Value Based And Intelligent Asset Management

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.

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.

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.

This book provides a thorough overview of the integration of cyber-physical systems and maintenance management models. It begins by explaining the fundamental concepts behind maintenance digital transformation. It discusses key decision areas in digital maintenance management, particularly focusing on strategic dimensions of maintenance, digital twin definition and strategy, and industry 4.0 digital tools frameworks to support emerging maintenance processes. Furthermore, the monograph dedicates time to the integration of digital maintenance with the entire digital factory. By presenting the possibilities for asset utilization improvement and for asset value enhancements, Digital Maintenance Management provides engineers and practitioners responsible for the management of complex industrial assets a complete guide to piloting the maintenance digital transformation.