Risk management is essential in modern business. This book compiles insights on enterprise risk management and its influence on decision-making. The first part introduces the concept of enterprise risk management. The second part examines it through various lenses, including finance, accounting, insurance, supply chain operations, and project management. Part three focuses on technological tools, covering financial risk models and accounting aspects, utilizing data envelopment analysis, neural networks for credit risk assessment, and real option analysis for IT outsourcing. The final section presents case studies of enterprise risk management practices in China, highlighting sectors such as banking, chemical plant operations, and information technology. The book serves as a comprehensive resource for understanding the multifaceted nature of risk management in today's business environment, illustrating both theoretical frameworks and practical applications across different industries and regions.
Edited as a Festschrift in honor of Prof Milan Zeleny, this volume reflects and emulates his unmistakable legacy: the essential multidimensionality of human and social affairs. It contains papers dealing with: Multiple Criteria Decision Making; Social and Human System Management; and Information, Knowledge and Wisdom Management.
Enterprise risk management has always been important. However, the events of the 21st Century have made it even more critical. The top level of business management became suspect after scandals at ENRON, WorldCom, and other business entities. Financially, many firms experienced difficulties from bubbles. The problems of interacting cultures demonstrated risk from terrorism as well, with numerous terrorist attacks, to include 9/11 in the U. S. Risks can arise in many facets of business. Businesses in fact exist to cope with risk in their area of specialization. Financial risk management has focused on banking, accounting, and finance. We have discussed several aspects of risk, to include information systems, disaster management, and supply chain perspectives. The bulk 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.
The book explores the swift advancements in computer technology and their profound impact on various aspects of society, including communication, education, and business. It delves into how these innovations shape human interaction, influence economic structures, and raise ethical questions. By examining the evolution of computer systems and their integration into daily life, the narrative highlights both the benefits and challenges posed by this technological surge, urging readers to consider the future implications of continued digital progress.
This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. The book is organized in three parts. Part I introduces concepts. Part II describes and demonstrates basic data mining algorithms. It also contains chapters on a number of different techniques often used in data mining. Part III focuses on business applications of data mining.
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. Inhaltsverzeichnis Chapter 1 Knowledge Management.- Chapter 2 Data Sets.- Chapter 3 Basic Forecasting ToolsChapter 3 Basic Forecasting Tools.- Chapter 4 Multiple Regression.- Chapter 5 Regression Tree Models.- Chapter 6 Autoregressive Models.- Chapter 7 GARCH Models.- Chapter 8 Comparison of Models.
Focusing on the evolution of supply chain efficiency, the book explores how modern vertical integration has shifted towards utilizing specialized organizations for specific tasks. It highlights the role of advanced technology in linking these specialists, resulting in faster and more cost-effective supply chains. Aimed at supply chain management practitioners, it surveys various information systems that enhance coordination and control, supported by analytic techniques and models to illustrate their design and functionality.
Supply chain management is rapidly evolving and becoming increasingly vital in today's global economy. This book offers insights into the latest trends, strategies, and technologies shaping the industry. It emphasizes the importance of efficiency and sustainability, providing practical tools for professionals to enhance their operations. Readers will find case studies and expert perspectives that illustrate the complexities of managing supply chains in a competitive environment, making it an essential resource for both newcomers and seasoned practitioners.
This guide offers a detailed, step-by-step system for navigating the residential real estate market, specifically designed for today's challenging economic climate. It focuses on strategies for buying and selling properties at full market value, providing practical insights and proven techniques to help readers succeed in real estate transactions.
Focusing on knowledge management, this book provides a comprehensive introduction to the field, integrating descriptive models with data mining analysis. It explores essential topics such as data visualization using R and Rattle, market basket analysis, smarketing RFM models, and association rules with the APriori algorithm. Additionally, it covers cluster analysis and link analysis, incorporating various open-source software tools. Each chapter builds on the previous one, culminating in a thorough understanding of both foundational concepts and advanced predictive models.
