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| data analytics in inventory management: Inventory Analytics Roberto Rossi, 2021-05-24 Inventory Analytics provides a comprehensive and accessible introduction to the theory and practice of inventory control – a significant research area central to supply chain planning. The book outlines the foundations of inventory systems and surveys prescriptive analytics models for deterministic inventory control. It further discusses predictive analytics techniques for demand forecasting in inventory control and also examines prescriptive analytics models for stochastic inventory control. Inventory Analytics is the first book of its kind to adopt a practicable, Python-driven approach to illustrating theories and concepts via computational examples, with each model covered in the book accompanied by its Python code. Originating as a collection of self-contained lectures, Inventory Analytics will be an indispensable resource for practitioners, researchers, teachers, and students alike. |
| data analytics in inventory 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 analytics in inventory management: Data Analytics and Artificial Intelligence for Inventory and Supply Chain Management Dinesh K. Sharma, Madhu Jain, 2022 This book considers new analytics and AI approaches in the areas of inventory control, logistics, and supply chain management. It provides valuable insights for the retailers and managers to improve business operations and make more realistic and better decisions. It also offers a number of smartly designed strategies related to inventory control and supply chain management for the optimal ordering and delivery policies. The book further uses detailed models and AI computing approaches for demand forecasting to planning optimization and digital execution tracking. One of its key features is use of real-life examples, case studies, practical models to ensure adoption of new solutions, data analytics, and AI-lead automation methodologies are included. The book can be utilized by retailers and managers to improve business operations and make more accurate and realistic decisions. The AI-based solution, agnostic assessment, and strategy will support the companies for better alignment and inventory control and capabilities to create a strategic road map for supply chain and logistics. The book is also useful for postgraduate students, researchers, and corporate executives. It addresses novel solutions for inventory to real-world supply chain and logistics that retailers, practitioners, educators, and scholars will find useful. It provides the theoretical and applicable subject matters for the senior undergraduate and graduate students, researchers, practitioners, and professionals in the area of artificial intelligent computing and its applications in inventory and supply chain management, inventory control, and logistics. . |
| data analytics in inventory management: Supply Chain Analytics for Inventory Management Christiane Haubitz, 2021-11-10 This book addresses the application of supply chain analytics to improve inventory management, a cornerstone for successful operations at many companies. Holding inventory reduces stockout cost, facilitates smooth operations, and improves service levels and customer experience; but it also ties up capital and goes along with costs for storage, obsolescence, handling, and other. Due to the complexity of the task, companies apply inventory models, which build on assumptions that seldomly fully hold in practice. As a consequence, the actual performance of the inventory system deviates from the projected performance and the full potential of the models cannot be exploited. This book covers three different problems that companies commonly face when managing their inventories: the introduction of new inventory policies in existing inventory systems, the use of algorithmic advice by human planners, and the accuracy of master data on which inventory models rely. By using mathematical optimization, behavioral experiments, and machine learning, the developed approaches support the successful implementation of state-of-the-art inventory research in practice. |
| data analytics in inventory management: Retail Analytics Anna-Lena Sachs, 2014-12-10 This book addresses the challenging task of demand forecasting and inventory management in retailing. It analyzes how information from point-of-sale scanner systems can be used to improve inventory decisions, and develops a data-driven approach that integrates demand forecasting and inventory management for perishable products, while taking unobservable lost sales and substitution into account in out-of-stock situations. Using linear programming, a new inventory function that reflects the causal relationship between demand and external factors such as price and weather is proposed. The book subsequently demonstrates the benefits of this new approach in numerical studies that utilize real data collected at a large European retail chain. Furthermore, the book derives an optimal inventory policy for a multi-product setting in which the decision-maker faces an aggregated service level target, and analyzes whether the decision-maker is subject to behavioral biases based on real data for bakery products. |
| data analytics in inventory management: Data Analytics and Artificial Intelligence for Inventory and Supply Chain Management Dinesh K. Sharma, Madhu Jain, 2022-11-08 This book considers new analytics and AI approaches in the areas of inventory control, logistics, and supply chain management. It provides valuable insights for the retailers and managers to improve business operations and make more realistic and better decisions. It also offers a number of smartly designed strategies related to inventory control and supply chain management for the optimal ordering and delivery policies. The book further uses detailed models and AI computing approaches for demand forecasting to planning optimization and digital execution tracking. One of its key features is use of real-life examples, case studies, practical models to ensure adoption of new solutions, data analytics, and AI-lead automation methodologies are included.The book can be utilized by retailers and managers to improve business operations and make more accurate and realistic decisions. The AI-based solution, agnostic assessment, and strategy will support the companies for better alignment and inventory control and capabilities to create a strategic road map for supply chain and logistics. The book is also useful for postgraduate students, researchers, and corporate executives. It addresses novel solutions for inventory to real-world supply chain and logistics that retailers, practitioners, educators, and scholars will find useful. It provides the theoretical and applicable subject matters for the senior undergraduate and graduate students, researchers, practitioners, and professionals in the area of artificial intelligent computing and its applications in inventory and supply chain management, inventory control, and logistics. |
| data analytics in inventory 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 analytics in inventory management: Optimization and Inventory Management Nita H. Shah, Mandeep Mittal, 2019-08-31 This book discusses inventory models for determining optimal ordering policies using various optimization techniques, genetic algorithms, and data mining concepts. It also provides sensitivity analyses for the models’ robustness. It presents a collection of mathematical models that deal with real industry scenarios. All mathematical model solutions are provided with the help of various optimization techniques to determine optimal ordering policy. The book offers a range of perspectives on the implementation of optimization techniques, inflation, trade credit financing, fuzzy systems, human error, learning in production, inspection, green supply chains, closed supply chains, reworks, game theory approaches, genetic algorithms, and data mining, as well as research on big data applications for inventory management and control. Starting from deterministic inventory models, the book moves towards advanced inventory models. The content is divided into eight major sections: inventory control and management – inventory models with trade credit financing for imperfect quality items; environmental impact on ordering policies; impact of learning on the supply chain models; EOQ models considering warehousing; optimal ordering policies with data mining and PSO techniques; supply chain models in fuzzy environments; optimal production models for multi-items and multi-retailers; and a marketing model to understand buying behaviour. Given its scope, the book offers a valuable resource for practitioners, instructors, students and researchers alike. It also offers essential insights to help retailers/managers improve business functions and make more accurate and realistic decisions. |
| data analytics in inventory management: Supply Chain Management in the Big Data Era Chan, Hing Kai, Subramanian, Nachiappan, Abdulrahman, Muhammad Dan-Asabe, 2016-11-04 Technological advancements in recent years have led to significant developments within a variety of business applications. In particular, data-driven research provides ample opportunity for enterprise growth, if utilized efficiently. Supply Chain Management in the Big Data Era is an authoritative reference source for the latest scholarly material on the implementation of big data analytics for improved operations and supply chain processes. Highlighting emerging strategies from different industry perspectives, this book is ideally designed for managers, professionals, practitioners, and students interested in the most recent research on supply chain innovations. |
| data analytics in inventory management: Demand Prediction in Retail Maxime C. Cohen, Paul-Emile Gras, Arthur Pentecoste, Renyu Zhang, 2022-01-01 From data collection to evaluation and visualization of prediction results, this book provides a comprehensive overview of the process of predicting demand for retailers. Each step is illustrated with the relevant code and implementation details to demystify how historical data can be leveraged to predict future demand. The tools and methods presented can be applied to most retail settings, both online and brick-and-mortar, such as fashion, electronics, groceries, and furniture. This book is intended to help students in business analytics and data scientists better master how to leverage data for predicting demand in retail applications. It can also be used as a guide for supply chain practitioners who are interested in predicting demand. It enables readers to understand how to leverage data to predict future demand, how to clean and pre-process the data to make it suitable for predictive analytics, what the common caveats are in terms of implementation and how to assess prediction accuracy. |
| data analytics in inventory management: Logistics, Supply Chain and Financial Predictive Analytics Kusum Deep, Madhu Jain, Said Salhi, 2018-08-06 This book addresses a broad range of problems commonly encountered in the fields of financial analysis, logistics and supply chain management, such as the use of big data analytics in the banking sector. Divided into twenty chapters, some of the contemporary topics discussed in the book are co-operative/non-cooperative supply chain models for imperfect quality items with trade-credit financing; a non-dominated sorting water cycle algorithm for the cardinality constrained portfolio problem; and determining initial, basic and feasible solutions for transportation problems by means of the “supply demand reparation method” and “continuous allocation method.” In addition, the book delves into a comparison study on exponential smoothing and the Arima model for fuel prices; optimal policy for Weibull distributed deteriorating items varying with ramp type demand rate and shortages; an inventory model with shortages and deterioration for three different demand rates; outlier labeling methods for medical data; a garbage disposal plant as a validated model of a fault-tolerant system; and the design of a “least cost ration formulation application for cattle”; a preservation technology model for deteriorating items with advertisement dependent demand and trade credit; a time series model for stock price forecasting in India; and asset pricing using capital market curves. The book offers a valuable asset for all researchers and industry practitioners working in these areas, giving them a feel for the latest developments and encouraging them to pursue further research in this direction. |
| data analytics in inventory management: Inventory Management Insights Mansoor Muallim, 101-01-01 Chapter 1: The Foundations of Inventory Management Characters: Jammy (Expert) and Canny (Enthusiast) Jammy: Hey there, Canny! I'm excited to share some valuable insights about inventory management with you today. It's a crucial aspect of any business, and I'm sure you'll find it fascinating. Canny: Hi, Jammy! I'm really eager to learn more. So, what exactly is inventory management? Jammy: Great question, Canny! Inventory management involves efficiently handling a company's stock of goods to ensure smooth operations. It's all about striking the right balance between having enough products to meet customer demand while avoiding overstocking that ties up unnecessary capital. Canny: I see. So, why is it essential for businesses? Jammy: Well, effective inventory management brings several benefits. First and foremost, it helps businesses maintain customer satisfaction. When you have products readily available, you can fulfill orders promptly, leading to happy customers. Moreover, it reduces holding costs, which are the expenses associated with storing excess inventory. Canny: That makes sense. How do companies decide how much inventory to carry? Jammy: Good question! There are various factors that influence this decision. One crucial aspect is demand forecasting. By analyzing historical sales data and market trends, businesses can estimate future demand and plan their inventory accordingly. Canny: Is there a specific method for managing different types of products? Jammy: Absolutely! Not all products are equal. Businesses often categorize their inventory based on demand and value. This categorization helps them apply appropriate management techniques. For instance, high-value items may require closer monitoring and tighter controls. Canny: Interesting! Are there any popular inventory control models? Jammy: Yes, indeed! One of the widely used models is the Economic Order Quantity (EOQ) model. It calculates the optimal order quantity that minimizes total inventory costs, including ordering and holding costs. Canny: Is there any way to handle unpredictable demand? Jammy: Definitely! Safety stock comes into play here. It's the buffer inventory kept to tackle unexpected spikes in demand or delays in supply. Safety stock acts as an insurance against stockouts. Canny: That sounds important. How can technology help with inventory management? Jammy: Technology plays a significant role in modern inventory management. Businesses use specialized software to automate various processes, such as order processing, tracking, and forecasting. This streamlines operations and enhances accuracy. Canny: Thanks for sharing all this valuable information, Jammy. It's been really enlightening. Jammy: You're welcome, Canny! Inventory management is an ever-evolving field, and there's always something new to learn. I'm glad I could help satisfy your thirst for knowledge! Key Takeaways: Inventory management is about efficiently handling a company's stock of goods to meet customer demand while minimizing holding costs. Demand forecasting is crucial for determining the right inventory levels. Categorizing inventory based on demand and value helps tailor management techniques. The Economic Order Quantity (EOQ) model is widely used for inventory control. Safety stock acts as a buffer against unexpected fluctuations in demand or supply. Technology, such as inventory management software, plays a significant role in streamlining processes and improving accuracy. |
| data analytics in inventory management: 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 analytics in inventory management: Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing Amit Kumar Tyagi, Shrikant Tiwari, Gulshan Soni, 2024-10-23 Today, in this smart era, data analytics and artificial intelligence (AI) play an important role in predictive maintenance (PdM) within the manufacturing industry. This innovative approach aims to optimize maintenance strategies by predicting when equipment or machinery is likely to fail so that maintenance can be performed just in time to prevent costly breakdowns. This book contains up-to-date information on predictive maintenance and the latest advancements, trends, and tools required to reduce costs and save time for manufacturers and industries. Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing provides an extensive and in-depth exploration of the intersection of data analytics, artificial intelligence, and predictive maintenance in the manufacturing industry and covers fundamental concepts, advanced techniques, case studies, and practical applications. Using a multidisciplinary approach, this book recognizes that predictive maintenance in manufacturing requires collaboration among engineers, data scientists, and business professionals and includes case studies from various manufacturing sectors showcasing successful applications of predictive maintenance. The real-world examples explain the useful benefits and ROI achieved by organizations. The emphasis is on scalability, making it suitable for both small and large manufacturing operations, and readers will learn how to adapt predictive maintenance strategies to different scales and industries. This book presents resources and references to keep readers updated on the latest advancements, tools, and trends, ensuring continuous learning. Serving as a reference guide, this book focuses on the latest advancements, trends, and tools relevant to predictive maintenance and can also serve as an educational resource for students studying manufacturing, data science, or related fields. |
| data analytics in inventory management: Machine Intelligence and Data Science Applications Amar Ramdane-Cherif, T. P. Singh, Ravi Tomar, Tanupriya Choudhury, Jung-Sup Um, 2023-10-03 This book is a compilation of peer-reviewed papers presented at the International Conference on Machine Intelligence and Data Science Applications (MIDAS 2022), held on October 28 and 29, 2022, at the University of Versailles—Paris-Saclay, France. The book covers applications in various fields like data science, machine intelligence, image processing, natural language processing, computer vision, sentiment analysis, and speech and gesture analysis. It also includes interdisciplinary applications like legal, healthcare, smart society, cyber-physical system, and smart agriculture. The book is a good reference for computer science engineers, lecturers/researchers in the machine intelligence discipline, and engineering graduates. |
| data analytics in inventory management: Sport Business Analytics C. Keith Harrison, Scott Bukstein, 2016-11-18 Developing and implementing a systematic analytics strategy can result in a sustainable competitive advantage within the sport business industry. This timely and relevant book provides practical strategies to collect data and then convert that data into meaningful, value-added information and actionable insights. Its primary objective is to help sport business organizations utilize data-driven decision-making to generate optimal revenue from such areas as ticket sales and corporate partnerships. To that end, the book includes in-depth case studies from such leading sports organizations as the Orlando Magic, Tampa Bay Buccaneers, Duke University, and the Aspire Group. The core purpose of sport business analytics is to convert raw data into information that enables sport business professionals to make strategic business decisions that result in improved company financial performance and a measurable and sustainable competitive advantage. Readers will learn about the role of big data and analytics in: Ticket pricing Season ticket member retention Fan engagement Sponsorship valuation Customer relationship management Digital marketing Market research Data visualization. This book examines changes in the ticketing marketplace and spotlights innovative ticketing strategies used in various sport organizations. It shows how to engage fans with social media and digital analytics, presents techniques to analyze engagement and marketing strategies, and explains how to utilize analytics to leverage fan engagement to enhance revenue for sport organizations. Filled with insightful case studies, this book benefits both sports business professionals and students. The concluding chapter on teaching sport analytics further enhances its value to academics. |
| data analytics in inventory management: Data Science and Business Intelligence for Corporate Decision-Making Dr. P. S. Aithal, 2024-02-09 About the Book: A comprehensive book plan on Data Science and Business Intelligence for Corporate Decision-Making with 15 chapters, each with several sections: Chapter 1: Introduction to Data Science and Business Intelligence Chapter 2: Foundations of Data Science Chapter 3: Business Intelligence Tools and Technologies Chapter 4: Data Visualization for Decision-Making Chapter 5: Machine Learning for Business Intelligence Chapter 6: Big Data Analytics Chapter 7: Data Ethics and Governance Chapter 8: Data-Driven Decision-Making Process Chapter 9: Business Intelligence in Marketing Chapter 10: Financial Analytics and Business Intelligence Chapter 11: Operational Excellence through Data Analytics Chapter 12: Human Resources and People Analytics Chapter 13: Case Studies in Data-Driven Decision-Making Chapter 14: Future Trends in Data Science and Business Intelligence Chapter 15: Implementing Data Science Strategies in Corporations Each chapter dives deep into the concepts, methods, and applications of data science and business intelligence, providing practical insights, real-world examples, and case studies for corporate decision-making processes. |
| data analytics in inventory 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 analytics in inventory management: Inventory Analytics Horst Tempelmeier, 2020-06-02 This textbook provides a practice-oriented introduction into Analytics-based inventory management in complex supply chains. In the context of Business Analytics, we concentrate on Prescriptive Analytics. In addition to standard single-level inventory models also multi-level approaches for the optimal allocation of safety inventory are presented. Moreover, dynamic lot sizing problems under random demand and random yield and their relationship to Material Requirements Planning (MRP) are discussed.The models and algorithms are illustrated with the help of numerous examples. The book has been written for students of Supply Chain Management and Operations Management as well as for practitioners who are confronted with inventory management in their daily work. |
| data analytics in inventory management: Management in the Era of Big Data Joanna Paliszkiewicz, 2020-06-18 This book is a wonderful collection of chapters that posits how managers need to cope in the Big Data era. It highlights many of the emerging developments in technologies, applications, and trends related to management’s needs in this Big Data era. —Dr. Jay Liebowitz, Harrisburg University of Science and Technology This book presents some meaningful work on Big Data analytics and its applications. Each chapter generates helpful guidance to the readers on Big Data analytics and its applications, challenges, and prospects that is necessary for organizational strategic direction. —Dr. Alex Koohang, Middle Georgia State University Big Data is a concept that has caught the attention of practitioners, academicians, and researchers. Big Data offers organizations the possibility of gaining a competitive advantage by managing, collecting, and analyzing massive amounts of data. As the promises and challenges posed by Big Data have increased over the past decade, significant issues have developed regarding how data can be used for improving management. Big Data can be understood as large amounts of data generated by the Internet and a variety of connected smart devices and sensors. This book discusses the main challenges posed by Big Data in a manner relevant to both practitioners and scholars. It examines how companies can leverage Big Data analytics to act and optimize the business. This book brings together the theory and practice of management in the era of Big Data. It offers a look at the current state of Big Data, including a comprehensive overview of both research and practical applications. By bringing together conceptual thinking and empirical research on the nature, meaning, and development of Big Data in management, this book unifies research on Big Data in management to stimulate new directions for academic investigation as well as practice. |
| data analytics in inventory management: Analytics, Operations, and Strategic Decision Making in the Public Sector Evans, Gerald William, Biles, William E., Bae, Ki-Hwan G., 2019-02-15 Analytics for the public sector involves the application of operations research and statistical techniques to solve various problems existing outside of the private sector. The use of analytics for the public sector results in more efficient and effective services for the clients and users of these systems. Analytics, Operations, and Strategic Decision Making in the Public Sector is an essential reference source that discusses analytics applications in various public sector organizations, and addresses the difficulties associated with the design and operation of these systems including multiple conflicting objectives, uncertainties and resulting risk, ill-structured nature, combinatorial design aspects, and scale. Featuring research on topics such as analytical modeling techniques, data mining, and statistical analysis, this book is ideally designed for academicians, educators, researchers, students, and public sector professionals including those in local, state, and federal governments; criminal justice systems; healthcare; energy and natural resources; waste management; emergency response; and the military. |
| data analytics in inventory management: AI and Data Analytics Applications in Organizational Management Merlo, Tereza Raquel, 2024-02-07 Within information sciences and organizational management, a pressing challenge emerges; How can we harness the transformative power of artificial intelligence (AI) and data analytics? As industries grapple with a deluge of data and the imperative to make informed decisions swiftly, the gap between data collection and actionable insights widens. Professionals in various sectors are in a race to unlock AI's full potential to drive operational efficiency, enhance decision-making, and gain a competitive edge. However, navigating this intricate terrain, laden with ethical considerations and interdisciplinary complexity, has proven to be a formidable undertaking. AI and Data Analytics Applications in Organizational Management, combines rigorous scholarship with practicality. It traverses the spectrum from theoretical foundations to real-world applications, making it indispensable for those seeking to implement AI-driven data analytics in their organizations. Moreover, it delves into the ethical and societal dimensions of this revolution, ensuring that the journey toward innovation is paved with responsible considerations. For researchers, scholars, and practitioners yearning to unleash the potential of AI in organizational management, this book is the key to not only understanding the landscape but also charting a course toward transformative change. |
| data analytics in inventory management: Data Analytics Dr. Hariharan R, Dr. Sudha E, Dr Vedapradha R, |
| data analytics in inventory management: Internet of Things and Big Data Analytics-Based Manufacturing Arun Kumar Rana, Sudeshna Chakraborty, Pallavi Goel, Sumit Kumar Rana, Ahmed A. Elngar, 2024-10-17 By enabling the conversion of traditional manufacturing systems into contemporary digitalized ones, Internet of Things (IoT) adoption in manufacturing creates huge economic prospects through reshaping industries. Modern businesses can more readily implement new data-driven strategies and deal with the pressure of international competition thanks to Industrial IoT. But as the use of IoT grows, the amount of created data rises, turning industrial data into Industrial Big Data. Internet of Things and Big Data Analytics-Based Manufacturing shows how Industrial Big Data can be produced as a result of IoT usage in manufacturing, considering sensing systems and mobile devices. Different IoT applications that have been developed are demonstrated and it is shown how genuine industrial data can be produced, leading to Industrial Big Data. This book is organized into four sections discussing IoT and technology, the future of Big Data, algorithms, and case studies demonstrating the use of IoT and Big Data in a variety of industries, including automation, industrial manufacturing, and healthcare. This reference title brings all related technologies into a single source so that researchers, undergraduate and postgraduate students, academicians, and those in the industry can easily understand the topic and further their knowledge. |
| data analytics in inventory management: Inventory and Production Management in Supply Chains Edward A. Silver, David F. Pyke, Douglas J. Thomas, 2016-12-19 Authored by a team of experts, the new edition of this bestseller presents practical techniques for managing inventory and production throughout supply chains. It covers the current context of inventory and production management, replenishment systems for managing individual inventories within a firm, managing inventory in multiple locations and firms, and production management. The book presents sophisticated concepts and solutions with an eye towards today’s economy of global demand, cost-saving, and rapid cycles. It explains how to decrease working capital and how to deal with coordinating chains across boundaries. |
| data analytics in inventory management: Understanding the Predictive Analytics Lifecycle Alberto Cordoba, 2014-07-30 A high-level, informal look at the different stages of the predictive analytics cycle Understanding the Predictive Analytics Lifecycle covers each phase of the development of a predictive analytics initiative. Through the use of illuminating case studies across a range of industries that include banking, megaresorts, mobile operators, healthcare, manufacturing, and retail, the book successfully illustrates each phase of the predictive analytics cycle to create a playbook for future projects. Predictive business analytics involves a wide variety of inputs that include individuals' skills, technologies, tools, and processes. To create a successful analytics program or project to gain forward-looking insight into making business decisions and actions, all of these factors must properly align. The book focuses on developing new insights and understanding business performance based on extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based management as input for human decisions. The book includes: An overview of all relevant phases: design, prepare, explore, model, communicate, and measure Coverage of the stages of the predictive analytics cycle across different industries and countries A chapter dedicated to each of the phases of the development of a predictive initiative A comprehensive overview of the entire analytic process lifecycle If you're an executive looking to understand the predictive analytics lifecycle, this is a must-read resource and reference guide. |
| data analytics in inventory management: 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 analytics in inventory management: AI-Driven Marketing Research and Data Analytics Masengu, Reason, Chiwaridzo, Option Takunda, Dube, Mercy, Ruzive, Benson, 2024-04-22 The surge in technological advancements, coupled with the exponential growth of data, has left marketers grappling with the need for a paradigm shift. The once-established methods of consumer engagement are now overshadowed by the complexities of the digital age, demanding a profound understanding of artificial intelligence (AI) and data analytics. The gap between academic knowledge and practical applications in the field of marketing has widened, leaving industry professionals, educators, and students seeking a comprehensive resource to navigate the intricacies of this transformative era. AI-Driven Marketing Research and Data Analytics is a groundbreaking book that serves as a beacon for marketers, educators, and industry leaders alike. With a keen focus on the symbiotic relationship between AI, data analytics, and marketing research, this book bridges the gap between theory and practice. It not only explores the historical evolution of marketing but also provides an innovative examination of how AI and data analytics are reshaping the landscape. Through real-time case studies, ethical considerations, and in-depth insights, the book offers a holistic solution to the challenges faced by marketing professionals in the digital age. |
| data analytics in inventory management: Operations Management Unleashed: Streamlining Efficiency and Innovation Dr.Garima Mathura, 2023-08-17 Unleash the potential of operations management with strategies to streamline efficiency and foster innovation. This book provides practical guidance for managers aiming to optimize processes and drive operational excellence. |
| data analytics in inventory management: Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science Sheng-Lung Peng, Noor Zaman Jhanjhi, Souvik Pal, Fathi Amsaad, 2023-08-28 The volume is a collection of high-quality, peer-reviewed research papers presented at the Third International Conference on Mathematical Modeling and Computational Science (ICMMCS 2023), held during 24 – 25 February 2023 in hybrid mode. The topics covered in the book are mathematical logic and foundations, numerical analysis, neural networks, fuzzy set theory, coding theory, higher algebra, number theory, graph theory and combinatory, computation in complex networks, calculus, differential educations and integration, application of soft computing, knowledge engineering, machine learning, artificial intelligence, big data and data analytics, high performance computing, network and device security, Internet of Things (IoT). |
| data analytics in inventory management: Data Analytics, Computational Statistics, and Operations Research for Engineers Debabrata Samanta, SK Hafizul Islam, Naveen Chilamkurti, Mohammad Hammoudeh, 2022-03-24 With the rapidly advancing fields of Data Analytics and Computational Statistics, it’s important to keep up with current trends, methodologies, and applications. This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements. Data Analytics, Computational Statistics, and Operations Research for Engineers: Methodologies and Applications presents applications of computationally intensive methods, inference techniques, and survival analysis models. It discusses how data mining extracts information and how machine learning improves the computational model based on the new information. Those interested in this reference work will include students, professionals, and researchers working in the areas of data mining, computational statistics, operations research, and machine learning. |
| data analytics in inventory management: Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry Kingshuk Srivastava, Thipendra P Singh, Manas Ranjan Pradhan, Vinit Kumar Gunjan, 2023-11-20 This book covers aspects of data science and predictive analytics used in the oil and gas industry by looking into the challenges of data processing and data modelling unique to this industry. It includes upstream management, intelligent/digital wells, value chain integration, crude basket forecasting, and so forth. It further discusses theoretical, methodological, well-established, and validated empirical work dealing with various related topics. Special focus has been given to experimental topics with various case studies. Features: Provides an understanding of the basics of IT technologies applied in the oil and gas sector Includes deep comparison between different artificial intelligence techniques Analyzes different simulators in the oil and gas sector as well as discussion of AI applications Focuses on in-depth experimental and applied topics Details different case studies for upstream and downstream This book is aimed at professionals and graduate students in petroleum engineering, upstream industry, data analytics, and digital transformation process in oil and gas. |
| data analytics in inventory management: Microsoft Certified: Dynamics 365 Supply Chain Management Functional Consultant Expert (MB-330) Cybellium, Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| data analytics in inventory management: Advanced Data Analytics with AWS Joseph Conley , 2024-04-17 Master the Fundamentals of Data Analytics at Scale KEY FEATURES ● Comprehensive guide to constructing data engineering workflows spanning diverse data sources ● Expert techniques for transforming and visualizing data to extract actionable insights ● Advanced methodologies for analyzing data and employing machine learning to uncover intricate patterns DESCRIPTION Embark on a transformative journey into the realm of data analytics with AWS with this practical and incisive handbook. Begin your exploration with an insightful introduction to the fundamentals of data analytics, setting the stage for your AWS adventure. The book then covers collecting data efficiently and effectively on AWS, laying the groundwork for insightful analysis. It will dive deep into processing data, uncovering invaluable techniques to harness the full potential of your datasets. The book will equip you with advanced data analysis skills, unlocking the ability to discern complex patterns and insights. It covers additional use cases for data analysis on AWS, from predictive modeling to sentiment analysis, expanding your analytical horizons. The final section of the book will utilize the power of data virtualization and interaction, revolutionizing the way you engage with and derive value from your data. Gain valuable insights into emerging trends and technologies shaping the future of data analytics, and conclude your journey with actionable next steps, empowering you to continue your data analytics odyssey with confidence. WHAT WILL YOU LEARN ● Construct streamlined data engineering workflows capable of ingesting data from diverse sources and formats. ● Employ data transformation tools to efficiently cleanse and reshape data, priming it for analysis. ● Perform ad-hoc queries for preliminary data exploration, uncovering initial insights. ● Utilize prepared datasets to craft compelling, interactive data visualizations that communicate actionable insights. ● Develop advanced machine learning and Generative AI workflows to delve into intricate aspects of complex datasets, uncovering deeper insights. WHO IS THIS BOOK FOR? This book is ideal for aspiring data engineers, analysts, and data scientists seeking to deepen their understanding and practical skills in data engineering, data transformation, visualization, and advanced analytics. It is also beneficial for professionals and students looking to leverage AWS services for their data-related tasks. TABLE OF CONTENTS 1. Introduction to Data Analytics and AWS 2. Getting Started with AWS 3. Collecting Data with AWS 4. Processing Data on AWS 5. Descriptive Analytics on AWS 6. Advanced Data Analysis on AWS 7. Additional Use Cases for Data Analysis 8. Data Visualization and Interaction on AWS 9. The Future of Data Analytics 10. Conclusion and Next Steps Index |
| data analytics in inventory management: Industrial Ecology Adeel Shah, |
| data analytics in inventory management: Database Management using AI: A Comprehensive Guide A Purushotham Reddy, 2024-10-20 Database Management Using AI: A Comprehensive Guide is a professional yet accessible exploration of how artificial intelligence (AI) is reshaping the world of database management. Designed for database administrators, data scientists, and tech enthusiasts, this book walks readers through the transformative impact of AI on modern data systems. The guide begins with the fundamentals of database management, covering key concepts such as data models, SQL, and the principles of database design. From there, it delves into the powerful role AI plays in optimizing database performance, enhancing security, and automating complex tasks like data retrieval, query optimization, and schema design. The book doesn't stop at theory. It brings AI to life with practical case studies showing how AI-driven database systems are being used in industries such as e-commerce, healthcare, finance, and logistics. These real-world examples demonstrate AI's role in improving efficiency, reducing errors, and driving intelligent decision-making. Key topics covered include: Introduction to Database Systems: Fundamentals of database management, from relational databases to modern NoSQL systems. AI Integration: How AI enhances database performance, automates routine tasks, and strengthens security. Real-World Applications: Case studies from diverse sectors like healthcare, finance, and retail, showcasing the practical impact of AI in database management. Predictive Analytics and Data Mining: How AI tools leverage data to make accurate predictions and uncover trends. Future Trends: Explore cutting-edge innovations like autonomous databases and cloud-based AI solutions that are shaping the future of data management. With its clear explanations and actionable insights, Database Management Using AI equips readers with the knowledge to navigate the fast-evolving landscape of AI-powered databases, making it a must-have resource for those looking to stay ahead in the digital age. |
| data analytics in inventory management: Cybersecurity Measures for Logistics Industry Framework Jhanjhi, Noor Zaman, Shah, Imdad Ali, 2024-02-14 Global supply chains are becoming more customer-centric and sustainable thanks to next-generation logistics management technologies. Automating logistics procedures greatly increases the productivity and efficiency of the workflow. There is a need, however, to create flexible and dynamic relationships among numerous stakeholders and the transparency and traceability of the supply chain. The digitalization of the supply chain process has improved these relationships and transparency; however, it has also created opportunities for cybercriminals to attack the logistics industry. Cybersecurity Measures for Logistics Industry Framework discusses the environment of the logistics industry in the context of new technologies and cybersecurity measures. Covering topics such as AI applications, inventory management, and sustainable computing, this premier reference source is an excellent resource for business leaders, IT managers, security experts, students and educators of higher education, librarians, researchers, and academicians. |
| data analytics in inventory management: Cognitive Science, Computational Intelligence, and Data Analytics Vikas Khare, Sanjeet Kumar Dwivedi, Monica Bhatia, 2024-06-06 Cognitive Science, Computational Intelligence, and Data Analytics: Methods and Applications with Python introduces readers to the foundational concepts of data analysis, cognitive science, and computational intelligence, including AI and Machine Learning. The book's focus is on fundamental ideas, procedures, and computational intelligence tools that can be applied to a wide range of data analysis approaches, with applications that include mathematical programming, evolutionary simulation, machine learning, and logic-based models. It offers readers the fundamental and practical aspects of cognitive science and data analysis, exploring data analytics in terms of description, evolution, and applicability in real-life problems.The authors cover the history and evolution of cognitive analytics, methodological concerns in philosophy, syntax and semantics, understanding of generative linguistics, theory of memory and processing theory, structured and unstructured data, qualitative and quantitative data, measurement of variables, nominal, ordinals, intervals, and ratio scale data. The content in this book is tailored to the reader's needs in terms of both type and fundamentals, including coverage of multivariate analysis, CRISP methodology and SEMMA methodology. Each chapter provides practical, hands-on learning with real-world applications, including case studies and Python programs related to the key concepts being presented. - Demystifies the theory of data analytics using a step-by-step approach - Covers the intersection of cognitive science, computational intelligence, and data analytics by providing examples and case studies with applied algorithms, mathematics, and Python programming code - Introduces foundational data analytics techniques such as CRISP-DM, SEMMA, and Object Detection Models in the context of computational intelligence methods and tools - Covers key concepts of multivariate and cognitive data analytics such as factor analytics, principal component analytics, linear regression analysis, logistic regression analysis, and value chain applications |
| data analytics in inventory management: AI Impacts in Digital Consumer Behavior Musiolik, Thomas Heinrich, Rodriguez, Raul Villamarin, Kannan, Hemachandran, 2024-03-04 In the ever-evolving landscape of digital innovation, businesses grapple with the challenge of deciphering dynamic consumer behavior. AI Impacts in Digital Consumer Behavior is a pioneering exploration tailored for academic scholars seeking insights into the profound influence of artificial intelligence on consumer dynamics. As businesses strive to harness the potential of data, this book serves as a beacon, offering a comprehensive understanding of the intricacies involved in tracking, analyzing, and predicting shifts in consumer preferences. This groundbreaking work not only identifies the complexities posed by the rapidly changing digital landscape but also presents a solution-oriented approach. It unveils a theoretical framework and the latest empirical research, providing scholars with a toolkit of concepts, theories, and analytical techniques. With a multidisciplinary focus on behavioral analysis, the book equips academic minds with the knowledge to navigate the challenges of the digital age. Furthermore, it addresses the ethical dimensions and ethic considerations associated with the accelerating pace of consumer behavior analysis, shedding light on the responsible use of AI technologies. |
| data analytics in inventory 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 and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a Transnational ...
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 enable a broader scientific community to benefit from the identified …
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 minimum time delay; Understandable in a way that allows …
Belmont Forum Adopts Open Data Principles for Environmental Change Research
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, released in June, 2015. “A Place to Stand” is the …
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, prevents fraud and thereby builds trust in …
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 …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues …
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 …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their …
