra mile. You do not need technical IT skills to start using the models of this book. You do not need a dedicated server or expensive software licenses: you solely need your own computer. You do not need a PhD in mathematics: mathematics will only be utilized as a tool to tweak and understand the models. In the majority of the cases - especially when it comes to machine learning - a deep understanding of the mathematical inner workings of a model will not be necessary in order to optimize it and understand its limitations. Reviews In an age where analytics and machine learning are taking on larger roles in the business forecasting, Nicolas' book is perfect solution for professionals who need to combine practical supply chain experience with the mathematical and technological tools that can help us predict the future more reliably. Daniel Stanton - Author, Supply Chain Management For Dummies Open source statistical toolkits have progressed tremendously over the last decade. Nicolas demonstrates that these toolkits are more than enough to start addressing real-world forecasting challenges as found in supply chains. Moreover, through its hands-on approach, this book is accessible to a large audience of supply chain practitioners. The supply chain of the 21st century will be data-driven and Nicolas gets it perfectly. Joannes Vermorel - CEO Lokad This book is unique in its kind. It explains the basics of Python using basic traditional forecasting techniques and shows how machine learning is revolutionizing the forecasting domain. Nicolas has done an outstanding job explaining a technical subject in an easily accessible way. A must-read for any supply chain professional. Professor Bram Desmet - CEO Solventure This book is before anything a practical and business-oriented DIY user manual to help planners move into 21st-century demand planning. The breakthrough comes from several tools and techniques available to all, and which thanks to Nicolas' precise and concrete explanations can now be implemented in real business environments by any normal planner. I can confirm that Nicolas' learnings are based on real-life experience and can tremendously help on improving top and bottom lines. Henri-Xavier Benoist - VP Supply Chain Bridegstone EMEA |
| data science in logistics: Big Data Driven Supply Chain Management Nada R. Sanders, 2014-05-07 Master a complete, five-step roadmap for leveraging Big Data and analytics to gain unprecedented competitive advantage from your supply chain. Using Big Data, pioneers such as Amazon, UPS, and Wal-Mart are gaining unprecedented mastery over their supply chains. They are achieving greater visibility into inventory levels, order fulfillment rates, material and product delivery… using predictive data analytics to match supply with demand; leveraging new planning strengths to optimize their sales channel strategies; optimizing supply chain strategy and competitive priorities; even launching powerful new ventures. Despite these opportunities, many supply chain operations are gaining limited or no value from Big Data. In Big Data Driven Supply Chain Management, Nada Sanders presents a systematic five-step framework for using Big Data in supply chains. You'll learn best practices for segmenting and analyzing customers, defining competitive priorities for each segment, aligning functions behind strategy, dissolving organizational boundaries to sense demand and make better decisions, and choose the right metrics to support all of this. Using these techniques, you can overcome the widespread obstacles to making the most of Big Data in your supply chain — and earn big profits from the data you're already generating. For all executives, managers, and analysts interested in using Big Data technologies to improve supply chain performance. |
| data science in logistics: Management Science, Logistics, and Operations Research Wang, John, 2013-09-30 This book examines related research in decision, management, and other behavioral sciences in order to exchange and collaborate on information among business, industry, and government, providing innovative theories and practices in operations research--Provided by publisher. |
| data science in logistics: Logistics Management Tan Miller, Matthew J. Liberatore, 2020-04-08 This book illustrates and explains a wide range of practical logistics strategies and analytic techniques to facilitate decision-making across functions such as manufacturing, warehousing, transportation, and inventory management. Logistics professionals must utilize a broad array of analytic techniques and approaches for decision-making. Effective use of analytics requires an understanding of both fundamental and advanced logistics decision-making techniques and methodologies. Further, logistics professionals must organize and view these analytics-based decision support tools through well-structured planning frameworks. In this book, we illustrate and explain a wide range of practical logistics strategies and analytic techniques to facilitate decision-making across functions such as manufacturing, warehousing, transportation and inventory management. We also describe how to organize these analytics-based tools and strategies through logistics frameworks that span strategic, tactical and operational planning and scheduling decisions. This book is intended for logistics professionals to use as a reference document that offers ideas and guidance for addressing specific logistics management decisions and challenges, and it will also serve as a valuable resource or secondary text for graduate and advanced undergraduate students. |
| data science in logistics: Inventory Optimization Nicolas Vandeput, 2020-08-24 In this book . . . Nicolas Vandeput hacks his way through the maze of quantitative supply chain optimizations. This book illustrates how the quantitative optimization of 21st century supply chains should be crafted and executed. . . . Vandeput is at the forefront of a new and better way of doing supply chains, and thanks to a richly illustrated book, where every single situation gets its own illustrating code snippet, so could you. --Joannes Vermorel, CEO, Lokad Inventory Optimization argues that mathematical inventory models can only take us so far with supply chain management. In order to optimize inventory policies, we have to use probabilistic simulations. The book explains how to implement these models and simulations step-by-step, starting from simple deterministic ones to complex multi-echelon optimization. The first two parts of the book discuss classical mathematical models, their limitations and assumptions, and a quick but effective introduction to Python is provided. Part 3 contains more advanced models that will allow you to optimize your profits, estimate your lost sales and use advanced demand distributions. It also provides an explanation of how you can optimize a multi-echelon supply chain based on a simple—yet powerful—framework. Part 4 discusses inventory optimization thanks to simulations under custom discrete demand probability functions. Inventory managers, demand planners and academics interested in gaining cost-effective solutions will benefit from the do-it-yourself examples and Python programs included in each chapter. Events around the book Link to a De Gruyter Online Event in which the author Nicolas Vandeput together with Stefan de Kok, supply chain innovator and CEO of Wahupa; Koen Cobbaert, Director in the S&O Industry practice of PwC Belgium; Bram Desmet, professor of operations & supply chain at the Vlerick Business School in Ghent; and Karl-Eric Devaux, Planning Consultant, Hatmill, discuss about models for inventory optimization. The event will be moderated by Eric Wilson, Director of Thought Leadership for Institute of Business Forecasting (IBF): https://youtu.be/565fDQMJEEg |
| data science in logistics: Applied Data Science Martin Braschler, Thilo Stadelmann, Kurt Stockinger, 2019-06-13 This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry. |
| data science in logistics: Data Analytics for Intelligent Transportation Systems Mashrur Chowdhury, Kakan Dey, Amy Apon, 2024-11-02 Data Analytics for Intelligent Transportation Systems provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems (ITS), including the tools needed to implement these methods using big data analytics and other computing techniques. The book examines the major characteristics of connected transportation systems, along with the fundamental concepts of how to analyze the data they produce. It explores collecting, archiving, processing, and distributing the data, designing data infrastructures, data management and delivery systems, and the required hardware and software technologies. It presents extensive coverage of existing and forthcoming intelligent transportation systems and data analytics technologies. All fundamentals/concepts presented in this book are explained in the context of ITS. Users will learn everything from the basics of different ITS data types and characteristics to how to evaluate alternative data analytics for different ITS applications. They will discover how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications, along with key safety and environmental applications for both commercial and passenger vehicles, data privacy and security issues, and the role of social media data in traffic planning. Data Analytics for Intelligent Transportation Systems will prepare an educated ITS workforce and tool builders to make the vision for safe, reliable, and environmentally sustainable intelligent transportation systems a reality. It serves as a primary or supplemental textbook for upper-level undergraduate and graduate ITS courses and a valuable reference for ITS practitioners. - Utilizes real ITS examples to facilitate a quicker grasp of materials presented - Contains contributors from both leading academic and commercial domains - Explains how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications - Includes exercise problems in each chapter to help readers apply and master the learned fundamentals, concepts, and techniques - New to the second edition: Two new chapters on Quantum Computing in Data Analytics and Society and Environment in ITS Data Analytics |
| data science in logistics: Data Science and Machine Learning Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman, 2019-11-20 Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code |
| data science in logistics: Recent Developments in Data Science and Business Analytics Madjid Tavana, Srikanta Patnaik, 2018-03-27 This edited volume is brought out from the contributions of the research papers presented in the International Conference on Data Science and Business Analytics (ICDSBA- 2017), which was held during September 23-25 2017 in ChangSha, China. As we all know, the field of data science and business analytics is emerging at the intersection of the fields of mathematics, statistics, operations research, information systems, computer science and engineering. Data science and business analytics is an interdisciplinary field about processes and systems to extract knowledge or insights from data. Data science and business analytics employ techniques and theories drawn from many fields including signal processing, probability models, machine learning, statistical learning, data mining, database, data engineering, pattern recognition, visualization, descriptive analytics, predictive analytics, prescriptive analytics, uncertainty modeling, big data, data warehousing, data compression, computer programming, business intelligence, computational intelligence, and high performance computing among others. The volume contains 55 contributions from diverse areas of Data Science and Business Analytics, which has been categorized into five sections, namely: i) Marketing and Supply Chain Analytics; ii) Logistics and Operations Analytics; iii) Financial Analytics. iv) Predictive Modeling and Data Analytics; v) Communications and Information Systems Analytics. The readers shall not only receive the theoretical knowledge about this upcoming area but also cutting edge applications of this domains. |
| data science in logistics: Supply Chain Analytics Peter W. Robertson, 2020-11-25 Supply Chain Analytics introduces the reader to data analytics and demonstrates the value of their effective use in supply chain management. By describing the key supply chain processes through worked examples, and the descriptive, predictive and prescriptive analytic methods that can be applied to bring about improvements to those processes, the book presents a more comprehensive learning experience for the reader than has been offered previously. Key topics are addressed, including optimisation, big data, data mining and cloud computing. The author identifies four core supply chain processes – strategy, design, execution and people – to which the analytic techniques explained can be applied to ensure continuous improvement. Pedagogy to aid learning is incorporated throughout, including an opening section for each chapter explaining the learnings designed for the chapter; worked examples illustrating how each analytic technique works, how it is applied and what to be careful of; tables, diagrams and equations to help ‘visualise’ the concepts and methods covered; chapter case studies; and end-of-chapter review questions and assignment tasks. Providing both management expertise and technical skills, which are essential to decision-makers in the supply chain, this textbook should be essential reading for advanced undergraduate and postgraduate students of supply chain analytics, supply chain leadership, and supply chain and operations management. Its practice-based and applied approach also makes it valuable for operating supply chain practitioners and those studying for professional qualifications. Online resources include chapter-by-chapter PowerPoint slides, tutorial exercises, written assignments and a test bank of exam questions. |
| data science in logistics: Managing Supply Chain And Logistics: Competitive Strategy For A Sustainable Future Ling Li, 2014-07-18 Managing Supply Chain and Logistics: Competitive Strategy for a Sustainable Future explores practical ways of investing in a sustainable future through real-world cases which demonstrate various supply chain management strategies and tactics. By applying viable value creation strategies, operational models, decision-making techniques, and information technology, the author provides in-depth analyses of new initiatives such as collaborative planning, forecasting, and replenishment (CPFR); demonstrates competitive approaches to managing flows of material, information and fund in supply chain; and illustrates creative methods to apply data science and business intelligence. This book also promotes cross-functional decision-making, problem solving skills and offers a feasible approach to managing a volatile business. Readers will find this book a valuable resource to solve supply chain management practical problems with a sustainable future in mind. |
| data science in logistics: Data Science and Digital Business Fausto Pedro García Márquez, Benjamin Lev, 2019-01-04 This book combines the analytic principles of digital business and data science with business practice and big data. The interdisciplinary, contributed volume provides an interface between the main disciplines of engineering and technology and business administration. Written for managers, engineers and researchers who want to understand big data and develop new skills that are necessary in the digital business, it not only discusses the latest research, but also presents case studies demonstrating the successful application of data in the digital business. |
| data science in logistics: Machine Learning Systems Jeffrey Smith, 2018-05-21 Summary Machine Learning Systems: Designs that scale is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web app. Foreword by Sean Owen, Director of Data Science, Cloudera Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology If you’re building machine learning models to be used on a small scale, you don't need this book. But if you're a developer building a production-grade ML application that needs quick response times, reliability, and good user experience, this is the book for you. It collects principles and practices of machine learning systems that are dramatically easier to run and maintain, and that are reliably better for users. About the Book Machine Learning Systems: Designs that scale teaches you to design and implement production-ready ML systems. You'll learn the principles of reactive design as you build pipelines with Spark, create highly scalable services with Akka, and use powerful machine learning libraries like MLib on massive datasets. The examples use the Scala language, but the same ideas and tools work in Java, as well. What's Inside Working with Spark, MLlib, and Akka Reactive design patterns Monitoring and maintaining a large-scale system Futures, actors, and supervision About the Reader Readers need intermediate skills in Java or Scala. No prior machine learning experience is assumed. About the Author Jeff Smith builds powerful machine learning systems. For the past decade, he has been working on building data science applications, teams, and companies as part of various teams in New York, San Francisco, and Hong Kong. He blogs (https: //medium.com/@jeffksmithjr), tweets (@jeffksmithjr), and speaks (www.jeffsmith.tech/speaking) about various aspects of building real-world machine learning systems. Table of Contents PART 1 - FUNDAMENTALS OF REACTIVE MACHINE LEARNING Learning reactive machine learning Using reactive tools PART 2 - BUILDING A REACTIVE MACHINE LEARNING SYSTEM Collecting data Generating features Learning models Evaluating models Publishing models Responding PART 3 - OPERATING A MACHINE LEARNING SYSTEM Delivering Evolving intelligence |
| data science in logistics: Advances in Shipping Data Analysis and Modeling César Ducruet, 2017-11-06 Shipping flows – maritime ‘footprints’ – remain underexplored in the existing literature despite the crucial importance of freight transport for global trade and economic development. Additionally, decision-makers lack a comprehensive view on how shipping flows can be measured, analyzed, and mapped in order to support their policies and strategies. This interdisciplinary volume, drawing on an international cast-list of experts, explores a number of crucial issues in shipping data estimation, construction, collection, mining, analysis, visualization, and mapping. Advances in Shipping Data Analysis and Modeling delivers several key messages. First, that in a world of just-in-time delivery and rapid freight transit, it is important to bear in mind the long-term roots of current trends as well as foreseeable future developments because shipping patterns exhibit recurrent, if not cyclical and path-dependent, dynamics. Second, shipping flows are currently often understood at the micro-level of intra-urban logistics delivery and at the national level using commodity flow analyses, but this volume emphasizes the need to expand the scale of analysis by offering new evidence on the changing distribution of global and international shipping flows, based on actual data. Third, that this multidisciplinary approach to shipping flows can shed important light on crucial issues that go beyond shipping itself including climate change, urban development, technological change, commodity specialization, digital humanities, navigation patterns, international trade, and regional growth. Edited by experts in their field, this volume is of upmost importance to those who study industrial economics, shipping industries and economic and transport geography. |
| data science in logistics: Sustainable Transportation and Smart Logistics Javier Faulin, Scott Grasman, Angel Juan, Patrick Hirsch, 2018-11-13 Sustainable Transportation and Smart Logistics: Decision-Making Models and Solutions provides deterministic and probabilistic models for transportation logistics problem-solving and decision-making. The book presents an overview of the intersections between sustainability, transportation, and logistics, and delves into the current problems associated with the implementation of sustainable transportation and smart logistics in urban settings. It also offers models for addressing complex structural problems and procedures for estimating transportation externalities such as environmental and social impacts, both in industrial and government arenas, as well as decision-making models from operational, tactical, and strategic management perspectives. Sustainable Transportation and Smart Logistics also covers best practices for practical corporate policy implementation, making it a comprehensive and vital resource for researchers, graduate students, practitioners, and policy makers in transportation, logistics, urban planning, economics, engineering, and environmental science. - Examines various modes of transportation - Includes mathematical models for decision-making in a wide variety of situations - Presents public transportation and smart cities use cases |
| data science in logistics: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians. |
| data science in logistics: Public Policy Analytics Ken Steif, 2021-08-18 Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand ‘spatial process’ and develop spatial analytics; how to develop ‘useful’ predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and ‘Planning’ are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government. |
| data science in logistics: Data Science and Its Applications Aakanksha Sharaff, G R Sinha, 2021-08-18 The term data being mostly used, experimented, analyzed, and researched, Data Science and its Applications finds relevance in all domains of research studies including science, engineering, technology, management, mathematics, and many more in wide range of applications such as sentiment analysis, social medial analytics, signal processing, gene analysis, market analysis, healthcare, bioinformatics etc. The book on Data Science and its applications discusses about data science overview, scientific methods, data processing, extraction of meaningful information from data, and insight for developing the concept from different domains, highlighting mathematical and statistical models, operations research, computer programming, machine learning, data visualization, pattern recognition and others. The book also highlights data science implementation and evaluation of performance in several emerging applications such as information retrieval, cognitive science, healthcare, and computer vision. The data analysis covers the role of data science depicting different types of data such as text, image, biomedical signal etc. useful for a wide range of real time applications. The salient features of the book are: Overview, Challenges and Opportunities in Data Science and Real Time Applications Addressing Big Data Issues Useful Machine Learning Methods Disease Detection and Healthcare Applications utilizing Data Science Concepts and Deep Learning Applications in Stock Market, Education, Behavior Analysis, Image Captioning, Gene Analysis and Scene Text Analysis Data Optimization Due to multidisciplinary applications of data science concepts, the book is intended for wide range of readers that include Data Scientists, Big Data Analysists, Research Scholars engaged in Data Science and Machine Learning applications. |
| data science in logistics: Operational Logistics Moshe Kress, 2012-12-06 Operational Logistics: The Art and Science of Sustaining Military Operations explores military logistics in terms of the theoretical foundations of operational logistics (OpLog) and its applications. The theoretical foundations are examined with regard to two dimensions. First, the artistic or qualitative aspects of contemporary logistics are looked at in the context of the operational level of war. These OpLog aspects include principles, imperatives and tenets, which are stated and analyzed. The second dimension relates to the scientific aspects of logistics. It is manifested by a formal network model that represents the structural and operational features of an OpLog system. Hence the book examines both artistic and scientific dimensions of military logistics and integrates the respective qualitative and quantitative aspects into a unified and definitive presentation of operational logistics. Chapter 1 presents a general introduction to military logistics. Chapter 2 discusses the general structure and characteristics of logistics and describes its three levels - strategic, operational and tactical. Chapter 3 focuses on Operational Logistics (OpLog). Chapter 4 deals with the logistics planning process. Chapter 5 addresses the issue of logistics information. Chapter 6 deals with forecasting logistics demands. Chapter 7 introduces the first version of the logistics network model. Chapter 8 addresses an important property of an OpLog system - Flexibility. Chapter 9 discusses two major (and dual) issues in OpLog practice: force accumulation and medical treatment and evacuation. Chapter 10 presents an inter-temporal network optimization model that is designed to determine deployment and employment of the support chain in an OpLog system. |
| data science in logistics: The Logic of Logistics David Simchi-Levi, Xin Chen, Julien Bramel, 2007-07-03 Fierce competition in today's global market provides a powerful motivation for developing ever more sophisticated logistics systems. This book, written for the logistics manager and researcher, presents a survey of the modern theory and application of logistics. The goal of the book is to present the state-of-the-art in the science of logistics management. As a result, the authors have written a timely and authoritative survey of this field that many practitioners and researchers will find makes an invaluable companion to their work. |
| data science in logistics: Logistics of Production and Inventory S.C. Graves, A.H.G. Rinnooy Kan, P.H. Zipkin, 1993-05-27 Handbook |
| data science in logistics: Data Science for Business and Decision Making Luiz Paulo Favero, Patricia Belfiore, 2019-04-11 Data Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on one or the other. As a result, the book more clearly defines the principles of business analytics for those who want to apply quantitative methods in their work. Its emphasis reflects the importance of regression, optimization and simulation for practitioners of business analytics. Each chapter uses a didactic format that is followed by exercises and answers. Freely-accessible datasets enable students and professionals to work with Excel, Stata Statistical Software®, and IBM SPSS Statistics Software®. - Combines statistics and operations research modeling to teach the principles of business analytics - Written for students who want to apply statistics, optimization and multivariate modeling to gain competitive advantages in business - Shows how powerful software packages, such as SPSS and Stata, can create graphical and numerical outputs |
| data science in logistics: Comprehensive Logistics Timm Gudehus, Herbert Kotzab, 2012-01-10 This survey of modern logistics, updated in a second edition, includes proven strategies and tools for solving numerous logistical problems, with algorithms and formulae for the computer-based planning of logistic systems as well as their dynamic scheduling. |
| data science in logistics: Technology in Supply Chain Management and Logistics Anthony M. Pagano, Matthew Liotine, 2019-09-07 Technology in Supply Chain Management and Logistics: Current Practice and Future Applications analyzes the implications of these technologies in a variety of supply chain settings, including block chain, Internet of Things (IoT), inventory optimization, and medical supply chain. This book outlines how technologies are being utilized for product planning, materials management and inventory, transportation and distribution, workflow, maintenance, the environment, and in health and safety. Readers will gain a better understanding of the implications of these technologies with respect to value creation, operational effectiveness, investment level, technical migration and general industry acceptance. In addition, the book features case studies, providing a real-world look at supply chain technology implementations, their necessary training requirements, and how these new technologies integrate with existing business technologies. - Identifies emerging supply chain technologies and trends in technology acceptance and utilization levels across various industry sectors - Assists professionals with technology investment decisions, procurement, best values, and how they can be utilized for logistics operations - Features videos showing technology application, including optimization software, cloud computing, mobility, 3D printing, autonomous vehicles, drones and machine learning |
| data science in logistics: Utilizing Blockchain Technologies in Manufacturing and Logistics Management S B Goyal, Nijalingappa Pradeep, Piyush Kumar Shukla, Mangesh M Ghonge, Renjith V Ravi, 2022-01-21 Blockchain technology has the potential to utterly transform supply chains, streamline processes, and improve the whole of security. Manufacturers across the globe face challenges with forecasting demand, controlling inventory, and accelerating digital transformation to cater to the challenges of changing market dynamics and evolving customer expectations. Hence, blockchain should be seen as an investment in future-readiness and customer-centricity, not as an experimental technology. Utilizing Blockchain Technologies in Manufacturing and Logistics Management explores the strengths of blockchain adaptation in manufacturing industries and logistics management, which include product traceability, supply chain transparency, compliance monitoring, and auditability, and also examines the current open issues and future research trends of blockchain. Leveraging blockchain technology into a manufacturing enterprise can enhance its security and reduce the rates of systematic failures. Covering topics such as fraud detection, Industry 4.0, and security threats, this book is a ready premier reference for graduate and post-graduate students, academicians, researchers, industrialists, consultants, and entrepreneurs, as well as micro, small, and medium enterprises. |
| data science in logistics: Transportation Analytics in the Era of Big Data Satish V. Ukkusuri, Chao Yang, 2018-07-28 This book presents papers based on the presentations and discussions at the international workshop on Big Data Smart Transportation Analytics held July 16 and 17, 2016 at Tongji University in Shanghai and chaired by Professors Ukkusuri and Yang. The book is intended to explore a multidisciplinary perspective to big data science in urban transportation, motivated by three critical observations: The rapid advances in the observability of assets, platforms for matching supply and demand, thereby allowing sharing networks previously unimaginable. The nearly universal agreement that data from multiple sources, such as cell phones, social media, taxis and transit systems can allow an understanding of infrastructure systems that is critically important to both quality of life and successful economic competition at the global, national, regional, and local levels. There is presently a lack of unifying principles and methodologies that approach big data urban systems. The workshop brought together varied perspectives from engineering, computational scientists, state and central government, social scientists, physicists, and network science experts to develop a unifying set of research challenges and methodologies that are likely to impact infrastructure systems with a particular focus on transportation issues. The book deals with the emerging topic of data science for cities, a central topic in the last five years that is expected to become critical in academia, industry, and the government in the future. There is currently limited literature for researchers to know the opportunities and state of the art in this emerging area, so this book fills a gap by synthesizing the state of the art from various scholars and help identify new research directions for further study. |
| data science in logistics: The Digital Supply Chain Bart L. MacCarthy, Dmitry Ivanov, 2022-06-09 The Digital Supply Chain is a thorough investigation of the underpinning technologies, systems, platforms and models that enable the design, management, and control of digitally connected supply chains. The book examines the origin, emergence and building blocks of the Digital Supply Chain, showing how and where the virtual and physical supply chain worlds interact. It reviews the enabling technologies that underpin digitally controlled supply chains and examines how the discipline of supply chain management is affected by enhanced digital connectivity, discussing purchasing and procurement, supply chain traceability, performance management, and supply chain cyber security. The book provides a rich set of cases on current digital practices and challenges across a range of industrial and business sectors including the retail, textiles and clothing, the automotive industry, food, shipping and international logistics, and SMEs. It concludes with research frontiers, discussing network science for supply chain analysis, challenges in Blockchain applications and in digital supply chain surveillance, as well as the need to re-conceptualize supply chain strategies for digitally transformed supply chains. |
| data science in logistics: Global Logistics James H. Bookbinder, 2012-06-30 This book offers complete coverage of logistics, examining modes, general issues, logistics in specific regions, free-trade zones, innovations in international logistics, case studies and a look at the future. |
| data science in logistics: Logistics Transportation Systems MD Sarder, 2020-10-17 Logistics Transportation Systems compiles multiple topics on transportation logistics systems from both qualitative and quantitative perspectives, providing detailed examples of real-world logistics workflows. It explores the key concepts and problem-solving techniques required by researchers and logistics professionals to effectively manage the continued expansion of logistics transportation systems, which is expected to reach an estimated 25 billion tons in the United States alone by 2045. This book provides an ample understanding of logistics transportation systems, including basic concepts, in-depth modeling analysis, and network analysis for researchers and practitioners. In addition, it covers policy issues related to transportation logistics, such as security, rules and regulations, and emerging issues including reshoring. This book is an ideal guide for academic researchers and both undergraduate and graduate students in transportation modeling, supply chains, planning, and systems. It is also useful to transportation practitioners involved in planning, feasibility studies, consultation and policy for transportation systems, logistics, and infrastructure. - Provides real-world examples of logistics systems solutions for multiple transportation modes, including seaports, rail, barge, road, pipelines, and airports - Covers a wide range of business aspects, including customer service, cost, and decision analysis - Features key-term definitions, concept overviews, discussions, and analytical problem-solving |
| data science in logistics: Software Engineering in IoT, Big Data, Cloud and Mobile Computing Haengkon Kim, Roger Lee, 2020-12-26 This edited book presents scientific results of the International Semi-Virtual Workshop on Software Engineering in IoT, Big data, Cloud and Mobile Computing (SE-ICBM 2020) which was held on October 15, 2020, at Soongsil University, Seoul, Korea. The aim of this workshop was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. Research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. The workshop organizers selected the best papers from those papers accepted for presentation at the workshop. The papers were chosen based on review scores submitted by members of the program committee and underwent further rigorous rounds of review. From this second round of review, 17 of the conference’s most promising papers are then published in this Springer (SCI) book and not the conference proceedings. We impatiently await the important contributions that we know these authors will bring to the field of computer and information science. |
| data science in logistics: Transportation, Logistics, and Supply Chain Management in Home Healthcare: Emerging Research and Opportunities Euchi, Jalel, 2019-09-27 With advancing technology and the digitization of the modern era, businesses are required to adopt the latest innovations computer science and information technology have to offer. The field of home healthcare must utilize the finest available operations management systems in order to remain relevant in a globalized world while also providing the best treatment possible to its patients. Transportation, Logistics, and Supply Chain Management in Home Healthcare: Emerging Research and Opportunities is an essential reference source that provides theoretical and empirical research on logistics management and transportation and scheduling routing and their applications in home healthcare and logistics. While highlighting topics such as hybrid energy, scheduling optimization, and forecasting techniques, this book is ideally designed for outpatient doctors and nurses, transportation professionals, logisticians, home healthcare managers, computer scientists, logistic engineers, health practitioners, academicians, researchers, and students. |
| data science in logistics: Streaming Architecture Ted Dunning, Ellen Friedman, 2016-05-10 More and more data-driven companies are looking to adopt stream processing and streaming analytics. With this concise ebook, you’ll learn best practices for designing a reliable architecture that supports this emerging big-data paradigm. Authors Ted Dunning and Ellen Friedman (Real World Hadoop) help you explore some of the best technologies to handle stream processing and analytics, with a focus on the upstream queuing or message-passing layer. To illustrate the effectiveness of these technologies, this book also includes specific use cases. Ideal for developers and non-technical people alike, this book describes: Key elements in good design for streaming analytics, focusing on the essential characteristics of the messaging layer New messaging technologies, including Apache Kafka and MapR Streams, with links to sample code Technology choices for streaming analytics: Apache Spark Streaming, Apache Flink, Apache Storm, and Apache Apex How stream-based architectures are helpful to support microservices Specific use cases such as fraud detection and geo-distributed data streams Ted Dunning is Chief Applications Architect at MapR Technologies, and active in the open source community. He currently serves as VP for Incubator at the Apache Foundation, as a champion and mentor for a large number of projects, and as committer and PMC member of the Apache ZooKeeper and Drill projects. Ted is on Twitter as @ted_dunning. Ellen Friedman, a committer for the Apache Drill and Apache Mahout projects, is a solutions consultant and well-known speaker and author, currently writing mainly about big data topics. With a PhD in Biochemistry, she has years of experience as a research scientist and has written about a variety of technical topics. Ellen is on Twitter as @Ellen_Friedman. |
| data science in logistics: Logistics 4.0 Turan Paksoy, Cigdem Gonul Kochan, Sadia Samar Ali, 2020-12-17 Industrial revolutions have impacted both, manufacturing and service. From the steam engine to digital automated production, the industrial revolutions have conduced significant changes in operations and supply chain management (SCM) processes. Swift changes in manufacturing and service systems have led to phenomenal improvements in productivity. The fast-paced environment brings new challenges and opportunities for the companies that are associated with the adaptation to the new concepts such as Internet of Things (IoT) and Cyber Physical Systems, artificial intelligence (AI), robotics, cyber security, data analytics, block chain and cloud technology. These emerging technologies facilitated and expedited the birth of Logistics 4.0. Industrial Revolution 4.0 initiatives in SCM has attracted stakeholders’ attentions due to it is ability to empower using a set of technologies together that helps to execute more efficient production and distribution systems. This initiative has been called Logistics 4.0 of the fourth Industrial Revolution in SCM due to its high potential. Connecting entities, machines, physical items and enterprise resources to each other by using sensors, devices and the internet along the supply chains are the main attributes of Logistics 4.0. IoT enables customers to make more suitable and valuable decisions due to the data-driven structure of the Industry 4.0 paradigm. Besides that, the system’s ability of gathering and analyzing information about the environment at any given time and adapting itself to the rapid changes add significant value to the SCM processes. In this peer-reviewed book, experts from all over the world, in the field present a conceptual framework for Logistics 4.0 and provide examples for usage of Industry 4.0 tools in SCM. This book is a work that will be beneficial for both practitioners and students and academicians, as it covers the theoretical framework, on the one hand, and includes examples of practice and real world. |
| data science in logistics: Logistics Clusters Yossi Sheffi, 2012-09-14 How logistics clusters can create jobs while providing companies with competitive advantage. Why is Memphis home to hundreds of motor carrier terminals and distribution centers? Why does the tiny island-nation of Singapore handle a fifth of the world's maritime containers and half the world's annual supply of crude oil? Which jobs can replace lost manufacturing jobs in advanced economies? Some of the answers to these questions are rooted in the phenomenon of logistics clusters—geographically concentrated sets of logistics-related business activities. In this book, supply chain management expert Yossi Sheffi explains why Memphis, Singapore, Chicago, Rotterdam, Los Angeles, and scores of other locations have been successful in developing such clusters while others have not. Sheffi outlines the characteristic “positive feedback loop” of logistics clusters development and what differentiates them from other industrial clusters; how logistics clusters “add value” by generating other industrial activities; why firms should locate their distribution and value-added activities in logistics clusters; and the proper role of government support, in the form of investment, regulation, and trade policy. Sheffi also argues for the most important advantage offered by logistics clusters in today's recession-plagued economy: jobs, many of them open to low-skilled workers, that are concentrated locally and not “offshorable.” These logistics clusters offer what is rare in today's economy: authentic success stories. For this reason, numerous regional and central governments as well as scores of real estate developers are investing in the development of such clusters. View a trailer for the book at: http://techtv.mit.edu/videos/22284-logistics-clusters-yossi-sheffi |
| data science in logistics: Supply Chain Management Donald Waters, 2019-02-12 A broad general introduction to all essential aspects of logistics and supply chain management, set within the wider business context. The book uses well-developed pedagogy and numerous case studies, guiding the reader through the subject by retaining a strong focus on the application of theory and practical situations. |
| data science in logistics: Modern Trends and Research in Intermodal Transportation Aleksander Sładkowski, 2022-01-21 This book presents a specific technical solution, called intermodal transport, which became the basic technological solution that made it possible to provide global interregional transport. Every day, new technical, technological, and organizational solutions appear that significantly affect the further development of this industry. However, there are certain local differences between regions. In addition, an essential factor is the exchange of experience between scientists from different countries. Accordingly, the purpose of this monothematic book is to acquaint readers with the achievements of scientists dealing with this topic and living in different regions. Scientists and specialists from Poland, Germany, Great Britain, USA, Romania, Bulgaria, Russia, Italy, Kazakhstan, and Lithuania participated in the writing of individual chapters of this book. This book is intended for professionals, teachers, students, and others who are interested in new approaches to solving transport problems. |
| data science in logistics: Innovations in Logistics and Supply Chain Management Technologies for Dynamic Economies Luo, ZongWei, 2012-03-31 This book disseminates supply chain management and applied logistic theories, technology development, innovation, and transformation in various economy sectors upon current, advancing technological opportunities and market imperatives--Provided by publisher. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
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A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. …
