David L. Olson

Supply chain operations face many risks, including political, environmental, and economic. The past five years have seen major challenges, from pandemic, impacts of global warming, wars, and tariff impositions. In this rapidly changing world, risks appear in every aspect of operations. This book presents data mining and analytics tools with R programming as well as a brief presentation of Monte Carlo simulation that can be used to anticipate and manage these risks. RStudio software and R programming language are widely used in data mining. For Monte Carlo simulation applications we cover Crystal Ball software, one of a number of commercially available Monte Carlo simulation tools.Chapter 1 of this book deals with classification of risks. It includes a typical supply chain example published in academic literature. Chapter 2 gives a brief introduction to R programming. It is not intended to be comprehensive, but sufficient for a user to get started using this free open source and highly popular analytics tool. Chapter 3 discusses risks commonly found in finance, to include basic data mining tools applied to analysis of credit card fraud data. Like the other datasets used in the book, this data comes from the Kaggle.com site, a free site loaded with realistic datasets. The remainder of the book covers risk analytics tools. Chapter 4 presents R association rule modeling using a supply chain related dataset. Chapter 5 presents Monte Carlo simulation of some supply chain risk situations. Chapter 6 gives both time series and multiple regression prediction models as well as autoregressive integrated moving average (ARIMA; Box-Jenkins) models in SAS and R. Chapter 7 covers classification models demonstrated with credit risk data. Chapter 8 deals with fraud detection and the common problem of modeling imbalanced datasets. Chapter 9 introduces Naïve Bayes modeling with categorical data using an employee attrition dataset.Features:Overview of predictive analytics presented in an understandable mannerPresentation of useful business applications of predictive data miningCoverage of risk management in finance, insurance, and supply chain contextsPresentation of predictive modelsDemonstration of using these predictive models in RScreenshots enabling readers to develop their own modelsThe purpose of the book is to present tools useful to analyze risks, especially those faced in supply chain management and finance.

This book is devoted to presenting models that have been applied in project management. There are a variety of project domains. We discuss engineering/construction projects, software development projects, massive projects to include governmental undertakings, and pharmaceutical product development. Chapter 1 gives an overview of projects, and discusses the difficulties in completing projects on time, within budget, and at designed functionality. While the successful completion of a project is a challenge, there are some things that can be done to improve the probability of a project’s success. The book reviews a number of project management concepts. These include developing organizational ability to work on projects, as discussed in Chapters 2 and 3. Sponsor expectations can be based on better information if a good job of project development, estimation and selection is conducted, as discussed in Chapters 4, 5 and 6. Project planning tools involving the critical path method are covered in Chapter 7. Tools to evaluate project risk are covered in Chapter 8. The critical chain method is covered in Chapter 9. Chapter 10 discusses means to rush projects when circumstances demand, to include project crashing as well as Agile and SCRUM approaches used in software engineering projects. Chapter 11 covers project implementation and control, including assessment of delay responsibility.

This book provides an overview of data mining methods in the field of business. Business management faces challenges in serving customers in better ways, in identifying risks, and analyzing the impact of decisions. Of the three types of analytic tools, descriptive analytics focuses on what has happened and predictive analytics extends statistical and/or artificial intelligence to provide forecasting capability. Chapter 1 provides an overview of business management problems. Chapter 2 describes how analytics and knowledge management have been used to better cope with these problems. Chapter 3 describes initial data visualization tools. Chapter 4 describes association rules and software support. Chapter 5 describes cluster analysis with software demonstration. Chapter 6 discusses time series analysis with software demonstration. Chapter 7 describes predictive classification data mining tools. Applications of the context of management are presented in Chapter 8. Chapter 9 covers prescriptive modeling in business and applications of artificial intelligence.

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.

This book is devoted to presenting models that have been applied in project management. There are a variety of project domains. We discuss engineering/construction projects, software development projects, massive projects to include governmental undertakings, and pharmaceutical product development. Chapter 1 gives an overview of projects, and discusses the difficulties in completing projects on time, within budget, and at designed functionality. While the successful completion of a project is a challenge, there are some things that can be done to improve the probability of a project’s success. The book reviews a number of project management concepts. These include developing organizational ability to work on projects, as discussed in Chapters 2 and 3. Sponsor expectations can be based on better information if a good job of project development, estimation and selection is conducted, as discussed in Chapters 4, 5 and 6. Project planning tools involving the critical path method are covered in Chapter 7. Tools to evaluate project risk are covered in Chapter 8. The critical chain method is covered in Chapter 9. Chapter 10 discusses means to rush projects when circumstances demand, to include project crashing as well as Agile and SCRUM approaches used in software engineering projects. Chapter 11 covers project implementation and control, including assessment of delay responsibility.

This textbook, now in its fourth edition, serves as a comprehensive guide to learning various aspects of risk, encompassing supply chain management, artificial intelligence, and sustainability. It demonstrates a wide range of operations research models that have been successfully applied to enterprise supply chain risk management. Each chapter of the book can function as a standalone module focusing on a specific topic, offering dedicated examples, definitions, and discussion notes.The publication of this book comes at a crucial time when the world is facing increasing challenges from various forms of risk. Events such as Covid-19, the energy crisis, wars, and terrorism in the 21st century have all disrupted supply chains, thus highlighting the critical importance of enterprise risk management. Additional risks, such as financial and technological bubbles, along with concerns surrounding rampant artificial intelligence, contribute to a climate that demands enhanced risk management within organizations.

The objective of the book is to provide materials to demonstrate the development of TOPSIS and to serve as a handbook. It contains the basic process of TOPSIS, numerous variant processes, property explanations, theoretical developments, and illustrative examples with real-world cases. Possible readers would be graduate students, researchers, analysts, and professionals who are interested in TOPSIS, a distance-based algorithm, and who would like to compare TOPSIS with other MCDM methods. The book serves as a research reference as well as a self-learning book with step-by-step illustrations for the MCDM community.

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.

Dieses Buch bietet einen Überblick über Data-Mining-Methoden, die durch Software veranschaulicht werden. Beim Wissensmanagement geht es um die Anwendung von menschlichem Wissen (Erkenntnistheorie) mit den technologischen Fortschritten unserer heutigen Gesellschaft (Computersysteme) und Big Data, sowohl bei der Datenerfassung als auch bei der Datenanalyse. Es gibt drei Arten von Analyseinstrumenten. Die deskriptive Analyse konzentriert sich auf Berichte über das, was passiert ist. Bei der prädiktiven Analyse werden statistische und/oder künstliche Intelligenz eingesetzt, um Vorhersagen treffen zu können. Dazu gehört auch die Modellierung von Klassifizierungen. Die diagnostische Analytik kann die Analyse von Sensoreingaben anwenden, um Kontrollsysteme automatisch zu steuern. Die präskriptive Analytik wendet quantitative Modelle an, um Systeme zu optimieren oder zumindest verbesserte Systeme zu identifizieren. Data Mining umfasst deskriptive und prädiktive Modellierung. Operations Research umfasst alle drei Bereiche. Dieses Buch konzentriert sich auf die deskriptive Analytik.Das Buch versucht, einfache Erklärungen und Demonstrationen einiger deskriptiver Werkzeuge zu liefern. Es bietet Beispiele für die Auswirkungen von Big Data und erweitert die Abdeckung von Assoziationsregeln und Clusteranalysen. Kapitel 1 gibt einen Überblick im Kontext des Wissensmanagements. Kapitel 2 erörtert einige grundlegende Softwareunterstützung für die Datenvisualisierung. Kapitel 3 befasst sich mit den Grundlagen der Warenkorbanalyse, und Kapitel 4 demonstriert die RFM-Modellierung, ein grundlegendes Marketing-Data-Mining-Tool. Kapitel 5 demonstriert das Assoziationsregel-Mining. Kapitel 6 befasst sich eingehender mit der Clusteranalyse. Kapitel 7 befasst sich mit der Link-Analyse. Die Modelle werden anhand geschäftsbezogener Daten demonstriert. Der Stil des Buches ist beschreibend und versucht zu erklären, wie die Methoden funktionieren, mit einigen Zitaten, aber ohne tiefgehende wissenschaftliche Referenzen. Die Datensätze und die Software wurden so ausgewählt, dass sie für jeden Leser, der über einen Computeranschluss verfügt, weithin verfügbar und zugänglich sind.

The objective of the book is to provide materials to demonstrate the development of TOPSIS and to serve as a handbook. It contains the basic process of TOPSIS, numerous variant processes, property explanations, theoretical developments, and illustrative examples with real-world cases. Possible readers would be graduate students, researchers, analysts, and professionals who are interested in TOPSIS, a distance-based algorithm, and who would like to compare TOPSIS with other MCDM methods. The book serves as a research reference as well as a self-learning book with step-by-step illustrations for the MCDM community.

Digitising Enterprise in an Information Age is an e?ort that focuses on a very vast cluster of Enterprises and their digitising technology involvement and take us through the road map of the implementation process in them, some of them being ICT, Banking, Stock Markets, Textile Industry & ICT, Social Media, Software Quality Assurance, Information Systems Security and Risk Management, Employee Resource Planning etc. It delves on increased instances of cyber spamming and the threat that poses to e-Commerce and Banking and tools that help and Enterprise toward of such threats. To quote Confucius, “As the water shapes itself to the vessel that contains it, so does a wise man adapts himself to circumstances.” And the journey of evolution and progression will continue and institutions and enterprises will continue to become smarter and more and more technology savvy. Enterprises and businesses across all genre and spectrum are trying their level best to adopt to change and move on with the changing requirements of technology and as enterprises and companies upgrade and speed up their digital transformations and move their outdate heirloom systems to the cloud, archaic partners that don't keep up will be left behind.Note: T&F does not sell or distribute the Hardback in India, Pakistan, Nepal, Bhutan, Bangladesh and Sri Lanka.

This book presents key concepts related to quantitative analysis in business. It is targeted at business students (both undergraduate and graduate) taking an introductory core course. Business analytics has grown to be a key topic in business curricula, and there is a need for stronger quantitative skills and understanding of fundamental concepts. This second edition adds material on Tableau, a very useful software for business analytics. This supplements the tools from Excel covered in the first edition, to include Data Analysis Toolpak and SOLVER.

This book presents key concepts related to quantitative analysis in business.It is targeted at business students (both undergraduate and graduate) taking an introductory core course. Business analytics has grown to be a key topic in business curricula, and there is a need for stronger quantitative skills and understanding of fundamental concepts.This second edition adds material on Tableau, a very useful software for business analytics. This supplements the tools from Excel covered in the first edition, to include Data Analysis Toolpak and SOLVER.

This book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R’) and WEKA. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on prescriptive analytics.The book seeks to provide simple explanations and demonstration of some descriptive tools. This second editionprovides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic data types. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Chapter 5 demonstrates regression tree modeling. Chapter 6 presents autoregressive/integrated/moving average models, as well as GARCH models. Chapter 7 covers the set of data mining tools used in classification, to include special variants support vector machines, random forests, and boosting. Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links.

COVID-19 has spread around the world, causing tremendous structural change, and severely affecting global supply chains and financial operations. As such there is a need for analytic tools help deal with the impact of the pandemic on the world’s economies; these tools are not panaceas and certainly won’t cure the problems faced, but they offer a means to aid governments, firms, and individuals in coping with specific problems. This book provides an overview of the COVID-19 pandemic and evaluates its effect on financial and supply chain operations. It then discusses epidemic modeling, presenting sources of quantitative and text data, and describing how models are used to illustrate the pandemic impact on supply chains, macroeconomic performance on financial operations. It highlights the specific experiences of the banking system, which offers predictions of the impact on the Swedish banking sector. Further, it examines models related to pandemic planning, such as evaluation of financial contagion, debt risk analysis, and health system efficiency performance, and addresses specific models of pandemic parameters. The book demonstrates various tools using available data on the ongoing COVID-19 pandemic. While it includes some citations, it focuses on describing the methods and explaining how they work, rather than on theory. The data sets and software presented were all selected on the basis of their widespread availability to any reader with computer links.

This book addresses the use of quantitative tools to support general project management.Part I of the book deals with critical path modeling. Part II discusses risk modeling tools to include Program Evaluation and Review Technique (PERT), critical chain modeling, and agile/scrum approaches. Project control through earned value analysis is also covered. Part III is a Microsoft Project orientation. A feature of the book is an effort to tie content to that of the Project Management Body of Knowledge (PMBOK).Each chapter includes reference to how each chapter relates to the PMBOK structure and its relationship to the 2020 Project Management Professional (PMP) Exam Outline.

This book addresses the use of quantitative tools to support general project management. Part I of the book deals with critical path modeling. Part II discusses risk modeling tools to include Program Evaluation and Review Technique (PERT), critical chain modeling, and agile/scrum approaches. Project control through earned value analysis is also covered. Part III is a Microsoft Project orientation. A feature of the book is an effort to tie content to that of the Project Management Body of Knowledge (PMBOK).Each chapter includes reference to how each chapter relates to the PMBOK structure and its relationship to the 2020 Project Management Professional (PMP) Exam Outline.

This book addresses project management in the context of general project management.An introductory chapter discusses project features in general. Part I of the book focuses attention on the important human element in project management. Part II discusses two processes involved in the initial project definition stage, as well as covering estimation. Part III involves planning and project risk and implementation.A feature of the book is an effort to tie content to that of the Project Management Body of Knowledge (PMBOK). Each chapter includes reference to how each chapter relates to the PMBOK structure, and relationship to the 2020 PMP Exam Outline.

This book addresses project management in the context of general project management.An introductory chapter discusses project features in general. Part I of the book focuses attention on the important human element in project management. Part II discusses two processes involved in the initial project definition stage, as well as covering estimation. Part III involves planning. Part III deals with project risk and implementation.A feature of the book is an effort to tie content to that of the Project Management Body of Knowledge (PMBOK). Each chapter includes reference to how each chapter relates to the PMBOK structure, and relationship to the 2020 PMP Exam Outline.