Desheng Dash Wu – författare

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.

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.

In this edited volume, we present the state-of-the-art views of the perspective of enterprise risk management, to include frameworks and controls in the ERM process with respect to supply chains, constructions, and project, energy, environmental and sustainable development risk management. The bulk of this volume is devoted to presenting a number of modeling approaches that have been (or could be) applied to enterprise risk management in construction.

In this edited volume, we present the state-of-the-art views of the perspective of enterprise risk management, to include frameworks and controls in the ERM process with respect to supply chains, constructions, and project, energy, environmental and sustainable development risk management. The bulk of this volume is devoted to presenting a number of modeling approaches that have been (or could be) applied to enterprise risk management in construction.

This edited volume expands the scope of risk management beyond finance to include resources and environment issues in China. It presents the state-of-the-art approaches of using risk management to effectively manage resources and environment. Both case studies and theoretical methodologies are discussed.

This edited volume expands the scope of risk management beyond finance to include resources and environment issues in China. It presents the state-of-the-art approaches of using risk management to effectively manage resources and environment. Both case studies and theoretical methodologies are discussed.

The bulk of this volume deals with the four main aspects of risk management: market risk, credit risk, risk management - in macro-economy as well as within companies. It presents a number of approaches and case studies directed at applying risk management to diverse business environments. Included are traditional market and credit risk management models such as the Black-Scholes Option Pricing Model, the Vasicek Model, Factor models, CAPM models, GARCH models, KMV models and credit scoring models.

The bulk of this volume deals with the four main aspects of risk management: market risk, credit risk, risk management - in macro-economy as well as within companies. It presents a number of approaches and case studies directed at applying risk management to diverse business environments. Included are traditional market and credit risk management models such as the Black-Scholes Option Pricing Model, the Vasicek Model, Factor models, CAPM models, GARCH models, KMV models and credit scoring models.

In this edited volume, we present the state-of-the-art views of the perspective of enterprise risk management, to include frameworks and controls in the ERM process with respect to supply chains, constructions, and project, energy, environmental and sustainable development risk management. The bulk of this volume is devoted to presenting a number of modeling approaches that have been (or could be) applied to enterprise risk management in construction.

The bulk of this volume deals with the four main aspects of risk management: market risk, credit risk, risk management - in macro-economy as well as within companies. It presents a number of approaches and case studies directed at applying risk management to diverse business environments. Included are traditional market and credit risk management models such as the Black-Scholes Option Pricing Model, the Vasicek Model, Factor models, CAPM models, GARCH models, KMV models and credit scoring models.

This edited volume expands the scope of risk management beyond finance to include resources and environment issues in China. It presents the state-of-the-art approaches of using risk management to effectively manage resources and environment. Both case studies and theoretical methodologies are discussed.

This book is a comprehensive guide to several aspects of risk, including information systems, disaster management, supply chain and disaster management perspectives. A major portion of this book is devoted to presenting a number of operations research models that have been (or could be) applied to enterprise supply risk management, especially from the supply chain perspective. Each chapter of this book can be used as a unique module on a different topics with dedicated examples, definitions and discussion notes.This book comes at a time when the world is increasingly challenged by different forms of risk and how to manage them. Events of the 21st Century have made enterprise risk management even more critical. Risks such as suspicions surrounding top-management structures, financial and technology bubbles (especially since 2008), as well as the demonstrated risk from terrorism, such as the 9/11 attack in the U.S. as well as more recent events in France, Belgium, and other locations in Europe, have a tremendous impact on many facets of business. Businesses, in fact, exist to cope with risk in their area of specialization.

Risk is inherent in business. Without risk, there would be no motivation to conduct business. But a key principle is that organizations should accept risks that they are competent enough to deal with, and “outsource” other risks to those who are more competent to deal with them (such as insurance companies). Enterprise Risk Management (2nd Edition) approaches enterprise risk management from the perspectives of accounting, supply chains, and disaster management, in addition to the core perspective of finance. While the first edition included the perspective of information systems, the second edition views this as part of supply chain management or else focused on technological specifics. It discusses analytical tools available to assess risk, such as balanced scorecards, risk matrices, multiple criteria analysis, simulation, data envelopment analysis, and financial risk measures.

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 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.