The fundamental motivation of this book is to contribute to the future advancement of Asset Management in the context of industrial plants and infrastructures. The book aims to foster a future perspective that takes advantage of value-based and intelligent asset management in order to make a step forward with respect to the evolution observed nowadays. Indeed, the current understanding of asset management is primarily supported by well-known standards. Nonetheless, asset management is still a young discipline and the knowledge developed by industry and academia is not set in stone yet. Furthermore, current trends in new organizational concepts and technologies lead to an evolutionary path in the field. Therefore, this book aims to discuss this evolutionary path, starting first of all from the consolidated theory, then moving forward to discuss: • The strategic understanding of value-based asset management in a company; • An operational definition of value, as a concept on the background of value-based asset management; • The identification of intelligent asset management, with the aim to frame a set of “tools” recommended to support the asset-related decision-making process over the asset lifecycle; • The emergence of new technologies such as cyber physical systems and digital twins, and the implications of this on asset management.

This book presents a systematic application of recent advances in artificial intelligence (AI) to the problem of asset management. While natural language processing and text mining techniques, such as semantic representation, sentiment analysis, entity extraction, commonsense reasoning, and fact checking have been evolving for decades, finance theories have not yet fully considered and adapted to these ideas. In this unique, readable volume, the authors discuss integrating textual knowledge and market sentiment step-by-step, offering readers new insights into the most popular portfolio optimization theories: the Markowitz model and the Black-Litterman model. The authors also provide valuable visions of how AI technology-based infrastructures could cut the cost of and automate wealth management procedures. This inspiring book is a must-read for researchers and bankers interested in cutting-edge AI applications in finance.

Whole-Life Value-Based Decision-Making in Asset Management is a comprehensive guide to improving the effectiveness of infrastructure asset management by determining the level of expenditure on infrastructure assets in order to maximise life-cycle value.

Time-Tested Techniques - Safe, Simple, and Proven Effective - for Building Your Own Investment Portfolio. "As its title suggest, Bill Bernstein's fine book honors the sensible principles of Benjamin Graham in the Intelligent Investor Bernstein's concepts are sound, his writing crystal clear, and his exposition orderly. Any reader who takes the time and effort to understand his approach to the crucial subject of asset allocation will surely be rewarded with enhanced long-term returns." - John C. Bogle, Founder and former Chief Executive Officer, The Vanguard Group President, Bogle Financial Markets Research Center Author, common Sense on Mutual Funds. "Bernstein has become a guru to a peculiarly '90s group: well-educated, Internet-powered people intent on investing well - and with minimal 'help' from professional Wall Street." - Robert Barker, Columnist, BusinessWeek. "I go home and tell my wife sometimes, 'I wonder if [Bernstein] doesn't know more than me.' It's humbling." - John Rekenthaler, Research Chief, Morningstar Inc. William Bernstein is an unlikely financial hero. A practicing neurologist, he used his self-taught investment knowledge and research to build one of today's most respected investor's websites. Now, let his plain-spoken The Intelligent Asset Allocator show you how to use the time-honored techniques of asset allocation to build your own pathway to financial security - one that is easy-to-understand, easier-to-apply, and supported by 75 years of solid history and wealth-building results.

This book reports on innovative strategies for quality control, risk assessment and sustainable development in production processes, in the era of industry 4.0. Based on peer-reviewed contributions to the 7th International Scientific-Technical Conference MANUFACTURING 2022, held on May 16–19, 2022, in Poznan, Poland, the chapters cover important topics relating to the use of quality management strategies in different stages of the production processes. They report on methods for statistical process control, vision control and inspection of machines, on the application of machine learning methods in quality control and/or risk assessment, on issues relating to digital transformation, and on methods to improve occupational safety. Besides industrial applications, the book also discusses the use quality management tools for educational purposes. By bridging between concepts in quality engineering, ergonomics, digitalization and industry 4.0, this book offers an authoritative source of information for researchers, engineers and managers.

It is critical to improve the asset management system implementation as well as economics and industrial decision making to ensure that a business may move smoothly internally. Maintenance management should be aligned to the activities of maintenance in accordance with key business strategies, which must be designed under the comprehensive approach of an asset management process. After transforming the priorities of the business into priorities of maintenance, maintenance managers will use their medium-team strategies to tackle potential weaknesses in the maintenance of the equipment in accordance with these objectives. Cases on Optimizing the Asset Management Process explains and summarizes the processes and the reference frame necessary for the implementation of the Maintenance Management Model (MMM). This book acts as an overview of the current state of the art in asset management, providing innovative tools and practices from the fourth industrial revolution. Presenting topics like criticality analysis, physical asset maintenance, and unified modelling language, this text is essential for industrial and manufacturing engineers, plant supervisors, academicians, researchers, advanced-level students, technology developers, and managers who make decisions in this field.

'Whole-Life Value Based Decision Making in Asset Management' is a comprehensive guide to improving the effectiveness of infrastructure asset management by determining the level of expenditure on infrastructure assets in order to maximise lifecycle value.

Make AI technology the backbone of your organization to compete in the Fintech era The rise of artificial intelligence is nothing short of a technological revolution. AI is poised to completely transform asset management and investment banking, yet its current application within the financial sector is limited and fragmented. Existing AI implementations tend to solve very narrow business issues, rather than serving as a powerful tech framework for next-generation finance. Artificial Intelligence for Asset Management and Investment provides a strategic viewpoint on how AI can be comprehensively integrated within investment finance, leading to evolved performance in compliance, management, customer service, and beyond. No other book on the market takes such a wide-ranging approach to using AI in asset management. With this guide, you’ll be able to build an asset management firm from the ground up—or revolutionize your existing firm—using artificial intelligence as the cornerstone and foundation. This is a must, because AI is quickly growing to be the single competitive factor for financial firms. With better AI comes better results. If you aren’t integrating AI in the strategic DNA of your firm, you’re at risk of being left behind. See how artificial intelligence can form the cornerstone of an integrated, strategic asset management framework Learn how to build AI into your organization to remain competitive in the world of Fintech Go beyond siloed AI implementations to reap even greater benefits Understand and overcome the governance and leadership challenges inherent in AI strategy Until now, it has been prohibitively difficult to map the high-tech world of AI onto complex and ever-changing financial markets. Artificial Intelligence for Asset Management and Investment makes this difficulty a thing of the past, providing you with a professional and accessible framework for setting up and running artificial intelligence in your financial operations.

This book gathers selected peer-reviewed papers from the 16th World Congress on Engineering Asset Management (WCEAM), held in Seville from 5–7 October 2022. This book covers a wide range of topics in Engineering Asset Management, including: Asset management and decision support system Industry 4.0 tools and its impact on asset management Monitoring, diagnostics and prognostics for smart maintenance Asset life cycle management Asset management in the industrial sector Human dimensions and asset management performance Infrastructure Asset management Asset condition, risk, resilience, and vulnerability assessments Asset operations and maintenance strategies Reliability and resilience engineering Applications of international and local guidelines and standards The breadth and depth of this state-of-the-art, comprehensive proceedings make it an excellent resource for asset management practitioners, researchers and academics, as well as undergraduate and postgraduate students.