Overview 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 book compiles information gathered from interesting research and innovation efforts in projects that were relevant to this scope, especially considering the evidences from state of the art and current research trends of Physical Asset Management (PAM) and Operations & Maintenance (O&M) of industrial plants and infrastructures. Among the new trends, digitalization is enabling new capabilities for asset management, by means of the appearance of Cyber Physical Systems (CPS), and the subsequent issues resulting from building the digital twins of the physical assets. This may lead to a new era of intelligent asset management systems. At the same time, basic principles of asset management will continue to be relevant in the new era, helping to guide the development of digitalization programs in assets intensive companies, and being transformed along the evolutionary path towards the achievement of a more digitized and intelligent management. Relevant Topics One of the main challenges in the field of physical asset management is to enhance the identification and quantification of cost and value to evaluate the total cost and value of industrial assets throughout their lifecycle. These concepts have been widely discussed in literature, by offering different perspectives and also using plenty of terms partially overlapping or providing slightly different interpretations. Terms such as TCO (Total Cost of Ownership), LCC (Life Cycle Cost), WLC (Whole Life Cost), COO (Cost of Ownership) and, if extending to values, TVO (Total Value of Ownership) and Whole Life Value (WLV), are widely cited. If one surfs the Internet, a myriad of definitions and references can be found. This does not mean that the terms are well understood and widely adopted in practice. Considering the industrial applications of TCO and TVO, it is worth remarking that their benefits are clearly envisioned (e.g., the benefits of TCO can be considered cost control support, management strategy selection, quality optimization, and best cost-effectiveness management). However, in practice, some missing links can be pointed out with regard to their use: even though the need and desire to implement life cycle costing is very much talked about, there are a number of difficulties that limit a widespread adoption by industry. This is even more challenging when extending to value and, thus, to the whole life value, which is a more recent concept. Another relevant challenge addressed by physical asset management, is the assurance of the cost and value along the asset lifecycle. Henceforth, appropriate “tools” are required in order to assure that the value delivery from industrial assets (at reasonable cost) is effectively achieved and, when not, that proper decisions are activated with the aim to guarantee value delivery. In particular, proper “tools” should be used when planning in advance, and when monitoring and controlling the effective outcomes, to eventually activate re-planning in case of extant discrepancies with respect to expectations, thus leading to a continuous improvement of what is decided over the asset life cycle. Identification and quantification of value delivered by the assets is essential in all the cases. Structure of the Book The book is divided into four Parts. In Part 1, the first Chapter introduces fundamental concepts used in this book and presents a generalized framework providing relevant dimensions of value-based and intelligent asset management. The rest of the chapters in this Part offer a long-term perspective of asset management, dealing with topics like societal impact of investments in infrastructure assets, performance and economic impacts of investments in manufacturing plants, and long-term deterioration and renewal of assets. In Part 2 the value-based decision-making approach is stressed as an overall perspective for management of the assets over their life cycle, and also exemplified in real world specific cases. The concept of value, understood as presented in the first Chapter of this book, is operationalized to drive day to day management decisions and activities. Part 3 is dedicated to different advanced developments at the operational level. Different tools are presented to predict and/or to determine properly assets conditions leading to the release and execution of the maintenance activities. Predictive analytics are used to make predictions about assets future behavior. Many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence can be applied to analyze current data to make predictions about future. The scalability of these emerging models, in this new scenario of individualised asset prognostics, is another topic discussed in this part of the book, trying to find a compromise between accuracy and computational power of these tools. Part 4 is devoted to new emerging processes, and new ideas that can be implemented by exploiting the power of new technologies such as cyber-physical systems that can certainly embed more intelligence and orientation to value in existing asset management systems. European Project and Worldwide Collaboration This book results from a collaboration of the authors, strengthened within the context of SustainOwner, ‘‘Sustainable Design and Management of Industrial Assets through Total Value and Cost of Ownership’’, a project sponsored by the EU Framework Programme Horizon 2020 and based on a knowledge sharing scheme involving many universities worldwide, from the Americas, Asia and Africa. Chapters Including Previously Published Research Results This book compiles a set of Chapters that were previously published as journal papers by the research groups involved in the Sustain Owner Project. The Editors would like to idenfify the correspondence between each book Chapter and the original research paper. According to Springer policy, the publishers were asked to provide their permissions for this work to be presented in its current form. The Editors thank the publishers for their cooperation making this book possible. The referred Chapters are: - Chapter 2: Heaton, J., Parlikad, A.K., “A conceptual framework for the alignment of infrastructure assets to citizen requirements within a smart cities framework,” Cities, Volume 90, pp 32-41, 2019. - Chapter 3: Roda I., Garetti M., “Application of a Performance-driven Total Cost of Ownership (TCO) Evaluation Model for Physical Asset Management”. In: Amadi-Echendu J., Hoohlo C., Mathew J. (eds) 9th WCEAM Research Papers. Lecture Notes in Mechanical Engineering. Springer, Cham, 2015, © Springer International Publishing Switzerland 2015, DOI 10.1007/978-3-319-15536-4. - Chapter 5: Roda, I., and M Macchi. “A framework to embed Asset Management in production companies.” Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 232, no. 4: 368-378, 2018, © IMechE 2018, DOI: 10.1177/1748006X17753501. - Chapter 6: Srinivasan, R., Parlikad, A.K., “An approach to value-based infrastructure asset management,” Infrastructure Asset Management, Volume 4, Issue 3, pp 87-95, 2017. - Chapter 9: Olivencia Polo F.A , Ferrero Bermejo J. Gómez Fernández JF., Crespo Márquez A..,”Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models”. Renewable Energy, Volume 81, pp 227-238. 2015. - Chapter 10: Liu, B., Liang, Z., Parlikad, A.K., Xie, M., Kuo, W., “Condition-based maintenance for systems with aging and cumulative damage based on proportional hazards model,” Reliability Engineering & System Safety, Volume 168, pp 200-209, 2017. - Chapter 11: C. Colace, L. Fumagalli, S. Pala, M. Macchi, N. R. Matarazzo, M. Rondi., “Implementation of a condition monitoring system on an electric arc furnace through a risk-based methodology.” Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, Volume 229, Issue 4, August 2015, 327-342, 2015, © IMechE 2015, DOI: 10.1177/1748006X15576441. - Chapter 12: Erguido A., Crespo Márquez A.. Castellano E., Gómez Fernández JF.,”A dynamic opportunistic maintenance model to maximize energy- based availability while reducing the life cycle cost of wind farms”. Renewable Energy, Volume 114, pp 843-856. 2017. - Chapter 13: Negri E., L. Fumagalli, M. Macchi, “A Review of the Roles of Digital Twin in CPS-based Production Systems”, in Proceedings 27th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM2017, Volume 11, 939-948, 27-30 June 2017, Modena, Italy, (Eds.) Marcello Pellicciari, Margherita Peruzzini, 2017, 2351-9789, © 2017 The Authors. Published by Elsevier B.V., doi: 10.1016/j.promfg.2017.07.198. - Chapter 14: Li, H., Salvador-Palau, A., Parlikad, A.K., “A Social Network of Collaborating Industrial Assets,” Proceedings of the IMechE Part O: Journal of Risk & Reliability, Volume 232, Issue 4, pp. 389-400, 2018, © IMechE 2018, DOI: 10.1177/1748006X18754975. - Chapter 15: Salvador-Palau, A., Liang, Z., Lutgehetmann, D., Parlikad, A.K., “Collaborative Prognostics in Social Asset Networks,” Future Generation Computer Systems, Volume 92, pp 987-995, 2019. - Chapter 16: Chekurov S, Metsä-Kortelainen S, Salmi M, Roda I, Jussila A., “The perceived value of additively manufactured digital spare parts in industry: an empirical investigation”. International Journal of Production Economics, 2015, 87-97, 2018, 0925-5273 © 2018 The Authors. Published by Elsevier B.V. T., DOI: 10.1016/j.ijpe.2018.09.008. Adolfo Crespo Márquez Marco Macchi Ajith Kumar Parlikad

Value Based and Intelligent Asset Management. Mastering the Asset Management Transformation in Industrial Plants and Infrastructures

M. Macchi;
2020-01-01

Abstract

Overview 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 book compiles information gathered from interesting research and innovation efforts in projects that were relevant to this scope, especially considering the evidences from state of the art and current research trends of Physical Asset Management (PAM) and Operations & Maintenance (O&M) of industrial plants and infrastructures. Among the new trends, digitalization is enabling new capabilities for asset management, by means of the appearance of Cyber Physical Systems (CPS), and the subsequent issues resulting from building the digital twins of the physical assets. This may lead to a new era of intelligent asset management systems. At the same time, basic principles of asset management will continue to be relevant in the new era, helping to guide the development of digitalization programs in assets intensive companies, and being transformed along the evolutionary path towards the achievement of a more digitized and intelligent management. Relevant Topics One of the main challenges in the field of physical asset management is to enhance the identification and quantification of cost and value to evaluate the total cost and value of industrial assets throughout their lifecycle. These concepts have been widely discussed in literature, by offering different perspectives and also using plenty of terms partially overlapping or providing slightly different interpretations. Terms such as TCO (Total Cost of Ownership), LCC (Life Cycle Cost), WLC (Whole Life Cost), COO (Cost of Ownership) and, if extending to values, TVO (Total Value of Ownership) and Whole Life Value (WLV), are widely cited. If one surfs the Internet, a myriad of definitions and references can be found. This does not mean that the terms are well understood and widely adopted in practice. Considering the industrial applications of TCO and TVO, it is worth remarking that their benefits are clearly envisioned (e.g., the benefits of TCO can be considered cost control support, management strategy selection, quality optimization, and best cost-effectiveness management). However, in practice, some missing links can be pointed out with regard to their use: even though the need and desire to implement life cycle costing is very much talked about, there are a number of difficulties that limit a widespread adoption by industry. This is even more challenging when extending to value and, thus, to the whole life value, which is a more recent concept. Another relevant challenge addressed by physical asset management, is the assurance of the cost and value along the asset lifecycle. Henceforth, appropriate “tools” are required in order to assure that the value delivery from industrial assets (at reasonable cost) is effectively achieved and, when not, that proper decisions are activated with the aim to guarantee value delivery. In particular, proper “tools” should be used when planning in advance, and when monitoring and controlling the effective outcomes, to eventually activate re-planning in case of extant discrepancies with respect to expectations, thus leading to a continuous improvement of what is decided over the asset life cycle. Identification and quantification of value delivered by the assets is essential in all the cases. Structure of the Book The book is divided into four Parts. In Part 1, the first Chapter introduces fundamental concepts used in this book and presents a generalized framework providing relevant dimensions of value-based and intelligent asset management. The rest of the chapters in this Part offer a long-term perspective of asset management, dealing with topics like societal impact of investments in infrastructure assets, performance and economic impacts of investments in manufacturing plants, and long-term deterioration and renewal of assets. In Part 2 the value-based decision-making approach is stressed as an overall perspective for management of the assets over their life cycle, and also exemplified in real world specific cases. The concept of value, understood as presented in the first Chapter of this book, is operationalized to drive day to day management decisions and activities. Part 3 is dedicated to different advanced developments at the operational level. Different tools are presented to predict and/or to determine properly assets conditions leading to the release and execution of the maintenance activities. Predictive analytics are used to make predictions about assets future behavior. Many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence can be applied to analyze current data to make predictions about future. The scalability of these emerging models, in this new scenario of individualised asset prognostics, is another topic discussed in this part of the book, trying to find a compromise between accuracy and computational power of these tools. Part 4 is devoted to new emerging processes, and new ideas that can be implemented by exploiting the power of new technologies such as cyber-physical systems that can certainly embed more intelligence and orientation to value in existing asset management systems. European Project and Worldwide Collaboration This book results from a collaboration of the authors, strengthened within the context of SustainOwner, ‘‘Sustainable Design and Management of Industrial Assets through Total Value and Cost of Ownership’’, a project sponsored by the EU Framework Programme Horizon 2020 and based on a knowledge sharing scheme involving many universities worldwide, from the Americas, Asia and Africa. Chapters Including Previously Published Research Results This book compiles a set of Chapters that were previously published as journal papers by the research groups involved in the Sustain Owner Project. The Editors would like to idenfify the correspondence between each book Chapter and the original research paper. According to Springer policy, the publishers were asked to provide their permissions for this work to be presented in its current form. The Editors thank the publishers for their cooperation making this book possible. The referred Chapters are: - Chapter 2: Heaton, J., Parlikad, A.K., “A conceptual framework for the alignment of infrastructure assets to citizen requirements within a smart cities framework,” Cities, Volume 90, pp 32-41, 2019. - Chapter 3: Roda I., Garetti M., “Application of a Performance-driven Total Cost of Ownership (TCO) Evaluation Model for Physical Asset Management”. In: Amadi-Echendu J., Hoohlo C., Mathew J. (eds) 9th WCEAM Research Papers. Lecture Notes in Mechanical Engineering. Springer, Cham, 2015, © Springer International Publishing Switzerland 2015, DOI 10.1007/978-3-319-15536-4. - Chapter 5: Roda, I., and M Macchi. “A framework to embed Asset Management in production companies.” Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 232, no. 4: 368-378, 2018, © IMechE 2018, DOI: 10.1177/1748006X17753501. - Chapter 6: Srinivasan, R., Parlikad, A.K., “An approach to value-based infrastructure asset management,” Infrastructure Asset Management, Volume 4, Issue 3, pp 87-95, 2017. - Chapter 9: Olivencia Polo F.A , Ferrero Bermejo J. Gómez Fernández JF., Crespo Márquez A..,”Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models”. Renewable Energy, Volume 81, pp 227-238. 2015. - Chapter 10: Liu, B., Liang, Z., Parlikad, A.K., Xie, M., Kuo, W., “Condition-based maintenance for systems with aging and cumulative damage based on proportional hazards model,” Reliability Engineering & System Safety, Volume 168, pp 200-209, 2017. - Chapter 11: C. Colace, L. Fumagalli, S. Pala, M. Macchi, N. R. Matarazzo, M. Rondi., “Implementation of a condition monitoring system on an electric arc furnace through a risk-based methodology.” Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, Volume 229, Issue 4, August 2015, 327-342, 2015, © IMechE 2015, DOI: 10.1177/1748006X15576441. - Chapter 12: Erguido A., Crespo Márquez A.. Castellano E., Gómez Fernández JF.,”A dynamic opportunistic maintenance model to maximize energy- based availability while reducing the life cycle cost of wind farms”. Renewable Energy, Volume 114, pp 843-856. 2017. - Chapter 13: Negri E., L. Fumagalli, M. Macchi, “A Review of the Roles of Digital Twin in CPS-based Production Systems”, in Proceedings 27th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM2017, Volume 11, 939-948, 27-30 June 2017, Modena, Italy, (Eds.) Marcello Pellicciari, Margherita Peruzzini, 2017, 2351-9789, © 2017 The Authors. Published by Elsevier B.V., doi: 10.1016/j.promfg.2017.07.198. - Chapter 14: Li, H., Salvador-Palau, A., Parlikad, A.K., “A Social Network of Collaborating Industrial Assets,” Proceedings of the IMechE Part O: Journal of Risk & Reliability, Volume 232, Issue 4, pp. 389-400, 2018, © IMechE 2018, DOI: 10.1177/1748006X18754975. - Chapter 15: Salvador-Palau, A., Liang, Z., Lutgehetmann, D., Parlikad, A.K., “Collaborative Prognostics in Social Asset Networks,” Future Generation Computer Systems, Volume 92, pp 987-995, 2019. - Chapter 16: Chekurov S, Metsä-Kortelainen S, Salmi M, Roda I, Jussila A., “The perceived value of additively manufactured digital spare parts in industry: an empirical investigation”. International Journal of Production Economics, 2015, 87-97, 2018, 0925-5273 © 2018 The Authors. Published by Elsevier B.V. T., DOI: 10.1016/j.ijpe.2018.09.008. Adolfo Crespo Márquez Marco Macchi Ajith Kumar Parlikad
2020
Springer Nature Switzerland AG
978-3-030-20703-8
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