Search results for "Operations Management and Data Analytics Modelling"
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R 2,136.00
Big Data Analytics Using Multiple Criteria Decision-Making Models
Multiple Criteria Decision Making (MCDM) is a subfield of Operations Research, dealing with decision making problems. A decision-making problem is characterized by the need to choose one or a few among a number of alternatives. The field of MCDM assumes special importance in this era of Big Data and Business Analytics. In this volume, the focus will be on modelling-based tools for Business Analytics (BA), with exclusive focus on the sub-field of MCDM within the domain of operations research. The book will include an Introduction to Big Data and Business Analytics, and challenges and opportunities for developing MCDM models in the era of Big Data.
R 7,595.28
Convergence of Technology and Operations Management in Modern Businesses
In the modern business landscape, the intersection of technology and operations management is driving efficiency and innovation. As organizations continue to rely on advanced technologies, such as artificial intelligence, data analytics, and automation, they are transforming their operational strategies to enhance productivity, streamline processes, and deliver valuable products. Aligning technological advancements with operational goals allows companies to achieve a competitive edge, improve customer satisfaction, and unlock new growth opportunities. Businesses must continue to explore this convergence to adapt their operations successfully and invest in necessary skills to connect technology with business processes. Convergence of Technology and Operations Management in Modern Businesses explores the intersection of technology and operations management in the modern business environment. It covers technological advancements for revolutionized operations and supply chain management for increased efficiency and competitiveness. This book covers topics such as smart banking, blockchain, and human capital, and is a useful resource for financial professionals, bankers, business owners, data scientists, computer engineers, academicians, scientists, and researchers.
R 8,174.24
Risk, Reliability and Resilience in Operations Management
Risk, Reliability and Resilience in Operations Management examines measurement tools and techniques and their real-world application. The book provides a resource that is needed to help solve complex business operations and global supply chains and their important requirements for the accurate measurement of risk, reliability, and resilience to inform decisions and reduce risk. In addition, the book discusses advancements in technology and data analytics, with final sections covering the COVID-19 pandemic and how it has put greater emphasis on the importance of risk, reliability, and resilience in business operations.This book provides a timely overview of measurement techniques and their application in operations management, offering insights into future directions in this field.
R 6,179.00
Data Analytics with Hadoop
Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you'll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce. Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hive, and HBase. You'll also learn about the analytical processes and data systems available to build and empower data products that can handle-and actually require-huge amounts of data.Understand core concepts behind Hadoop and cluster computing Use design patterns and parallel analytical algorithms to create distributed data analysis jobs Learn about data management, mining, and warehousing in a distributed context using Apache Hive and HBase Use Sqoop and Apache Flume to ingest data from relational databases Program complex Hadoop and Spark applications with Apache Pig and Spark DataFrames Perform machine learning techniques such as classification, clustering, and collaborative filtering with Spark's MLlib
R 798.00
Snowflake Data Warehouse Engineering
Design, build, and operate a production-grade analytics platform on Snowflake. This practical guide shows how Snowflake architecture shapes modeling, ingestion, and transformation choices; how to engineer ELT pipelines for structured and semi-structured data; and how to make performance, workload, security, and cost decisions that stand up in real projects. The approach is engineering-first and scenario-driven, turning concepts into repeatable, auditable solutions teams can use day to day. Beyond feature coverage, the emphasis is operations: CI/CD for SQL and Snowpark code, monitoring and observability, least-privilege governance with roles and policies, cost guardrails, secure sharing and collaboration, and business continuity with Time Travel, cloning, and replication. You will learn Snowflake-specific techniques for pruning, selective clustering, streaming and CDC, and dynamic refresh. What makes this book especially useful is its end-to-end operating playbook: opinionated patterns, checklists, and guardrails that connect architecture, modeling, ingestion and ELT, governance and security, performance and cost, and the everyday practices of releasing and recovering safely. It focuses on concrete decisions and the trade-offs behind them, helping teams avoid legacy anti-patterns while building a reliable, auditable platform that is ready to evolve. What You Will Learn Design Snowflake architectures that align storage, compute, security, and governance into a coherent, scalable platform.Model, load, and transform structured and semi-structured data using streams, tasks, MERGE, and SCD2 patterns.Tune performance and control cost with micro-partition pruning, selective clustering, warehouse sizing, and workload isolation.Implement least-privilege RBAC, masking and row access policies, auditing, and tag-driven governance.Build reliable ELT pipelines and release safely with CI/CD, testing, cloning, and SWAP-based promotion.Operate with observability and SRE practices using Snowflake usage views and SLOs.Share and collaborate securely with Secure Data Sharing and Marketplace, and plan replication and DR for continuity. Who This Book Is For Data engineers; data warehouse and solution architects; analytics engineers; BI developers; advanced data analysts; DBAs moving from on-prem to cloud (intermediate level with SQL and warehousing basics).
R 1,208.00
Decision Analytics
Mathematical models and algorithms for sequential decisions under uncertainty Sequential decisions under uncertainty arise in many fields including energy, healthcare, finance, transportation, and logistics, yet accessible treatments linking foundational theory to computational practice remain scarce. Decision Analytics: Mathematical Models and Algorithms for Sequential Decision-Making, written by Brian T. Denton, a past President of INFORMS, presents a structured progression from core concepts through advanced methods, pairing rigorous mathematics with implementable Python code. Across ten chapters, Decision Analytics covers decision trees, Monte Carlo simulation, Markov chains, Markov decision processes, partially observable Markov decision processes, and constrained optimization models, including stochastic programs. Dedicated chapters on reinforcement learning and multi-agent learning introduce model-free approaches for finding optimal or near-optimal solutions. The final chapter covers approximate dynamic programming for decision-making at scale. Real-world examples, exercises, and an instructor's solution manual support classroom adoption. Readers will also find: Coverage of artificial intelligence techniques applied to sequential decision-making problemsMonte Carlo simulation methods used to analyse decision trees, Markov decision processes, and stochastic programming formulationsPython code examples throughout the text enabling direct implementation and experimentation with each model and algorithm presentedPractice exercises with solutions and an instructor's manual designed to support both self-study and classroom-based teachingA concept-first pedagogical approach that explains foundational principles before demonstrating how they solve applied problems Designed for undergraduate and graduate students in industrial engineering, operations research, and related STEM disciplines with introductory knowledge of mathematics, probability, and statistics, this book also serves researchers and professionals who require unified treatment of sequential decision-making methods.
R 4,559.00
Logistics Operations and Management
This book provides a comprehensive overview of how to strategically manage the movement and storage of products or materials from any point in the manufacturing process to customer fulfillment. Topics covered include important tools for strategic decision making, transport, packaging, warehousing, retailing, customer services and future trends.
R 4,031.00
Advances in Applied Operations Research and Analytics for Business Intelligence
This book includes selected papers from the International Conference on Emerging Trends in Business Analytics and Management Sciences, 57th Annual Convention of ORSI (BAMS 2024), held during December 12-14, 2024, at Victor Menezes Convention Center (VMCC), IIT Bombay, Mumbai, India. The book includes research works from engineers, researchers, and practitioners in the field of operations research and applications, machine learning, artificial intelligence and data science, big data, IoT, blockchain, cloud computing, inventory management and supply chain analytics, retail analytics, information systems and analytics, financial data analytics, marketing analytics, healthcare analytics, operations and project management, applied mathematics and simulation in business, analytics in strategy and competitiveness, analytics for environmental economics, circular economy and sustainability through business analytics, applied statistics in management, fuzzy logic and intelligent systems, and environment, social, and governance analytics.
R 12,811.17
Data and Analytics Playbook
The Data and Analytics Playbook: Proven Methods for Governed Data and Analytic Quality explores the way in which data continues to dominate budgets, along with the varying efforts made across a variety of business enablement projects, including applications, web and mobile computing, big data analytics, and traditional data integration. The book teaches readers how to use proven methods and accelerators to break through data obstacles to provide faster, higher quality delivery of mission critical programs. Drawing upon years of practical experience, and using numerous examples and an easy to understand playbook, Lowell Fryman, Gregory Lampshire, and Dan Meers discuss a simple, proven approach to the execution of multiple data oriented activities. In addition, they present a clear set of methods to provide reliable governance, controls, risk, and exposure management for enterprise data and the programs that rely upon it. In addition, they discuss a cost-effective approach to providing sustainable governance and quality outcomes that enhance project delivery, while also ensuring ongoing controls. Example activities, templates, outputs, resources, and roles are explored, along with different organizational models in common use today and the ways they can be mapped to leverage playbook data governance throughout the organization.
R 1,764.00
Total Quality Management and Operational Excellence
The notion of 'Quality' in business performance has exploded since the publication of the first edition of this classic text in 1989. Today there is a plethora of performance improvement frameworks including lean–Six Sigma and the latest version of ISO 9001, offering an often confusing variety of ways to achieve business excellence.Quality guru John Oakland’s famous TQM model, in many ways a precursor to these frameworks, has evolved to become the ultimate holistic overview of performance improvement strategy. Incorporating the frameworks that succeeded it, the revised model redefines Quality by:Accelerating changeReducing costProtecting reputationThe popular, practical, jargon-free writing style, along with ten supporting case studies, effortlessly ties the model to its real-life applications, making it easy to understand how to apply what you’ve learned to your practices and achieve sustainable competitive advantage.Guiding readers through the language of TQM and OpEx and all their recent developments, including data analytics, this book sets out a clear way to manage change. This exciting update of a classic is all the busy student or professional will need to begin understanding how to manage Quality and achieve Operational Excellence.
R 8,603.75
Advances in Production Management Systems. Towards Smart and Digital Manufacturing
The two-volume set IFIP AICT 591 and 592 constitutes the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2020, held in Novi Sad, Serbia, in August/September 2020.The 164 papers presented were carefully reviewed and selected from 199 submissions. They discuss globally pressing issues in smart manufacturing, operations management, supply chain management, and Industry 4.0. The papers are organized in the following topical sections:Part I: advanced modelling, simulation and data analytics in production and supply networks; advanced, digital and smart manufacturing; digital and virtual quality management systems; cloud-manufacturing; cyber-physical production systems and digital twins; IIOT interoperability; supply chain planning and optimization; digital and smart supply chain management; intelligent logistics networks management; artificial intelligence and blockchain technologies in logistics and DSN; novel production planning and control approaches; machine learning and artificial intelligence; connected, smart factories of the future; manufacturing systems engineering: agile, flexible, reconfigurable; digital assistance systems: augmented reality and virtual reality; circular products design and engineering; circular, green, sustainable manufacturing; environmental and social lifecycle assessments; socio-cultural aspects in production systems; data-driven manufacturing and services operations management; product-service systems in DSN; and collaborative design and engineeringPart II: the Operator 4.0: new physical and cognitive evolutionary paths; digital transformation approaches in production management; digital transformation for more sustainable supply chains; data-driven applications in smart manufacturing and logistics systems; data-driven services: characteristics, trends and applications; the future of lean thinking and practice; digital lean manufacturing and its emerging practices; new reconfigurable, flexible or agile production systems in the era of industry 4.0; operations management in engineer-to-order manufacturing; production management in food supply chains; gastronomic service system design; product and asset life cycle management in the circular economy; and production ramp-up strategies for product
R 3,118.71
Operations Management
Discover how operations shape every industry —from AI-driven innovation to the human decisions at its core. Operations Management, by Nigel Slack, Alistair Brandon-Jones and Nicola Burgess, is a comprehensive and essential guide which gives you the tools to design, deliver, and improve processes so you can boost performance in any organization. Packed with fresh case studies and examples, this edition brings theory to life and helps students connect with the real world of business. Focused on practical decision-making, this bestselling textbook takes you through the ‘4 Ds’ of Operations management: direction, design, delivery, and development. Real-world ‘Operations in Practice’ examples, short cases, and end-of-chapter case studies show how concepts work whilst worked examples blend quantitative and qualitative analysis. Whether you are studying for a business degree or building professional skills, you will finish with a confident, real-world toolkit for analysing operations, improving processes, and delivering results. From services to manufacturing, explore how people drive operations—and how operations drive success. Pair this text with MyLab Management MyLab is the teaching and learning platform available to support this title. By combining trusted author content with digital tools and a flexible learning experience, MyLab Management personalises learning and assessment to help improve results. MyLab Management is not included with this title. If you would like to purchase both the physical textbook and MyLab Management (which also comes with the eTextbook), search for: 9781292759456 Slack Operations Management 11th edition ‘MyLab Package’ which consists of: Print textbookeTextbookMyLab Management
R 3,197.00
Data Analytics, Computational Statistics, and Operations Research for Engineers
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.
R 9,315.40
R 10,257.00
Management Approach for Resource-Productive Operations
Markus Hammer investigates a time-based and analytics-supported operations management approach. He explores five perspectives: 1) the needs of industry, in particular manufacturing in process industries, 2) the impact of digitization, with focus on Big Data and analytics, 3) the management of operations through time-based performance metrics, 4) how operations improvement methods and advanced process control help achieve resource-productive operations and 5) learning from practice based on two empirical case studies. The author conceives, explains, and tests an implementation methodology. The final case study proves that the developed implementation methodology works in practice.
R 3,888.65
Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry
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 sectorIncludes deep comparison between different artificial intelligence techniquesAnalyzes different simulators in the oil and gas sector as well as discussion of AI applicationsFocuses on in-depth experimental and applied topicsDetails different case studies for upstream and downstreamThis book is aimed at professionals and graduate students in petroleum engineering, upstream industry, data analytics, and digital transformation process in oil and gas.
R 2,980.48
Analytics in Finance and Risk Management
This book presents contemporary issues and challenges in finance and risk management in a time of rapid transformation due to technological advancements. It includes research articles based on financial and economic data and intends to cover the emerging role of analytics in financial management, asset management, and risk management.Analytics in Finance and Risk Management covers statistical techniques for data analysis in finance. It explores applications in finance and risk management, covering empirical properties of financial systems. It addresses data science involving the study of statistical and computational models and includes basic and advanced concepts. The chapters incorporate the latest methodologies and challenges facing financial and risk management and illustrate related issues and their implications in the real world. The primary users of this book will include researchers, academicians, postgraduate students, professionals in engineering and business analytics, managers, consultants, and advisors in IT firms, financial markets, and services domains.
R 3,014.41
Data Mining and Analytics in Healthcare Management
This book presents data mining methods in the field of healthcare management in a practical way. Healthcare quality and disease prevention are essential in today’s world. Healthcare management faces a number of challenges, e.g. reducing patient growth through disease prevention, stopping or slowing disease progression, and reducing healthcare costs while improving quality of care. The book provides an overview of current healthcare management problems and highlights how analytics and knowledge management have been used to better cope with them. It then demonstrates how to use descriptive and predictive analytics tools to help address these challenges. In closing, it presents applications of software solutions in the context of healthcare management. Given its scope, the book will appeal to a broad readership, from researchers and students in the operations research and management field to practitioners such as data analysts and decision-makers who work in the healthcare sector.
R 3,184.44
Big Data and Analytics
This book presents and discusses the main strategic and organizational challenges posed by Big Data and analytics in a manner relevant to both practitioners and scholars. The first part of the book analyzes strategic issues relating to the growing relevance of Big Data and analytics for competitive advantage, which is also attributable to empowerment of activities such as consumer profiling, market segmentation, and development of new products or services. Detailed consideration is also given to the strategic impact of Big Data and analytics on innovation in domains such as government and education and to Big Data-driven business models. The second part of the book addresses the impact of Big Data and analytics on management and organizations, focusing on challenges for governance, evaluation, and change management, while the concluding part reviews real examples of Big Data and analytics innovation at the global level. The text is supported by informative illustrations and case studies, so that practitioners can use the book as a toolbox to improve understanding and exploit business opportunities related to Big Data and analytics.
R 1,541.80
Advances in Applied Operations Research and Analytics for Business Intelligence
This book includes selected papers from the International Conference on Emerging Trends in Business Analytics and Management Sciences, 57th Annual Convention of ORSI (BAMS 2024), held during December 12-14, 2024, at Victor Menezes Convention Center (VMCC), IIT Bombay, Mumbai, India. The book includes research works from engineers, researchers, and practitioners in the field of operations research and applications, machine learning, artificial intelligence and data science, big data, IoT, blockchain, and cloud computing, inventory management and supply chain analytics, retail analytics, information systems and analytics, financial data analytics, marketing analytics, healthcare analytics, operations and project management, applied mathematics and simulation in business, analytics in strategy and competitiveness, analytics for environmental economics, circular economy and sustainability through business analytics, applied statistics in management, fuzzy logic and intelligent systems, and environment, social, and governance analytics.
R 12,929.79
Supply Chain Analytics
Supply Chain Analytics, second edition, introduces the reader to data analytics and demonstrates the value of its effective use in the improvement of supply chain (SC) process performance. By describing four key SC processes and illustrating – through worked examples – how the descriptive, predictive, and prescriptive analytic methods can be applied to enhance those processes, this book presents a more comprehensive learning experience for the reader than has been offered previously.Key topics and issues are addressed, including the capriciousness of modern SC operating environments; the imperative of SC sustainability; the need for heightened SC risk management; the building of SC resilience; the pursuit of SC optimisation; and the use of big data, data mining, cloud computing, machine learning, artificial intelligence (AI), and importantly the social issues confronting SC analysts in carrying out their work.The author identifies four core SC processes – strategy, design, execution, and people – to which the analytic techniques explained can be applied to ensure continuous performance improvement and the growth of competitive advantage.Pedagogy to aid learning is incorporated throughout, including an opening section for each chapter explaining the intended learning outcomes; 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; end-of-chapter case studies; review questions; and assignment tasks.Providing both management expertise and technical skills, which are essential to decision-makers in the SC, this textbook is an essential reading for advanced undergraduate and postgraduate students of SC analytics, SC leaders, and SC operations management professionals. Its practice-based and applied approach also makes it valuable for teaching academics, organisational trainers and coaches, operating SC practitioners, and those pursuing professional qualifications.Online resources include chapter-by-chapter PowerPoint slides, tutorial exercises, written assignments, worked examples using Excel, and a test bank of exam questions.
R 2,140.96
Hands-on Data Analytics for Business
The knowledge and skills of data analytics enable business professionals to make evidence-based decisions rather than just by intuition. To appreciate the knowledge and skills, one needs to understand the theory, hands-on statistical operations using computer software, business application case studies and the analytics project maturity roadmap.Hands-on Data Analytics for Business is a concise all-in-one analytics textbook for business professionals and university students who want to:Understand the essential theories and concepts of data analyticsGrasp hands-on practical statistical and analytics skills step-by-step using SPSS and ExcelAppreciate real-world well-known case studies in human resources, marketing and supply chain on how data analytics is applied in business.This book is for business professionals or university students seeking to master data analytics knowledge and technical skills to a level allowing them to practice data analytics hands-on and manage data analytics projects at scale. For business professionals, the book is written in a way to help learn statistical skills in a concise way with practical real-world examples to inspire business applications. For students, this book is organized in a sequence readily deliverable for a university course, with classes combining theory or concepts, hands-on practice and case studies. For all readers, the book is an essential guide on using data analytics to tackle modern complex activities for companies to gain a competitive edge.
R 2,674.00
Innovative Technology at the Interface of Finance and Operations
This book examines the challenges and opportunities arising from an assortment of technologies as they relate to Operations Management and Finance. The book contains primers on operations, finance, and their interface. After that, each section contains chapters in the categories of theory, applications, case studies, and teaching resources. These technologies and business models include Big Data and Analytics, Artificial Intelligence, Machine Learning, Blockchain, IoT, 3D printing, sharing platforms, crowdfunding, and crowdsourcing.The balance between theory, applications, and teaching materials make this book an interesting read for academics and practitioners in operations and finance who are curious about the role of new technologies. The book is an attractive choice for PhD-level courses and for self-study.
R 7,220.24
Hands-on Data Analytics for Business
The knowledge and skills of data analytics enable business professionals to make evidence-based decisions rather than just by intuition. To appreciate the knowledge and skills, one needs to understand the theory, hands-on statistical operations using computer software, business application case studies and the analytics project maturity roadmap.Hands-on Data Analytics for Business is a concise all-in-one analytics textbook for business professionals and university students who want to:Understand the essential theories and concepts of data analyticsGrasp hands-on practical statistical and analytics skills step-by-step using SPSS and ExcelAppreciate real-world well-known case studies in human resources, marketing and supply chain on how data analytics is applied in business.This book is for business professionals or university students seeking to master data analytics knowledge and technical skills to a level allowing them to practice data analytics hands-on and manage data analytics projects at scale. For business professionals, the book is written in a way to help learn statistical skills in a concise way with practical real-world examples to inspire business applications. For students, this book is organized in a sequence readily deliverable for a university course, with classes combining theory or concepts, hands-on practice and case studies. For all readers, the book is an essential guide on using data analytics to tackle modern complex activities for companies to gain a competitive edge.
R 7,942.93
Data Analytics
Data analytics is creeping into the lexicon of our daily language. This book gives the reader a perspective as to the overall data analytics skill set, startingwith a primer on statistics, and works toward the application of those methods.There are a variety of formulas and algorithms used in the data analyticsprocess. These formulas can be plugged into whatever software application thereader uses to obtain the answer they need. There are several demonstrations ofthis process to provide straightforward instruction as to how to bring data analytics skills to your critical thinking. This bookpresents a variety of methods and techniques, as well as case studies, toenrich the knowledge of data analytics for project managers, systems engineers,and cybersecurity professionals. It separates the case studies so that eachprofession can practice some straightforward data analytics specific to their fields. The main purpose of this text is to refresh the statistical knowledgenecessary to build models for data analytics. Along with that, this bookencompasses the analytics thinking that is essential to all three professions.FEATURES:Provides straightforward instruction on data analytics methodsIncludes methods, techniques, and case studies for project managers, systems engineers, and cybersecurity professionalsRefreshes the statistical knowledgeneeded to bring data analytics into your skillset and decision-makingFocuses on getting readers up to speed quickly and efficiently to be able to see the impact of data analytics and analytical thinking
R 1,185.00
Data Analytics for Management, Banking and Finance
This book is a practical guide on the use of various data analytics and visualization techniques and tools in the banking and financial sectors. It focuses on how combining expertise from interdisciplinary areas, such as machine learning and business analytics, can bring forward a shared vision on the benefits of data science from the research point of view to the evaluation of policies. It highlights how data science is reshaping the business sector. It includes examples of novel big data sources and some successful applications on the use of advanced machine learning, natural language processing, networks analysis, and time series analysis and forecasting, among others, in the banking and finance.It includes several case studies where innovative data science models is used to analyse, test or model some crucial phenomena in banking and finance. At the same time, the book is making an appeal for a further adoption of these novel applications in the field of economics and finance so that they can reach their full potential and support policy-makers and the related stakeholders in the transformational recovery of our societies.The book is for stakeholders involved in research and innovation in the banking and financial sectors, but also those in the fields of computing, IT and managerial information systems, helping through this new theory to better specify the new opportunities and challenges. The many real cases addressed in this book also provide a detailed guide allowing the reader to realize the latest methodological discoveries and the use of the different Machine Learning approaches (supervised, unsupervised, reinforcement, deep, etc.) and to learn how to use and evaluate performance of new data science tools and frameworks
R 6,211.43
Operations Management for Healthcare
This fully updated edition of the bestselling textbook on Health Service Operations Management provides an invaluable reference for students and researchers in the fields of healthcare management, operations management and patient flow logistics. Featuring theoretical frameworks and a comprehensive set of practical case studies, this book also covers subjects such as hospital planning and supply chain management in healthcare, quality assurance and performance management. Healthcare managers work together with healthcare professionals in a multitude of challenging scenarios. Trade-offs have to be made between waiting times for customers and efficient use of scarce resources, between quality of care and quality of services, between the perspective of a single pathway and the total system, and between the perspective of a single provider and that of a network of providers working together in the chain of primary care, hospitals, nursing homes and home care. This book guides healthcare students and professionals through a set of practical tools and resources, ranging from simple queueing models to more complicated analytical models, to help address these issues. The book can be used at an undergraduate level by introducing concepts, definitions and approaches, and at a postgraduate level through the application of approaches to operations management problems in healthcare practice. It will serve as a primary textbook for a health service operations management course module in a Master's program on healthcare management.
R 5,850.44
Data Mining and Business Analytics with R
Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: A thorough discussion and extensive demonstration of the theory behind the most useful data mining toolsIllustrations of how to use the outlined concepts in real-world situationsReadily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materialsNumerous exercises to help readers with computing skills and deepen their understanding of the material Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.
R 4,148.00
Digital Transformation for the Process Industries
Imagine if your process manufacturing plants were running so well that your production, safety, environmental, and profitability targets were being met so that your subject matter experts could focus on data-driven business improvements. Through proper use and analysis of your existing operations data, your company can become an industry leader and reward your stakeholders.Written in an engaging and easily understandable manner, this book demonstrates a step-by-step process of how an organization can effectively utilize technology and make the necessary culture changes to achieve operational excellence. You will see how several industry-leading companies have used an effective real-time data infrastructure for mission-critical business use cases. The book also addresses challenges involved, such as effectively integrating operational (OT) data with business (IT) systems to enable a more proactive, predictive management model for a fleet of process plants.Some of the things you will take away: Learn how a real-time data infrastructure enables transformation of raw sensor data into contextualized information for operational insights and business process improvement. Understand how reusing the same operational data for multiple use cases significantly impacts fleet management, profitability, and asset stewardship. See how a simple digital unit template representing production flows can be repeatedly used to identify critical inefficiencies in plant operations. Discover best practices of deploying real-time situational awareness alerts and predictive analytics. Realize how to transform your organization into a data-driven culture for continuous sustainable improvement. Find out how leading companies integrate operations data with business intelligence and predictive analytics tools in a corporate on-premises or cloud-enabled environment. Learn how industry-leading companies have imaginatively used a real-time data infrastructure to improve yields, reduce cycle times, and slash operating costs.This book is targeted for process industries production and operations leadership, senior engineers, IT management, CIOs, and service providers to those industries. Academics will benefit from latest data analysis strategies. This book guides readers to use the best, results-proven approaches to ensure operational excellence.
R 3,001.79
Learning Tableau 2025
Explore the full potential of Tableau and learn how to draw valuable insights from data directly from a Tableau Visionary and Zen MasterKey FeaturesLearn Tableau's foundational principles while exploring new AI features like Tableau Pulse and AgentTransform complex datasets into interactive insights with Tableau PrepGain deeper insights using geospatial analysis and advanced methodsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionTableau 2025 marks a new era in data visualization and analysis, bringing together advanced AI integrations and dynamic user experiences. This sixth edition, written by Tableau Visionary and Zen Master Joshua Miligan, is an end-to-end guide to mastering the latest innovations in Tableau that transform raw data into actionable insights.This edition introduces groundbreaking features like Tableau AI (including Tableau Pulse and Tableau Agent), enhancing your analytical capabilities with AI-driven data exploration and automated insights. With detailed walkthroughs, you’ll learn to build dynamic dashboards that respond to your data in real time and work with sophisticated AI functionalities that predict trends and model scenarios.Whether you're a seasoned data professional or new to Tableau, this book provides the tools you need to leverage Tableau’s full potential. From integrating diverse data sources using the enhanced data model to employing advanced geospatial functions for detailed mapping, every chapter is packed with expert knowledge and practical applications designed to put powerful analytics at your fingertips.What you will learnImplement advanced AI features to streamline data analysisBuild and customize dynamic dashboards for interactive data storytellingLeverage new geospatial functions for comprehensive mappingEnhance data prep with Tableau Prep's new featuresIntegrate and analyze data from multiple sources effectivelyShare your data stories to build a culture of trust and actionWho this book is forThis Tableau book is for aspiring BI developers and data analysts, data scientists, researchers, and anyone else who wants to gain a deeper understanding of data using Tableau. This book takes you from the ground up, so you won't need any prior experience with Tableau before you dive in, but a full Tableau license (or 14-day demo license) is essential to be able to make use of all the exercises.
R 1,954.00
Innovative Technology at the Interface of Finance and Operations
This book examines the challenges and opportunities arising from an assortment of technologies as they relate to Operations Management and Finance. The book contains primers on operations, finance, and their interface. After that, each section contains chapters in the categories of theory, applications, case studies, and teaching resources. These technologies and business models include Big Data and Analytics, Artificial Intelligence, Machine Learning, Blockchain, IoT, 3D printing, sharing platforms, crowdfunding, and crowdsourcing.The balance between theory, applications, and teaching materials make this book an interesting read for academics and practitioners in operations and finance who are curious about the role of new technologies. The book is an attractive choice for PhD-level courses and for self-study.
R 6,228.00
Analytics in Finance and Risk Management
This book presents contemporary issues and challenges in finance and risk management in a time of rapid transformation due to technological advancements. It includes research articles based on financial and economic data and intends to cover the emerging role of analytics in financial management, asset management, and risk management.Analytics in Finance and Risk Management covers statistical techniques for data analysis in finance. It explores applications in finance and risk management, covering empirical properties of financial systems. It addresses data science involving the study of statistical and computational models and includes basic and advanced concepts. The chapters incorporate the latest methodologies and challenges facing financial and risk management and illustrate related issues and their implications in the real world. The primary users of this book will include researchers, academicians, postgraduate students, professionals in engineering and business analytics, managers, consultants, and advisors in IT firms, financial markets, and services domains.
R 9,967.58
Handbook of Disruptive Technologies
This handbook addresses how smart operations, management, and healthcare can be used to detect and analyze supply chain problems, business problems, and diseases. It also discusses the underlying methodologies and related security concerns.Handbook of Disruptive Technologies: Operations, Business, Management, and Healthcare provides updated and timely insights, unique approaches or frameworks, practical applications, and case studies. It provides in-depth knowledge of disruptive technologies and applies data analytics to different areas to solve complex situations. The handbook uses data analytics in decision-making and policy framing and identifies practices and applications of disruptive technologies that can assist organizations in gaining a competitive advantage. Valuable insights, innovative approaches, practical applications, and case studies can be found in this handbook as it effectively consolidates and synthesizes information to provide a comprehensive understanding of the subject matter.This book is tailored for researchers, academics, and professionals seeking to deepen their knowledge in the field.
R 6,659.30
Elements of Joint Learning and Optimization in Operations Management
This book examines recent developments in Operations Management, and focuses on four major application areas: dynamic pricing, assortment optimization, supply chain and inventory management, and healthcare operations. Data-driven optimization in which real-time input of data is being used to simultaneously learn the (true) underlying model of a system and optimize its performance, is becoming increasingly important in the last few years, especially with the rise of Big Data.
R 4,762.00
Management Decision-Making, Big Data and Analytics
Accessible and concise, this exciting new textbook examines data analytics from a managerial and organizational perspective and looks at how they can help managers become more effective decision-makers. The book successfully combines theory with practical application, featuring case studies, examples and a ‘critical incidents’ feature that make these topics engaging and relevant for students of business and management. The book features chapters on cutting-edge topics, including: • Big data • Analytics • Managing emerging technologies and decision-making • Managing the ethics, security, privacy and legal aspects of data-driven decision-making The book is accompanied by an Instructor’s Manual, PowerPoint slides and access to journal articles. Suitable for management students studying business analytics and decision-making at undergraduate, postgraduate and MBA levels.
R 5,974.37
Business Analytics for Supply Chains and Operations
All businesses face continuous pressure to improve efficiency, reduce costs, and enhance profitability. By adopting an analytical approach, managers and business owners can identify opportunities and solutions that may not be immediately apparent. This book demonstrates how such an approach can be applied systematically to address complex operational challenges.Business Analytics for Supply Chains and Operations introduces a range of key topics that are grouped into three areas: modelling, optimisation, and probability. The optimisation component includes methods such as linear and integer programming and network flows, which are used to address logistics problems (e.g. transportation) in supply chains and production strategies in operations. The probabilistic component covers queueing systems and inventory models, providing tools to analyse uncertainty in operational settings.The book aims to enable readers to increase the efficiency and productivity of business operations. This requires analysts to observe and define challenges concisely, precisely, and logically. Familiarity with both classical and state-of-the-art operational research techniques and tools is essential for approaching such challenges effectively. These tools allow analysts to devise solutions, algorithms, and methods that provide a competitive advantage. Finally, results must be communicated clearly to support management decision-making. This book guides the reader through this process using numerous examples and well-worked solutions.The book is designed for postgraduate and advanced undergraduate students on business degrees, as well as for practising managers seeking analytical tools for decision-making. It emphasises hands-on learning with three main computational tools: Excel (the primary tool, introduced in depth), the MathProg modelling language for optimisation, and SimPy (with Python and a brief introduction to Simio) for simulation experiments. Online support materials include PowerPoint slides, a test bank, computer lab sheets with solutions, and links to video tutorials.
R 2,711.00
Management Decision-Making, Big Data and Analytics
Accessible and concise, this exciting new textbook examines data analytics from a managerial and organizational perspective and looks at how they can help managers become more effective decision-makers. The book successfully combines theory with practical application, featuring case studies, examples and a ‘critical incidents’ feature that make these topics engaging and relevant for students of business and management. The book features chapters on cutting-edge topics, including: • Big data • Analytics • Managing emerging technologies and decision-making • Managing the ethics, security, privacy and legal aspects of data-driven decision-making The book is accompanied by an Instructor’s Manual, PowerPoint slides and access to journal articles. Suitable for management students studying business analytics and decision-making at undergraduate, postgraduate and MBA levels.
R 3,175.56
Heterogeneous Data Management, Polystores, and Analytics for Healthcare
This book constitutes revised selected papers from two VLDB workshops: The International Workshop on Polystore Systems for Heterogeneous Data in Multiple Databases with Privacy and Security Assurances, Poly 2021, and the 7th International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2021, which were held virtually on August 2021. For Poly 2021, 7 full and 2 short papers were accepted from 10 submissions; and for DMAH 2021, 4 full papers together with 2 invited papers were accepted from a total of 7 submissions. The papers were organized in topical sections as follows: distributed information systems in enterprises, enterprise access to data constructed from a variety of programming models, data management, data integration, data curation, privacy, and security innovative data management and analytics technologies highlighting end-to-end applications, systems, and methods to address problems in healthcare.
R 2,347.54
Operations Management for Business Excellence
In a context of heightened global disruption, accelerating digital transformation, and increasing sustainability imperatives, the fifth edition of Operations Management for Business Excellence, provides a contemporary and analytically grounded framework for understanding how operations and supply chain management contribute to long-term organisational performance.The text integrates foundational principles of operations and supply chain management with recent developments in automation, artificial intelligence, robotics, connected systems, and real-time analytics. It examines how these technologies are reshaping operational decision-making, blurring traditional boundaries between planning and execution, and raising new questions concerning governance, ethics, and the role of human judgement in technology-enabled systems. Each chapter combines conceptual clarity with applied examples, linking operations strategy to practical challenges such as warehouse design in the era of Industry 5.0, omnichannel logistics, returns management, and supply network resilience. A consistent five-pillar strategic framework – adaptability, customer-driven innovation, continuous competitive recalibration, proactive risk management, and workforce empowerment – provides a structured lens through which advanced supply chain concepts are analysed.Written for undergraduate and postgraduate students, executives, and practitioners, the book balances analytical rigour with accessibility. It encourages critical evaluation of how organisations can design effective, resilient, and responsible operations, positioning operations management as a discipline that integrates people, technology, and sustainability to create enduring value.
R 3,114.00
Handbook of Big Data Analytics
Big Data analytics is the complex process of examining big data to uncover information such as correlations, hidden patterns, trends and user and customer preferences, to allow organizations and businesses to make more informed decisions. These methods and technologies have become ubiquitous in all fields of science, engineering, business and management due to the rise of data-driven models as well as data engineering developments using parallel and distributed computational analytics frameworks, data and algorithm parallelization, and GPGPU programming. However, there remain potential issues that need to be addressed to enable big data processing and analytics in real time. In the first volume of this comprehensive two-volume handbook, the authors present several methodologies to support Big Data analytics including database management, processing frameworks and architectures, data lakes, query optimization strategies, towards real-time data processing, data stream analytics, Fog and Edge computing, and Artificial Intelligence and Big Data. The second volume is dedicated to a wide range of applications in secure data storage, privacy-preserving, Software Defined Networks (SDN), Internet of Things (IoTs), behaviour analytics, traffic predictions, gender based classification on e-commerce data, recommender systems, Big Data regression with Apache Spark, visual sentiment analysis, wavelet Neural Network via GPU, stock market movement predictions, and financial reporting. The two-volume work is aimed at providing a unique platform for researchers, engineers, developers, educators and advanced students in the field of Big Data analytics.
R 4,702.00
Big Data Management and Analytics
As more companies go digital and conduct their business online, this book provides practical examples of how they can better manage their data and use it to generate maximum value. It offers an integrated approach by treating data as an asset and discusses how to preserve and protect it just like any other corporate asset.Big Data Management and Analytics: Concepts, Tools, and Applications illustrates effective strategies for managing, governing, and analyzing big data to gain a competitive edge for companies utilizing big data and analytics. It offers a comprehensive guide on methods, tools, and concepts to efficiently manage and analyze big data in order to make informed decisions. Additionally, this book explores the significance of artificial intelligence and machine learning in leveraging big data and how they can be optimized in a well-structured environment. This book also emphasizes treating big data as a valuable asset and outlines strategies for preserving and safeguarding it like any other corporate asset. The inclusion of case studies ensures that the methodologies and concepts presented can be easily implemented in day-to-day operations.Given the current significance of big data in the business world, this book equips readers with the necessary skills to effectively manage this valuable asset. It is tailored for practitioners, students, and professionals working in data mining, big data, and machine learning across various industries, including manufacturing.
R 4,652.00
Intelligent Techniques for Predictive Data Analytics
Comprehensive resource covering tools and techniques used for predictive analytics with practical applications across various industries Intelligent Techniques for Predictive Data Analytics provides an in-depth introduction of the tools and techniques used for predictive analytics, covering applications in cyber security, network security, data mining, and machine learning across various industries. Each chapter offers a brief introduction on the subject to make the text accessible regardless of background knowledge. Readers will gain a clear understanding of how to use data processing, classification, and analysis to support strategic decisions, such as optimizing marketing strategies and customer relationship management and recommendation systems, improving general business operations, and predicting occurrence of chronic diseases for better patient management. Traditional data analytics uses dashboards to illustrate trends and outliers, but with large data sets, this process is labor-intensive and time-consuming. This book provides everything readers need to save time by performing deep, efficient analysis without human bias and time constraints. A section on current challenges in the field is also included. Intelligent Techniques for Predictive Data Analytics covers sample topics such as: Models to choose from in predictive modeling, including classification, clustering, forecast, outlier, and time series models Price forecasting, quality optimization, and insect and disease plant and monitoring in agriculture Fraud detection and prevention, credit scoring, financial planning, and customer analytics Big data in smart grids, smart grid analytics, and predictive smart grid quality monitoring, maintenance, and load forecasting Management of uncertainty in predictive data analytics and probable future developments in the field Intelligent Techniques for Predictive Data Analytics is an essential resource on the subject for professionals and researchers working in data science or data management seeking to understand the different models of predictive analytics, along with graduate students studying data science courses and professionals and academics new to the field.
R 4,722.00
Recent Trends and Future Direction for Data Analytics
In an increasingly data-centric world, scholars and practitioners grapple with the complexities of harnessing data analytics effectively across various industries. The challenge lies in navigating the rapid evolution of methodologies, identifying emerging trends, and understanding the nuanced applications of data analytics in real-world scenarios. This gap between theory and practice inhibits academic progress. It hampers industry innovation, leaving stakeholders needing help to leverage data to its full potential. Recent Trends and Future Direction for Data Analytics presents a compelling solution. By delving into real-world case studies spanning supply chain management, marketing, healthcare, and finance, this book bridges the gap between theory and practice, offering invaluable insights into the practical applications of data analytics. A systematic exploration of fundamental concepts, advanced techniques, and specialized topics equips scholars, researchers, and industry professionals with the knowledge and tools needed to navigate the complexities of data analytics with confidence. This book is designed as a comprehensive resource for academia and industry alike. It serves as a roadmap for understanding the current state of data analytics and anticipating its future trajectories. By offering a blend of theoretical foundations and practical applications, it empowers readers to unlock the full potential of data analytics, driving innovation, informed decision-making, and transformative change across diverse sectors.
R 14,400.39
Operations Management
This fascinating new core textbook, authored by a highly respected academic with over a decade of industry experience, takes a global and strategic approach to the important topic of operations management (OM). Integrating contemporary and traditional theories the text covers everything a student needs to understand the reality of operations in the modern world and combines the latest cutting-edge thinking with innovative learning features. Written in a concise and engaging style and based on up-to-date research in the field, the book provides a range of international case studies and examples that help students to apply theoretical knowledge to real-world practice.This is a must-have textbook for students studying operations management modules on undergraduate, postgraduate and MBA programmes. In addition, this is an ideal textbook to accompany modules on operations strategy, production management and services management.Accompanying online resources for this title can be found at bloomsburyonlineresources.com/operations-management. These resources are designed to support teaching and learning when using this textbook and are available at no extra cost.
R 1,210.00
Spatio-Temporal Data Analytics for Wind Energy Integration
This SpringerBrief presents spatio-temporal data analytics for wind energy integration using stochastic modeling and optimization methods. It explores techniques for efficiently integrating renewable energy generation into bulk power grids. The operational challenges of wind, and its variability are carefully examined. A spatio-temporal analysis approach enables the authors to develop Markov-chain-based short-term forecasts of wind farm power generation. To deal with the wind ramp dynamics, a support vector machine enhanced Markov model is introduced. The stochastic optimization of economic dispatch (ED) and interruptible load management are investigated as well. Spatio-Temporal Data Analytics for Wind Energy Integration is valuable for researchers and professionals working towards renewable energy integration. Advanced-level students studying electrical, computer and energy engineering should also find the content useful.
R 1,511.95
