| data mining in supply chain management: 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 mining in supply chain management: 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 mining in supply chain management: 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 mining in supply chain management: Business Management and Communication Perspectives in Industry 4.0 Özbebek Tunç, Ay?egül, Aslan, P?nar, 2019-07-26 Changes in the global economy bring new dynamics, concepts, and implications that require digitalization and adaptation. The new “normal” has changed, and companies must adopt such strategies if they want to survive in the ever-changing business environments. Business Management and Communication Perspectives in Industry 4.0 is a pivotal reference source that provides vital research on the planning, implementing, and evaluating of strategies for the new industry standards. While highlighting topics such as artificial intelligence, digital leadership, and management science, this publication theorizes about tomorrow’s business and communication environments based on the past and present of the concepts. This book is ideally designed for managers, researchers, educators, students, professionals, and policymakers seeking current research on blending managerial and communicational concepts with a multidisciplinary approach. |
| data mining in supply chain management: Supply Chain Management Ling Li, 2007 Integrates the theory and practices of supply chain management. This book focuses on how to build a competitive supply chain using viable management strategies, operational models, decision-making techniques, and information technology. It also includes initiatives such as e-commerce, collaborative planning, forecasting, and replenishment (CPFR). |
| data mining in supply chain management: Applications and Innovations in Intelligent Systems XV Richard Ellis, Tony Allen, Miltos Petridis, 2008-08-12 |
| data mining in supply chain management: Supply Chain Optimization, Management and Integration: Emerging Applications Wang, John, 2010-11-30 Our rapidly changing world has forced business practitioners, in corporation with academic researchers, to respond quickly and develop effective solution methodologies and techniques to handle new challenges in supply chain systems. Supply Chain Optimization, Management and Integration: Emerging Applications presents readers with a rich collection of ideas from researchers who are bridging the gap between the latest in information technology and supply chain management. This book includes theoretical, analytical, and empirical research, comprehensive reviews of relevant research, and case studies of effective applications in the field of SCM. The use of new technologies, methods, and techniques are emphasized by those who have worked with supply chain management across the world for those in the field of information systems. |
| data mining in supply chain management: The basics of supply chain management Dr. Vivek Kumar, Mr. Hari Bhagat, Mr. Vikash Kumar, 2021-03-09 This book “The basics of Supply chain management” can provide the first step in understanding the world of the supply chain. Supply chain concepts are explained from the basic with widespread coverage of the methodology and key strategies drivers in various processes involved in designing and implementation of the supply chain. The book can be a game-changer for new entrants in the field of the supply chain. |
| data mining in supply chain management: Supply Chain Management For Dummies Daniel Stanton, 2017-11-29 Everyone can impact the supply chain Supply Chain Management For Dummies helps you connect the dots between things like purchasing, logistics, and operations to see how the big picture is affected by seemingly isolated inefficiencies. Your business is a system, made of many moving parts that must synchronize to most efficiently meet the needs of your customers—and your shareholders. Interruptions in one area ripple throughout the entire operation, disrupting the careful coordination that makes businesses successful; that's where supply chain management (SCM) comes in. SCM means different things to different people, and many different models exist to meet the needs of different industries. This book focuses on the broadly-applicable Supply Chain Operations Reference (SCOR) Model: Plan, Source, Make, Deliver, Return, and Enable, to describe the basic techniques and key concepts that keep businesses running smoothly. Whether you're in sales, HR, or product development, the decisions you make every day can impact the supply chain. This book shows you how to factor broader impact into your decision making process based on your place in the system. Improve processes by determining your metrics Choose the right software and implement appropriate automation Evaluate and mitigate risks at all steps in the supply chain Help your business function as a system to more effectively meet customer needs We tend to think of the supply chain as suppliers, logistics, and warehousing—but it's so much more than that. Every single person in your organization, from the mailroom to the C-suite, can work to enhance or hinder the flow. Supply Chain Management For Dummies shows you what you need to know to make sure your impact leads to positive outcomes. |
| data mining in supply chain management: Encyclopedia of Data Warehousing and Mining Wang, John, 2005-06-30 Data Warehousing and Mining (DWM) is the science of managing and analyzing large datasets and discovering novel patterns and in recent years has emerged as a particularly exciting and industrially relevant area of research. Prodigious amounts of data are now being generated in domains as diverse as market research, functional genomics and pharmaceuticals; intelligently analyzing these data, with the aim of answering crucial questions and helping make informed decisions, is the challenge that lies ahead. The Encyclopedia of Data Warehousing and Mining provides a comprehensive, critical and descriptive examination of concepts, issues, trends, and challenges in this rapidly expanding field of data warehousing and mining (DWM). This encyclopedia consists of more than 350 contributors from 32 countries, 1,800 terms and definitions, and more than 4,400 references. This authoritative publication offers in-depth coverage of evolutions, theories, methodologies, functionalities, and applications of DWM in such interdisciplinary industries as healthcare informatics, artificial intelligence, financial modeling, and applied statistics, making it a single source of knowledge and latest discoveries in the field of DWM. |
| data mining in supply chain management: Supply Chain Strategy Edward H. Frazelle, 2001-10-16 High-Tech and High-Touch Logistics Solutions for Supply Chain Challenges In today's fast-paced and customer-oriented business environment, superior supply chain performance is a prerequisite to getting and staying competitive. Supply Chain Strategy is based on world-class logistics practices in place in successful supply chain organizations, the latest academic breakthroughs in logistics system design, and the logic of logistics. It presents the proven pillars of success in logistics and supply chain management. Part of McGraw-Hill's Logistics Management Library, Supply Chain Strategy is organized according to author Dr. Ed Frazelle's breakthrough logistics master planning methodology. The methodology leads to metrics, process designs, system designs, and organizational strategies for total supply chain management, total logistics management, customer response, inventory planning and management, supply, transportation, and warehousing. Concise yet complete, Dr. Frazelle's book shows how to develop a comprehensive logistics and supply chain strategy, one that will both complement and support a company's strategic objectives and long-term success. Logisticsthe flow of material, information, and money between consumers and suppliershas become a key boardroom topic. It is the subject of cover features in business publications from Wall Street Journal to BusinessWeek. Annual global logistics expenditures exceed $3.5 trillion, nearly 20 percent of the world's GDP, making logistics perhaps the last frontier for major corporations to significantly increase shareholder and customer value. And at the heart of every effort to improve organizational logistics performance? Supply chain efficiency. Supply Chain Strategy is today's most comprehensive resource for up-to-the-minute thinking and practices on developing supply chain strategies that support a company's overall objectives. Covering world-class practices and systems, taken from the files of Coca-Cola, Wal-Mart, General Electric, and other companies, it covers essential supply chain subjects including: Logistics data miningfor identifying the root cause of material and information flow problems, pinpointing opportunities for process improvements, and providing an objective basis for project-team decision making Inventory planning and managementpresenting metrics, processes, and systems for forecasting, demand planning, and inventory control, yielding lower inventory levels and improved customer service Logistics information systems and Web-based logisticshelping to substitute information for inventory and work content Transportation and distributionfor connecting sourcing locations with customers at the lowest cost by, among other things, leveraging private and third-party transportation systems Logistics organization developmentincluding the seven disciplines that link enterprises across the supply chain, as well as logistics activities within those enterprises Supply Chain Strategy explains and demonstrates how decision makers can use today's technology to enhance key logistics systems at every point in the supply chain, from the time an idea or product is conceived through its delivery to the final user. It describes the major steps in developing an effective, workable logistics management programone that will reduce operating expenses, minimize capital investment, and improve overall customer service and satisfaction. |
| data mining in supply chain management: Techniques, Tools and Methodologies Applied to Global Supply Chain Ecosystems Jorge Luis García-Alcaraz, Cuauhtémoc Sánchez-Ramírez, Liliana Avelar-Sosa, Giner Alor-Hernández, 2019-08-29 This book presents the latest developments concerning techniques, tools, and methodologies in supply chain ecosystems. It gathers contributions from a variety of experts, who analyze a range of case studies and industrial sectors such as manufacturing, energy, agricultural, healthcare, humanitarian logistics, and urban goods distribution, to name but a few. The book is chiefly intended to meet the needs of two sectors: firstly, the academic sector, so as to familiarize students, professors, and researchers with the tools that are now being used to optimize supply chains; and secondly, the industrial and managerial sector, so that supply chain management practitioners can benefit from methods and tools that are yielding valuable results in other contexts. |
| data mining in supply chain management: Encyclopedia of Organizational Knowledge, Administration, and Technology Khosrow-Pour D.B.A., Mehdi, 2020-09-29 For any organization to be successful, it must operate in such a manner that knowledge and information, human resources, and technology are continually taken into consideration and managed effectively. Business concepts are always present regardless of the field or industry – in education, government, healthcare, not-for-profit, engineering, hospitality/tourism, among others. Maintaining organizational awareness and a strategic frame of mind is critical to meeting goals, gaining competitive advantage, and ultimately ensuring sustainability. The Encyclopedia of Organizational Knowledge, Administration, and Technology is an inaugural five-volume publication that offers 193 completely new and previously unpublished articles authored by leading experts on the latest concepts, issues, challenges, innovations, and opportunities covering all aspects of modern organizations. Moreover, it is comprised of content that highlights major breakthroughs, discoveries, and authoritative research results as they pertain to all aspects of organizational growth and development including methodologies that can help companies thrive and analytical tools that assess an organization’s internal health and performance. Insights are offered in key topics such as organizational structure, strategic leadership, information technology management, and business analytics, among others. The knowledge compiled in this publication is designed for entrepreneurs, managers, executives, investors, economic analysts, computer engineers, software programmers, human resource departments, and other industry professionals seeking to understand the latest tools to emerge from this field and who are looking to incorporate them in their practice. Additionally, academicians, researchers, and students in fields that include but are not limited to business, management science, organizational development, entrepreneurship, sociology, corporate psychology, computer science, and information technology will benefit from the research compiled within this publication. |
| data mining in supply chain management: Supply Chain Management and Advanced Planning Hartmut Stadtler, Christoph Kilger, 2007-10-25 With a wealth of updated material, rewritten chapters and additional case studies, this fourth edition of a hugely important work gives a broad and up-to-date overview of the concepts underlying APS. Special emphasis is given to modeling supply chains and implementing APS successfully in industrial contexts. What’s more, readers’ understanding is enhanced by several case studies covering a wide range of industrial sectors. What makes this book so crucial is that Supply Chain Management, Enterprise Resources Planning (ERP), and Advanced Planning Systems (APS) are concepts that must be mastered in order to organize and optimize the flow of goods, materials, information and funds. Here, leading experts provide insights into the concepts underlying APS. |
| data mining in supply chain management: 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 mining in supply chain management: Practical E-Manufacturing and Supply Chain Management Gerhard Greeff, Ranjan Ghoshal, 2004-08-11 New technologies are revolutionising the way manufacturing and supply chain management are implemented. These changes are delivering manufacturing firms the competitive advantage of a highly flexible and responsive supply chain and manufacturing system to ensure that they meet the high expectations of their customers, who, in today's economy, demand absolutely the best service, price, delivery time and product quality.To make e-manufacturing and supply chain technologies effective, integration is needed between various, often disparate systems. To understand why this is such an issue, one needs to understand what the different systems or system components do, their objectives, their specific focus areas and how they interact with other systems. It is also required to understand how these systems evolved to their current state, as the concepts used during the early development of systems and technology tend to remain in place throughout the life-cycle of the systems/technology. This book explores various standards, concepts and techniques used over the years to model systems and hierarchies in order to understand where they fit into the organization and supply chain. It looks at the specific system components and the ways in which they can be designed and graphically depicted for easy understanding by both information technology (IT) and non-IT personnel.Without a good implementation philosophy, very few systems add any real benefit to an organization, and for this reason the ways in which systems are implemented and installation projects managed are also explored and recommendations are made as to possible methods that have proven successful in the past. The human factor and how that impacts on system success are also addressed, as is the motivation for system investment and subsequent benefit measurement processes.Finally, the vendor/user supply/demand within the e-manufacturing domain is explored and a method is put forward that enables the reduction of vendor bias during the vendor selection process.The objective of this book is to provide the reader with a good understanding regarding the four critical factors (business/physical processes, systems supporting the processes, company personnel and company/personal performance measures) that influence the success of any e-manufacturing implementation, and the synchronization required between these factors.· Discover how to implement the flexible and responsive supply chain and manufacturing execution systems required for competitive and customer-focused manufacturing· Build a working knowledge of the latest plant automation, manufacturing execution systems (MES) and supply chain management (SCM) design techniques· Gain a fuller understanding of the four critical factors (business and physical processes, systems supporting the processes, company personnel, performance measurement) that influence the success of any e-manufacturing implementation, and how to evaluate and optimize all four factors |
| data mining in supply chain management: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates |
| data mining in supply chain management: Developing Quality Complex Database Systems: Practices, Techniques and Technologies Becker, Shirley, 2000-07-01 Developing Quality Complex Database Systems: Practices, Techniques and Technologies provides opportunities for improving today's database systems using innovative development practices, tools and techniques. An emphasis is placed on organizational and management issues. |
| data mining in supply chain management: Inventory and Supply Chain Management with Forecast Updates Suresh P. Sethi, Houmin Yan, Hanqin Zhang, 2006-03-30 Real problems are formulated into tractable mathematical models, which allow for an analysis of various approaches. Attention is focused on solutions. Provides a unified treatment of the models discussed , presents a critique of the existing results, and points out potential research directions. |
| data mining in supply chain management: Artificial Intelligence. An International Perspective Max Bramer, 2009-09-19 Artificial Intelligence (AI) is a rapidly growing inter-disciplinary field with a long and distinguished history that involves many countries and considerably pre-dates the development of computers. It can be traced back at least as far as Ancient Greece and has evolved over time to become a major subfield of computer science in general. This state-of-the-art survey not only serves as a position paper on the field from the viewpoint of expert members of the IFIP Technical Committee 12, its Working Groups and their colleagues, but also presents overviews of current work in different countries. The chapters describe important relatively new or emerging areas of work in which the authors are personally involved, including text and hypertext categorization; autonomous systems; affective intelligence; AI in electronic healthcare systems; artifact-mediated society and social intelligence design; multilingual knowledge management; agents, intelligence and tools; intelligent user profiling; and supply chain business intelligence. They provide an interesting international perspective on where this significant field is going at the end of the first decade of the twenty-first century. |
| data mining in supply chain management: 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 mining in supply chain management: Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods Minis, Ioannis, Zeimpekis, Vasileios, Dounias, Georgios, Ampazis, Nicholas, 2010-12-31 Computational Intelligence (CI) is a term corresponding to a new generation of algorithmic methodologies in artificial intelligence, which combines elements of learning, adaptation, evolution and approximate (fuzzy) reasoning to create programs that can be considered intelligent. Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods presents computational intelligence methods for addressing supply chain issues. Emphasis is given to techniques that provide effective solutions to complex supply chain problems and exhibit superior performance to other methods of operations research. |
| data mining in supply chain management: Information Technologies, Methods, and Techniques of Supply Chain Management John Wang, 2012 This book has compiled chapters from experts from around the world in the field of supply chain management and provides a vital compendium of the latest research, case studies, frameworks, methodologies, architectures, and best practices within the field of supply chain management--Provided by publisher. |
| data mining in supply chain management: 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 mining in supply chain management: Research Anthology on Big Data Analytics, Architectures, and Applications Information Resources Management Association, 2022 Society is now completely driven by data with many industries relying on data to conduct business or basic functions within the organization. With the efficiencies that big data bring to all institutions, data is continuously being collected and analyzed. However, data sets may be too complex for traditional data-processing, and therefore, different strategies must evolve to solve the issue. The field of big data works as a valuable tool for many different industries. The Research Anthology on Big Data Analytics, Architectures, and Applications is a complete reference source on big data analytics that offers the latest, innovative architectures and frameworks and explores a variety of applications within various industries. Offering an international perspective, the applications discussed within this anthology feature global representation. Covering topics such as advertising curricula, driven supply chain, and smart cities, this research anthology is ideal for data scientists, data analysts, computer engineers, software engineers, technologists, government officials, managers, CEOs, professors, graduate students, researchers, and academicians. |
| data mining in supply chain management: Social Implications of Data Mining and Information Privacy: Interdisciplinary Frameworks and Solutions Eyob, Ephrem, 2009-01-31 This book serves as a critical source to emerging issues and solutions in data mining and the influence of social factors--Provided by publisher. |
| data mining in supply chain management: 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 mining in supply chain management: Supply Chain Management: Concepts, Methodologies, Tools, and Applications Management Association, Information Resources, 2012-12-31 In order to keep up with the constant changes in technology, business have adopted supply chain management to improve competitive strategies on a strategic and operational level. Supply Chain Management: Concepts, Methodologies, Tools, and Applications is a reference collection which highlights the major concepts and issues in the application and advancement of supply chain management. Including research from leading scholars, this resource will be useful for academics, students, and practitioners interested in the continuous study of supply chain management and its influences. |
| data mining in supply chain management: Sustainable Supply Chain Management Joëlle Morana, 2013-05-06 It is commonly recognized that logistics has become a major strategic issue for all companies, whether they are part of the primary, secondary or tertiary sector. Faced with the external pressures of globalization and competition, logistics optimizes processes and reduces production and delivery cycles. The use of Sustainable Supply Chain Management (SuSCM) is now increasingly at the center of thought, due to the numerous factors favoring its implementation: requests from various stakeholders, governmental pressures (decrees, laws, regulations, etc.), environmental pressures (pollution, disappearance of fuel fossils, etc.) and societal pressures (reputation/image, protection, etc.). However, there are still obstacles to the implementation of SuSCM, including significant costs, the complexity of coordination and the lack of communication within the whole supply chain. Nevertheless, it should nowadays be included by any organization in its decisions towards a strategic approach towards sustainability. This book presents each economic, environmental and societal aspect of SuSCM. By considering each of these dimensions separately, the primary objective is to facilitate the implementation of the elements that make it up. Readers are also provided with several “strategic interpretive lenses” to be able to perform audits and diagnostics of each component. Contents: 1. The Economic Aspect of Sustainable Supply Chain Management. 2. The Environmental Aspect of Sustainable Supply Chain Management. 3. The Social/Societal Aspect of Sustainable Supply Chain Management. 4. Sustainable Supply Chain Management Balanced Scorecard. About the Authors Joëlle Morana is Lecturer in management science, attached to the Laboratoire d’Economie des Transports (Transport Economy Laboratory) at University Lumière Lyon II in France. Her fields of research concern economic, environmental and societal logistics. |
| data mining in supply chain management: Data Preparation for Data Mining Dorian Pyle, 1999-03-22 This book focuses on the importance of clean, well-structured data as the first step to successful data mining. It shows how data should be prepared prior to mining in order to maximize mining performance. |
| data mining in supply chain management: Introduction to Financial Forecasting in Investment Analysis John B. Guerard, Jr., 2013-01-04 Forecasting—the art and science of predicting future outcomes—has become a crucial skill in business and economic analysis. This volume introduces the reader to the tools, methods, and techniques of forecasting, specifically as they apply to financial and investing decisions. With an emphasis on earnings per share (eps), the author presents a data-oriented text on financial forecasting, understanding financial data, assessing firm financial strategies (such as share buybacks and R&D spending), creating efficient portfolios, and hedging stock portfolios with financial futures. The opening chapters explain how to understand economic fluctuations and how the stock market leads the general economic trend; introduce the concept of portfolio construction and how movements in the economy influence stock price movements; and introduce the reader to the forecasting process, including exponential smoothing and time series model estimations. Subsequent chapters examine the composite index of leading economic indicators (LEI); review financial statement analysis and mean-variance efficient portfolios; and assess the effectiveness of analysts’ earnings forecasts. Using data from such firms as Intel, General Electric, and Hitachi, Guerard demonstrates how forecasting tools can be applied to understand the business cycle, evaluate market risk, and demonstrate the impact of global stock selection modeling and portfolio construction. |
| data mining in supply chain management: Developing Churn Models Using Data Mining Techniques and Social Network Analysis Klepac, Goran, 2014-07-31 This book provides an in-depth analysis of attrition modeling relevant to business planning and management, offering insightful and detailed explanation of best practices, tools, and theory surrounding churn prediction and the integration of analytic tools--Provided by publisher. |
| data mining in supply chain management: Essentials of Supply Chain Management Michael H. Hugos, 2018-02-22 The bestselling guide to the field, updated with the latest innovations Essentials of Supply Chain Management is the definitive guide to the field, providing both broad coverage and necessary detail from a practical, real-world perspective. From clear explanation of fundamental concepts to insightful discussion of supply chain innovation, this book offers students and professionals a comprehensive introduction with immediately-applicable understanding. The fourth edition has been updated to reflect the current state of the field, with coverage of the latest technologies and new case studies that illustrate critical concepts in action. Organized for easy navigation and ease-of-use, this invaluable guide also serves as a quick reference for managers in the field seeking tips and techniques for maximizing efficiency and turning the supply chain into a source of competitive advantage. The supply chain underpins the entire structure of manufacturing and retailing. Well-run, it can help a company become a global behemoth—or, if poorly-managed, it can sink a company before the product ever sees the light of day. The supply chain involves many moving parts, constantly-changing variables, and a network of other business that may have different priorities and interests—keeping it all running smoothly is a complex, but immensely powerful skill. This book takes you inside the supply chain to show you what you need to know. Understand the fundamental concepts behind supply chain management Learn how supply chains work, and how to measure their performance Explore the ways in which innovation is improving supply chains around the world Examine the supply chain as a source of competitive advantage Whether you’re at the front or the back of your supply chain, your business is affected by every other company and event in the chain. Deep understanding and a host of practical skills are required to accurately predict, react to, and manage the ever-changing stream of events that could potentially disrupt the flow. Essentials of Supply Chain Management prepares you to take on the challenge and succeed. |
| data mining in supply chain management: Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications Wang, John, 2008-05-31 In recent years, the science of managing and analyzing large datasets has emerged as a critical area of research. In the race to answer vital questions and make knowledgeable decisions, impressive amounts of data are now being generated at a rapid pace, increasing the opportunities and challenges associated with the ability to effectively analyze this data. |
| data mining in supply chain management: Business Intelligence Carlo Vercellis, 2011-08-10 Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all factors that affect a business, such as customers, competitors, business partners, economic environment, and internal operations, therefore enabling optimal decisions to be made. Business Intelligence provides readers with an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence. This book: Combines detailed coverage with a practical guide to the mathematical models and analysis methodologies of business intelligence. Covers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance evaluation. Is made accessible to readers through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies. Explains how to utilise mathematical models and analysis models to make effective and good quality business decisions. This book is aimed at postgraduate students following data analysis and data mining courses. Researchers looking for a systematic and broad coverage of topics in operations research and mathematical models for decision-making will find this an invaluable guide. |
| data mining in supply chain management: Encyclopedia of Information Science and Technology, First Edition Khosrow-Pour, D.B.A., Mehdi, 2005-01-31 Comprehensive coverage of critical issues related to information science and technology. |
| data mining in supply chain management: The Applied Business Analytics Casebook Matthew J. Drake, 2014 The first collection of cases on big data analytics for supply chain, operations research, and operations management, this reference puts readers in the position of the analytics professional and decision-maker. Perfect for students, practitioners, and certification candidates in SCM, OM, and OR, these short, focused, to-the-point case studies illustrate the entire decision-making process. They provide realistic opportunities to perform analyses, interpret output, and recommend an optimal course of action. Contributed by leading big data experts, the cases in The Applied Business Analytics Casebook covers: Forecasting and statistical analysis: time series forecasting models, regression models, data visualization, and hypothesis testing Optimization and simulation: linear, integer, and nonlinear programming; Monte Carlo simulation and risk analysis; and stochastic optimization Decision analysis: decision making under uncertainty; expected value of perfect information; decision trees; game theory models; AHP; and multi-criteria decision making Advanced business analytics: data warehousing/mining; text mining; neural networks; financial analytics; CRM analytics; and revenue management models |
| data mining in supply chain management: Supply Chain Management Models Hamed Fazlollahtabar, 2018-01-02 Supply Chain Management (SCM) is a wide field in which several specialties are included. In general, operations and production management players use SCM to organize the problems and analyze the solution approaches. Due to these points, a reference which can encompass a range of problems and their modelling approaches is required. This book will contain three general sections of forward, reverse, intelligent, and uncertain problems. While the book provides different problems in the three commonly used categories in SCM, it is very helpful for the readers to find out, or adapt their own application studies to the ones given in the book and employ the corresponding modeliing approach. |
| data mining in supply chain management: New Supply Chain Agenda Reuben Slone, Paul Dittmann, John Mentzer, 2010-04-27 Is your company delivering products to customers at the right time, place, and price—with the best possible availability and lowest possible cost and working capital? If not, you’re probably alienating your customers and suppliers, eroding shareholder value, and losing control of your fixed costs. These dangerous mistakes can put you out of business. In The New Supply Chain Agenda, Reuben Slone, J. Paul Dittmann, and John Mentzer explain how to reinvent your supply chain to avoid those errors—and turn your supply chain into a competitive weapon that produces unprecedented economic profit for your firm. Drawing on a wealth of company examples, the authors show how to activate the five levers of supply chain excellence: • Putting the right people with the right skills in the right jobs • Leveraging supply chain technologies such as system optimization and visibility tools • Eliminating cross-functional disconnects, including SKU proliferation • Collaborating with suppliers and customers to generate a seamless flow of information and supply chain improvements • Managing supply chain projects skillfully Apply the steps in this book, and you build a supply chain that delivers as it should—without leaving money on the table. |
| data mining in supply chain management: The Fourth Industrial Revolution Klaus Schwab, 2017-01-03 World-renowned economist Klaus Schwab, Founder and Executive Chairman of the World Economic Forum, explains that we have an opportunity to shape the fourth industrial revolution, which will fundamentally alter how we live and work. Schwab argues that this revolution is different in scale, scope and complexity from any that have come before. Characterized by a range of new technologies that are fusing the physical, digital and biological worlds, the developments are affecting all disciplines, economies, industries and governments, and even challenging ideas about what it means to be human. Artificial intelligence is already all around us, from supercomputers, drones and virtual assistants to 3D printing, DNA sequencing, smart thermostats, wearable sensors and microchips smaller than a grain of sand. But this is just the beginning: nanomaterials 200 times stronger than steel and a million times thinner than a strand of hair and the first transplant of a 3D printed liver are already in development. Imagine “smart factories” in which global systems of manufacturing are coordinated virtually, or implantable mobile phones made of biosynthetic materials. The fourth industrial revolution, says Schwab, is more significant, and its ramifications more profound, than in any prior period of human history. He outlines the key technologies driving this revolution and discusses the major impacts expected on government, business, civil society and individuals. Schwab also offers bold ideas on how to harness these changes and shape a better future—one in which technology empowers people rather than replaces them; progress serves society rather than disrupts it; and in which innovators respect moral and ethical boundaries rather than cross them. We all have the opportunity to contribute to developing new frameworks that advance progress. |
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 and open science. This will …
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 data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
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 Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
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 and open science. This will …
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 data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
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 Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
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 …
