Case Studies in Digital Transformation
Integration of Digital Technologies to Enhance Asset Management Processes
- 2026
- Book
- Editors
- Adolfo Crespo Márquez
- Turuna Seecharan
- Georges Abdul-Nour
- Joe Amadi-Echendu
- Jay Lee
- Book Series
- Engineering Asset Management Review
- Publisher
- Springer Nature Switzerland
About this book
This book is exploration into the forefront of digital evolution, presenting a compelling array of real-world case studies that underscore the transformative impact of emerging processes and services on asset management. The book goes beyond theoretical frameworks, offering a dynamic narrative that reveals how organizations across industries are strategically integrating cutting-edge technologies into their asset management practices.
With a keen focus on innovation, the book examines the key role of IoT, artificial intelligence, and other groundbreaking technologies in reshaping traditional asset management paradigms. Readers will gain valuable insights into success stories, challenges faced, and the strategic considerations that underpin these transformative journeys. The book's coverage highlights the tangible benefits of digital solutions, such as enhanced efficiency, heightened resilience, and strategic advantage.
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Table of Contents
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Frontmatter
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Digital Transformation and Value Creation in Asset Management
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Frontmatter
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New Value Perspective for Value Creation with Social Priceless Assets Through Digital Transformations
Amir Farmanesh, Joaquín Ordieres-Meré, Antonia Pacios Álvarez, Ángel Paris LoreiroThis chapter delves into the transformative potential of digital technologies in managing public assets and emergency response systems. It explores two key use cases: smart parking solutions and emergency healthcare management. The study highlights the integration of IoT devices, machine learning models, and real-time data analytics to optimize parking space utilization and improve emergency response efficiency. The chapter also discusses the challenges and benefits of implementing these technologies, including enhanced compliance, reduced operational costs, and improved public safety. The results demonstrate that moderate investments in digital infrastructure can yield significant improvements in service delivery and operational efficiency. The conclusion emphasizes the need for continuous IT support and adaptation to evolving technologies to ensure the effectiveness and efficiency of these systems. Professionals will gain insights into the practical applications and strategic planning required for successful digital transformation in public services.AI Generated
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AbstractThis paper investigates the potential of digital transformation (DT) in enhancing the management and value of public assets that are currently under-managed due to limited information and their inherent social value. Focusing on regions where these assets are in high demand, the lack of effective management not only wastes time but also contributes to environmental pollution and endangers public safety. To address these challenges, we explore two pivotal use cases: the management of handicapped parking and reserved load/unload spaces, and Public primary emergency response services. The first use case employs a smart monitoring system to track usage patterns and demands, thereby facilitating better asset management and decision-making. The second use case introduces and propose IoT emergency response management system that digitalizes communications and management of emergency response services through mobile applications, wearable devices and sensors, cloud computing, and machine learning models. This system allows individuals to contact public safety answering points (PSAP) and request emergency services via their mobile devices, providing critical information such as location, personal health data, real-time health monitoring streamed by wearable devices and sensors, and real-time data like image, video and direct communication like live voice or video. Furthermore, AI models trained on historical and incoming data analyze this information to assist PSAP agents and provide recommendations. This system not only ensures faster and more accurate responses during emergencies but also collects comprehensive data—including voice, text, events, images, and videos—throughout the incident. Such data is crucial for ongoing analysis and process improvement, supporting future decision-making, training first responders, and enhancing AI models for predictive analytics. In addition to improving emergency service speed and efficiency, which enhances public satisfaction, this system also advances and improves management of the emergency response service as a public asset by optimizing resource management and allocation, providing comprehensive statistics and insights that aid in process and managerial decision-making. This work underscores the transformative power of digital solutions in managing public assets and enhancing public safety, highlighting the organizational implications and the necessary adjustments in change management strategies. -
A Roadmap for Digital Transformation in the Architecture, Engineering, Construction, and Operations Industry: A Survey and Industry Analysis
Seyed Mohammad Hossein Seyedi Rezvani, Maria João Falcão Silva, Nuno Marques de AlmeidaThis chapter delves into the digital transformation journey of the Architecture, Engineering, Construction, and Operations (AECO) industry, highlighting the significant productivity challenges and barriers to digital adoption. It explores the current state of digitalization through a systematic literature review and expert interviews, identifying key trends, challenges, and best practices. The article presents a roadmap for digital transformation, tailored to companies of different sizes and project types, emphasizing the importance of strategic planning, technology adoption, skills development, and continuous improvement. It also showcases three case studies illustrating the practical application of the digital transformation roadmap. The chapter concludes with a discussion on the complex landscape of digitalization in the AECO industry and the critical factors for successful digital transformation, including employee training and cultural change.AI Generated
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AbstractThe Architecture, Engineering, Construction, and Operations (AECO) industry lags in digital adoption despite its potential to enhance productivity and efficiency. This study develops a roadmap for digital transformation within the AECO sector, informed by both a thorough literature review and a survey of industry practitioners across various geographical regions and project types. The research examines challenges hindering digital progress, including low research and development spending, resistance to change, and a skills gap in digital technologies. The study investigates how digitalization challenges differ across various company sizes and project types, including residential buildings, infrastructure, and specialized infrastructure. The proposed roadmap outlines key steps for successful digital adoption, encompassing strategic planning, technology selection, skills development, and process optimization. Emerging technologies like Artificial Intelligence (AI), Virtual Reality (VR), and digital twins are explored for their potential to revolutionize construction practices and enhance productivity. Insights from this research provide a practical guide for AECO stakeholders to overcome barriers to digital adoption and accelerate their digital transformation journey, leading to improved efficiency, collaboration, and sustainability within the industry. -
Resilience and Life Cycle Management of Critical Urban Asset Systems in Face of Emerging Threats
Seyed Mohammad Hossein Seyedi Rezvani, Maria João Falcão Silva, Nuno Marques de AlmeidaThis chapter delves into the complexities of emerging urban risks and explores how life cycle management (LCM) principles can be effectively integrated to bolster the resilience of critical urban asset systems. It begins with an introduction to the study's context and significance, followed by a Systematic Literature Review detailing the methodology, search strategy, and selection criteria. The paper addresses emerging threats and risk assessment methodologies, proposes a framework for enhancing urban resilience, and provides practical examples of its application. It concludes with a discussion and summary of key findings, suggesting directions for future research. The chapter also includes bibliometric analysis and VOSviewer visualizations to illustrate literature connections and trends. By integrating resilience thinking with life cycle management approaches, the study offers practical guidance for city administrators and policymakers, helping them to create robust and adaptive urban systems capable of thriving in the face of uncertainty and change.AI Generated
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AbstractWith rapid urbanization comes an array of evolving risks. These risks, spanning environmental, socio-economic, and infrastructural realms, present a growing challenge to the stability of urban habitats. However, through strategic asset and risk management, cities have the potential to safeguard their futures. This study seeks to elucidate the nature of emerging urban risks and juxtapose them against the principles of asset life cycle management as well as critically examine the interplay between emerging risks in urban landscapes applied to critical infrastructure and the role other types of urban critical assets and asset and risk management in fortifying city resilience systems. By dissecting how cities can optimize their resources, both tangible and intangible, the research underscores how urban centers can not only shield themselves against immediate threats but also invest in long-term resilience. As we pivot towards a world where urban centers become even more central to global progress, this study aims to provide a robust framework for city administrators and policymakers, guiding them towards creating resilient and prosperous urban spaces. -
The Impact of Blockchain Technology for Sustainable Asset Maintenance in Telecoms Domain in Emerging Markets
Charles Okeyia, Nuno Marques de AlmeidaThis chapter delves into the impact of blockchain technology on sustainable asset maintenance in the telecoms domain, particularly in emerging markets. It highlights the persistent problems of power supply and asset malfunctionality caused by ineffective maintenance practices, which are critical for reliable network services. The text explores how blockchain can facilitate real-time monitoring of activities, contributing to effective asset maintenance management practices. It also discusses the transformative potential of blockchain in streamlining processes and minimizing inefficiencies in asset operations and maintenance. The chapter provides a detailed analysis of the current asset maintenance management practices and the challenges faced by telecom providers. It offers a comprehensive overview of the features and capabilities of blockchain technology that foster efficient asset maintenance practices through transparency, trust, security, traceability, and immutability. The text also presents a case study of IHS Towers in Nigeria, illustrating the practical application of blockchain in telecom asset maintenance. The findings from the study indicate that blockchain technology shows significant considerations and differences with a p-value of < 0.01, signifying the importance of blockchain technology's features of transparency, real-time, and immutability in enhancing asset maintenance management. The chapter concludes by highlighting the multifaceted advantages of blockchain technology in promoting sustainable asset management practices and offers a blockchain-based resolution for adoption in the telecom domain in developing markets.AI Generated
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AbstractBlockchain technology (BCT) is a paradigm shift in the telecom domain, offering unparalleled benefits in asset maintenance adoption. It opens up new avenues for managing asset infrastructure, especially in the operation and maintenance activities and phases of asset life cycle management. This extended phase is pivotal for tackling intricate asset issues that involve energy supply concerns and intermittent outages caused by unstable, irregular and unreliable power supply and inefficient maintenance practices. Thus, customers face challenges related to unreliable network services, while telecom providers experience maintenance and operations challenges on their asset performance and operating expenditure. The primary aim of this study was to increase the understanding of sustainable asset maintenance management in emerging markets using blockchain technology. The study used a case study methodology to demonstrate its practical importance by drawing from a qualitative and quantitative data collection approach; this study also analysed the various features and characteristics of blockchain technology and identified some of the benefits of sustainable asset maintenance management. The quantitative data were analysed with SPSS AMOS by performing structural equation modelling to assist in supporting the theories and research and also understanding the variables to explore their relationships. The qualitative data were transcribed using the descriptive analysis of the semi-structured interview data. Our analysis of the procedures and adoption of each identified value of PdM and CBM from the BCT maintenance adoption, based on the extant studies and the generated quantitative data, reveals significant potential cost savings. The BCT application's effectiveness in asset performance not only significantly enhances network quality but also serves as a promising indicator of operations and maintenance benefits. The results present a robust data-driven model for BCT adoption, which not only enhances confidence in the thoroughness and validity of the research process but also instils a sense of encouragement in the research findings. The findings enable the BCT framework to predict asset infrastructure outages, ensuring an efficient integration of the asset maintenance activities in a BCT approach and adoption. This action includes addressing resource issues, outage visibility and monitoring, and asset infrastructure performance degradation, all of which contribute to reducing operating costs and enhancing financial sustainability. The potential for cost savings and improved network quality is a beacon of hope for the telecom industry's future.
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Data-Driven Asset Management and IoT Applications
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Frontmatter
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From Data to Decisions: Empowering Pipeline Operators with Actionable Insights
Ana Silva, Luís Evangelista, Cláudia Ferreira, Jónatas Valença, Maria Paula MendesThis chapter delves into the critical factors influencing gas pipeline failures, emphasizing the complex interplay between physical, operational, and environmental elements. By analyzing historical data from the U.S. Pipeline and Hazardous Materials Safety Administration (PHMSA), the study identifies key causes of incidents, including external forces, corrosion, and construction defects. The analysis reveals that while time-dependent failures like corrosion have decreased due to advancements in materials and safety standards, time-independent failures such as those caused by natural disasters and third-party actions have become more prominent. The study also highlights the shift from steel to plastic pipelines and the associated risks, including a higher potential for explosions. Additionally, it underscores the importance of proactive maintenance and robust risk assessment strategies to mitigate these risks. The findings provide valuable insights for pipeline operators to prioritize inspections and maintenance, ultimately enhancing the safety and reliability of gas transmission infrastructures.AI Generated
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AbstractUnderstanding the mechanisms behind pipeline failures is critical for identifying vulnerabilities in gas transmission pipelines and developing strategies to enhance energy supply chain reliability. Pipelines are recognised as the most cost-effective and reliable solution for energy transportation, playing a vital role in modern societies among increasing global demand for natural gas resources. Despite their reliability, pipelines are susceptible to various deterioration mechanisms, making them prone to catastrophic incidents. While fatalities due to pipeline leaks are relatively low in absolute numbers, the consequences pose significant threats to human safety, the environment, and economic stability. Pipeline failures result from cumulative aging processes influenced by physical, operational, and environmental factors. Time-dependent hazards such as corrosion evolve progressively, while time-independent hazards like natural disasters and third-party activities pose immediate risks. The interplay among these factors requires a comprehensive understanding to prioritize maintenance and implement effective risk control measures. This study proposes an innovative approach to analyse historical pipeline failure data, based on incident records from 1970 to 2023 provided by the Pipeline & Hazardous Materials Safety Administration (PHMSA) of the United States. With the United States housing 65% of the world’s pipeline length, a dataset of 12,182 incidents from 1970 to 2023 provides a unique opportunity for analysis. However, the lack of comprehensive historical failure data analysis underscores the need for predictive models capable of identifying multi-cause-and-effect relationships. To address this gap, by offering a data-driven and precise prediction of incident years, this study enhances understanding of underlying causes and circumstances, enabling interventions to mitigate future incidents. By adopting a “lessons learned” perspective, this study provides strategic insights for operators to proactively address potential vulnerabilities, promoting sustained operational integrity and minimizing unexpected events throughout pipeline service life. This study converts data from self-contained case reports into a user-friendly knowledge framework. The results are expected to assist pipeline operators in evaluating and predicting the condition of existing gas pipelines, enabling them to prioritize inspections and maintenance activities effectively. By using the heat maps created in this study, potential failure points can be identified proactively, assisting the implementation of timely maintenance procedures tailored to the estimated service life of each pipeline. By leveraging this knowledge for proactive maintenance, organizations can mitigate risks, enhance operational efficiency, and gain a competitive advantage in their industries. -
Industrial IoT and 3D Digital Twin for Real Time-Evergreening of Engineering Assets Subjected to Corrosion Under Insulation
Alvaro Rodríguez-Prieto, Alberto Mura, Eduardo Nuevo, Manuel Callejas, Ernesto Primera, Franco GambatoThis chapter delves into the critical issue of corrosion under insulation (CUI) in engineering assets, particularly those made of carbon steel and subjected to thermal insulation. The text highlights the traditional maintenance strategies—failure maintenance, preventive maintenance, and predictive maintenance—and introduces a novel methodology that leverages Industrial IoT and 3D Digital Twin technologies for real-time monitoring and dynamic risk analysis. Key topics include the factors influencing CUI, such as moisture, temperature, and the presence of chlorides or sulfides, as well as the critical areas prone to CUI in various types of equipment and piping systems. The chapter also discusses the integration of IoT sensors for monitoring parameters like external corrosion rate, moisture level, and coating condition, and the use of advanced software platforms for data management and risk assessment. The implementation of a digital twin allows for a better understanding of the level of risk on the plant, while mobile apps and CMMS systems enhance the monitoring, management, and repair activities. The conclusion emphasizes the economic benefits and improved reliability of assets achieved through this methodology, making it a valuable resource for professionals in asset integrity and maintenance.AI Generated
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AbstractCorrosion under insulation (CUI) is a big problem that can affect any industrial process plant and could generate plant unavailability with often high production losses. CUI is a phenomenon that normally affects carbon steel pipes and pressure equipment, as a result of the entry or condensation of water under the thermal insulation. The recent development of technologies 4.0 opens the door to real-time monitoring of relevant degradation mechanisms like CUI. The advancement of non-destructive testing techniques and their practical applications provide data with more quality. During the last decade, several disruptive technologies has been consolidated, such as the Internet of Things (IoT) and big data, as well as the popularization of data science, Digital Twins and artificial intelligence-based techniques. Recently, the worldwide industry has required a mindset change, switching from corrective maintenance to predictive maintenance and recently focusing on approaches related to prescriptive maintenance and prognosis. Therefore, the aim of this work is to present a new methodology for dynamic risk analysis and evaluation of engineering-assets performance. This type of tools that are based on the use of collected data (by using IoT sensors) of process parameters and materials condition can provide a real time (updated) evergreening of engineering assets like thermal-insulated piping and equipment based on a robustly built Dynamic Digital Twin. -
Optimization of Maintenance Management Through Digital Integration: AMORMS Audit and SAP Implementation in a Cosmetics Manufacturing Company
Pablo Andrés Duque Ramírez, Carlos Parra, Félix Pizarro, Vicente Gonzalez-Prida, Carlos Baldi, Emanuel VegaThis case study delves into the optimization of maintenance management through digital integration in a cosmetics manufacturing company. The focus areas include the implementation of SAP for real-time data analytics and predictive maintenance, the application of the AMORMS audit to identify areas for improvement, and the adoption of agile methodologies like Scrum for continuous improvement. The study reveals significant deficiencies in the current maintenance management model, such as the lack of structured policies, performance indicators, and inventory management models. By implementing these digital tools and methodologies, the company aims to transition from reactive to proactive maintenance strategies, ultimately enhancing operational efficiency and profitability. The results of the AMORMS audit highlight critical areas for improvement, including asset management, risk-based hierarchy models, and life cycle cost analysis. The proposed improvement plan, supported by external consultants and SAP integration, is expected to yield substantial savings and a quick return on investment, demonstrating the feasibility and effectiveness of digital integration in maintenance management.AI Generated
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AbstractThis study aims to enhance the maintenance management of a Chilean cosmetics manufacturing company through the application of the AMORMS audit (Asset Management, Operational Reliability & Maintenance Survey). The audit follows the 8-phase Maintenance Management Model (MGM) proposed by INGEMAN, which identified several areas for improvement in the company’s maintenance processes. The audit results revealed that six out of eight maintenance processes were classified as “below average.” Based on these findings, specific improvement actions were proposed for each phase of the model, categorized into four key areas: Effectiveness, Efficiency, Optimization, and Continuous Improvement. The proposed actions include defining maintenance objectives and policies, developing a ranking method and criticality analysis of assets, identifying and analysing failures, designing efficient maintenance plans, and implementing continuous improvement processes. The implementation of these improvements will be guided by agile methodologies, specifically Scrum, to organize the work in iterative cycles and allow for continuous adjustments. This approach enables the company to remain flexible, making data-driven decisions based on feedback and real-time insights. The integration of SAP as a management and data collection tool is central to this process, serving as the core system for tracking performance, analysing maintenance needs, and ensuring that improvements are aligned with operational goals. SAP’s capabilities for integrating maintenance schedules, asset data, and real-time reporting provide the company with a comprehensive view of asset performance, facilitating strategic decision-making. External consulting will play a key role in optimizing the use of SAP and guiding the development of strategic asset management plans, ensuring that the improvements are effectively implemented and sustained over time. Additionally, consultants will provide staff training on the use of SAP to maximize the value of data integration and process automation in the maintenance management framework. A technical–economic analysis indicates that a 2% increase in equipment availability in the selected pilot area could generate savings equivalent to 20% of the maintenance budget, resulting in a benefit–cost ratio (BCR) of 4. These results suggest that the financial benefits would significantly exceed the costs within the first year of implementation, greatly improving the economic viability of the project.
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Case Studies and Implementation Frameworks
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Frontmatter
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Implementation of an Asset Management Plan According to ISO 55001 at the Colmito Power Generation Plant—Chile
Carlos Parra, Carlos Baldi, Cristian Cuadra, Pablo Duque, José Núñez, Juan Araya, Jorge Parra, Vicente González-PridaThis chapter delves into the implementation of an Asset Management Plan according to ISO 55001 at the Colmito Power Generation Plant in Chile. It begins with an introduction to the maintenance management process, emphasizing the importance of aligning maintenance activities with the organization's business strategy. The text explores the Maintenance Management Model (MMM), which comprises eight interconnected blocks focusing on management effectiveness, planning, evaluation, and continuous improvement. Key techniques such as the Balanced Scorecard (BSC), Root Cause Analysis (RCA), and Reliability-Centered Maintenance (RCM) are discussed in detail, highlighting their role in defining and implementing maintenance strategies. The chapter also covers the integration of the MMM with the ISO 55000 asset management standard, providing a structured approach to achieving efficiency and effectiveness in asset management. A case study of the Colmito Power Generation Plant is presented, illustrating the audit process and the development of an improvement plan based on the ISO 55001 requirements. The conclusion summarizes the findings and outlines action plans for enhancing the asset management system, offering practical insights for professionals in the field.AI Generated
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AbstractThe proposed article describes the key factors of a Maintenance Management Model (MMM) aligned with an Asset Management implementation plan following the NCh-ISO 55001 standard for the Colmito Power Generation Plant in Chile. The aim is to improve service quality by reducing downtime caused by faults, optimizing operations, and extending asset lifespan. This implementation aligns with current legislation and fulfills the objectives and goals defined in asset management, in compliance with Supreme Decree 109 of 2017 from the Ministry of Energy. This decree regulates the safety of electrical installations involved in production, transportation, provision of complementary services, energy storage systems, and the distribution of electrical energy. It specifically refers to Normative Technical Specification RPTD No. 17, which establishes the requirements for the SGIIE (Integrity Management System for Electrical Installations) in Chile. The first part describes the most important aspects of the MMM: Maintenance Management Model developed by INGEMAN. It discusses the eight phases of the MMM and explains the key fundamentals that organizations must implement to apply the MMM and optimize the technical and economic performance of assets throughout their life cycle. Additionally, it analyzes the relationship between the MMM and the requirements of the Asset Management Standard ISO 55001. The second part provides details on the AMS-ISO 55001 diagnostic tool (Asset Management Survey—ISO 55001), used to assess the requirements stipulated by the Asset Management standard NCh-ISO 55001 at the Colmito Power Generation Plant, referencing a tool developed by the IAM (Institute of Asset Management). The article also includes the results of the maturity level assessment of the Colmito Power Generation Plant in relation to the requirements of the NCh-ISO 55001 standard. Additionally, the operational context of the Colmito Power Generation Plant and the regulatory framework of the electrical market are presented to provide an understanding of the organization and its interactions with various market entities. Finally, the article describes the guidelines for the development and implementation of the Asset Management System for the Colmito Power Generation Plant. It outlines the planning, support, operation, performance evaluation, and improvement processes proposed to consolidate the Asset Management Strategic Plan (PGEA). The goal is to comply with items 4 to 10 of the NCh-ISO 55001 standard in order to minimize operational risks and maximize the profitability of the assets of the Colmito Power Generation Plant throughout their entire lifecycle. -
BIM-Powered Asset Management Solutions—Digitalizing Building Maintenance
Jónatas Valença, Cláudia Ferreira, Maria Paula Mendes, Ana SilvaThis chapter explores the integration of Building Information Modelling (BIM) with advanced technologies to create proactive building maintenance strategies. It highlights the importance of transitioning from reactive to proactive maintenance to prevent costly repairs and extend the lifespan of buildings. The methodology involves image-based assessments of building facades using computer vision and machine learning algorithms to detect and map damages. These assessments are then used to compute a degradation index and analyze maintenance costs across various scenarios. The information is integrated into BIM models, enabling comprehensive analysis and visualization of building conditions over time. The case study of Bairro De Alvalade in Lisbon demonstrates the practical application of this methodology, showcasing how different maintenance plans impact degradation and costs. The chapter concludes by discussing the benefits and challenges of implementing BIM-powered asset management solutions, emphasizing their potential to enhance collaboration and decision-making among stakeholders.AI Generated
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AbstractBuilding maintenance is often seen as a low-priority financial burden, which is a paradox, since given that the built environment corresponds to approximately 50% of the wealth of most European countries. Buildings are one of the most valuable assets of any individual or collective entity, public or private. Reactive maintenance, based on subjective criteria, is still current practice. In a Society with scarce resources facing the challenges of economic and environmental sustainability, typified measures for the maintenance of buildings can no longer be applied. Currently, paper-based records and digital spreadsheets are still the best approach used in buildings’ management, resulting in inefficient operations and maintenance. Building information modelling (BIM) is a practical approach to store, visualize and exchange building information in design and construction, but it is still at its beginning concerning the operation and maintenance phases, which comprise around 80% of whole life-cycle costs. To overcome the current limitations, image-based methods for the inspection of buildings’ facades can be very effective, particularly when combined with advanced technologies such as computer vision and machine learning. The image-based methods allow a promptly capture of large amounts of data in a relatively short amount of time. This can lead to more efficient inspections, especially for a large set of buildings. The computer vision and machine learning algorithms can automate the analysis of images, making it easier to detect defects, anomalies, or critical areas. These algorithms can be trained on large datasets to improve accuracy and reliability over time. Factors such as lighting conditions, weather, and the presence of obstructions can affect the quality of images, and the effectiveness of analysis algorithms, being essential to take it into account. This paper presents a methodology to build a digital model of buildings to support management maintenance, by merging the damage maps by computer vision and a degradation index, both automatically obtained. The automatic mapping of facades is performed by a supervised classification, followed by the calculation of the degradation index, based on the maps obtained. The results are introduced in a digital model of the buildings (such as BIM) for a comprehensive analysis of the actual state of conservation on the building as well as its evolution during service life. The methodology is showcased with a real building, evaluating the impact of different maintenance strategies in the performance of the case study under analysis. The results demonstrate the applicability of the proposed approach to support decision making in terms of maintenance. The final digital model enables the integration of all data, being a valuable and useful tool for different stakeholders’, such as asset managers. -
Assessment of the Technical and Functional Performance of Public Buildings
Frederico Hooper, Francielle Santos, Filipa Salvado, Nuno AlmeidaThis chapter delves into the critical role of public buildings in society and the importance of asset management for their sustainability. It presents a methodology for assessing the technical and functional performance of public buildings over their life cycle, focusing on a case study of secondary school buildings in Lisbon. The methodology employs two distinct approaches: one based on the remaining service life of building subsystems and the other on performance indicators evaluated through a questionnaire. The assessment process adheres to the 'Plan-Do-Check-Act' methodology and includes steps such as identifying requirements, specifying assessment strategies, observation, assessment, and validation. The results of the assessment, which include a classification system for building performance, highlight the positive impact of recent rehabilitation and modernization efforts on the performance of these buildings. The chapter concludes with a discussion on the broader implications of the methodology for various stakeholders, including facility managers, building owners, regulatory bodies, and end users.AI Generated
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AbstractIntegrated in the construction management activity, the concept of performance has been applied to address the suitability for use of attributes of a building, where each specific characteristic must be assessed in terms of satisfying or not the intended performance. Related to technical and functional performance, emphasis should be placed on the international standard ISO 19,208, which establishes regulatory requirements at the level of the methodological framework, and on the international standards ISO 15,928 and ISO 11,863, which establish regulatory requirements at the building’s level. Regarding the end users technical and functional requirements, the European standard EN 15,643 has relevant contributions and should be considered. Based on this normative framework, the present research work defines a methodology for assessing the technical and functional performance of buildings. Its performance specifications are framed, to satisfy end-users’ requirements and society's expectations. Buildings behaviour in the use phase (e.g., health, safety, convenience, comfort, property protection, contributions to sustainable development) and changes in its performance throughout its life cycle are considered. To apply the developed methodology, a case study composed of a set of existing public buildings in existing in Portugal is used. Thus, demonstrated that the assessment of the technical and functional performance of these buildings (based on the identification of anomalies at various levels), helps in the decision-making process of interventions to be carried out throughout their useful life’s. This methodology also contributes to long-term economic sustainability, as it optimizes the costs of using buildings, as well as future investments in rehabilitation interventions.
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Specialized Topics and Emerging Applications
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Frontmatter
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Infodynamic Analysis of Damage Detection on Bridges
Jorge Vieira, Miguel O. PanãoInfodynamic analysis presents a groundbreaking method for damage detection in bridges by utilizing information theory to identify structural changes. The chapter explores the application of this approach to raw acceleration data, which can detect outliers indicative of damage or environmental effects. The text delves into the concept of informature, a measure of uncertainty in a signal, and its potential to enhance structural health monitoring (SHM) systems. A detailed case study on the Z24 bridge illustrates the practical application of this method, showcasing its ability to detect structural changes and differentiate between environmental and damage effects. The chapter also discusses the potential of infodynamic analysis to optimize SHM systems, reduce data acquisition costs, and improve the accuracy of damage detection. Additionally, the text highlights the importance of benchmarking tests and progressive damage tests in developing reliable damage detection methodologies. The conclusion emphasizes the need for further research to explore the full potential of infodynamic analysis in SHM applications, including its integration with artificial intelligence and machine learning solutions.AI Generated
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AbstractThis study advances Structural Health Monitoring (SHM) by leveraging infodynamics, a novel approach that measures the information extracted from SHM raw data. Unlike conventional methods that rely on distances or statistical moments, this approach uses the information theory to interpret the stochastic nature of data as a key metric for quantifying informational content. Redundant data lack informational value, whereas stochasticity from external events necessitates real-time monitoring to identify structural health changes. A central question in SHM is determining when sufficient information is acquired for reliable damage assessment. This research explores Shannon’s formulation for measuring information, termed “informature,” which is sensitive to the entire data distribution and lays the foundation for new infodynamic strategies in SHM. To validate this approach, we applied informational analysis to dynamic data from the Z24 Bridge in Switzerland before its demolition, part of the Brite EuRam BE-3157 project “System Identification to Monitor Civil Engineering Structures” (SIMCES). The data included accelerometer readings that reflected the bridge’s response to the induced damage. The results demonstrate the effectiveness of “informature” in capturing sudden and gradual changes in the accelerometer data, highlighting the potential for real-time assessment of the correlation between “informature” and structural damage indicators, marking a significant advancement in SHM. -
Early Failure Detection in Secondary Cryogenic Pumps Through Machine Learning Techniques in the Context of Industry 4.0
Sonia Liñán García, Antonio de la Fuente Carmona, Javier Serra Parajes, Adolfo Crespo MárquezThis chapter delves into the critical role of secondary cryogenic pumps in the LNG regasification process and the challenges associated with their operation. It emphasizes the need for early failure detection to ensure safety and efficiency. The study focuses on developing a machine learning tool to identify anomalous pump operations before incidents occur, using historical data and advanced techniques like Random Forest and Deep Learning. The methodology involves data preprocessing, model selection, and validation, with a detailed comparison of different machine learning algorithms. The chapter also presents case studies of pumps in various operational scenarios, highlighting the differences in behavior before and after failures. It concludes with recommendations for operation and maintenance based on the analysis, demonstrating the potential of machine learning in predictive maintenance and the importance of continuous monitoring and data analysis.AI Generated
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AbstractThe paper presents a new approach to Asset Management that utilizes Industry 4.0 techniques and Artificial Intelligence (AI), specifically Machine Learning (ML). The research emphasizes early anomaly detection and failure mode classification in operating equipment in dynamic ranges. The study is based on the secondary cryogenic pumps of a Liquefied Natural Gas (LNG) regasification plant, which operate in a highly dynamic environment with rapid changes in pressure. Traditional condition monitoring methods, which rely on monitoring operational parameters and physical measurements, are contrasted with the proposed ML-based approach. ML models aim to detect early-stage issues and forecast critical failures by exploiting complex and nonlinear relationships among multiple operational variables. For the study, six years of historical data gathered by the different sensors of the 4 pumps have been used. -
Statistical Modelling of Digital Capture Data of Water Assets: Principal Components and Cluster Analyses of Water Tanks in Brazilian Municipalities
Wagner Oliveira de Carvalho, Nuno Marques de Almeida, Rui Cunha Marques, Marta Castilho GomesThis chapter explores the statistical modelling of digital capture data of water assets, focusing on principal components and cluster analyses of water tanks in Brazilian municipalities. The study is motivated by the need to enhance operational efficiency and service sustainability in water supply services, which manage extensive infrastructure assets. The research applies advanced statistical modelling methods to a sample of data from a Brazilian water utility, collected through innovative digital reality capture technologies. The methodology involves principal component analysis (PCA) and cluster analysis to characterize and group assets based on their quantitative and qualitative attributes. The study highlights the potential of digitalization to increase the reliability and quality of asset information, addressing challenges such as urban growth, natural resource limitations, and climate change. The results demonstrate the effectiveness of digitalization processes in improving asset management practices, offering opportunities for prioritizing resource allocation and making more informed decisions. The chapter concludes by emphasizing the benefits of investing in advanced data collection technologies and suggests future research directions, including the expansion of data analysis to other infrastructure assets and the exploration of AI algorithms for enhanced decision-making and risk management.AI Generated
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AbstractAccess to clean drinking water remains a global challenge, impacting 2 billion people. Additionally, the water distribution sector experiences substantial financial losses, estimated at $14 billion annually due to high water loss. The digital transformation heralded by Industry 4.0 promises significant enhancements in water service efficiency. The Digital Water program launched by IWA in 2022 highlights the several benefits of digitalization in operational efficiency and water resource management. This study investigates the application of digital reality capture technologies to improve asset information for water supply services. Using statistical modelling techniques of principal components and cluster analyses, this research analyzes a database comprising 2,584 physical assets from water tanks in Brazil. The data analysis, conducted using IBM SPSS and visualized in PowerBI, demonstrates the potential of these technologies as a robust decision-making support tool for the Asset Management System (AMS). -
The Synergy Between Lean Philosophy and Value Engineering for New Product Development
André Guimarães, Daniel Gaspar, Pedro Reis, Antonio J. Marques CardosoThis chapter delves into the powerful combination of Lean philosophy and Value Engineering (VE) to revolutionize new product development. It highlights how Lean principles, initially rooted in manufacturing, have expanded to optimize various processes, including product development, by minimizing waste and enhancing productivity. The text emphasizes the potential of Lean Product Development to expedite time-to-market, improve manufacturability, and elevate product quality. Additionally, it explores how Value Engineering can reduce costs, improve communication, and foster innovation. The study identifies three major themes: Lean Healthcare and Lean Performance, Customer Value Improvement, and Process and Product Optimization. It also discusses the integration of Lean and VE, proposing a hybrid methodology to address industry challenges and enhance customer value. The research uses a natural language processing model to analyze abstracts and identify key topics, providing a unique perspective on the synergy between these methodologies. The chapter concludes by suggesting future research directions and emphasizing the practical benefits of the Lean-VE hybrid approach in various industries.AI Generated
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AbstractOrganizations worldwide consistently strive to eliminate waste through approaches like Lean and adopting core functions through concepts such as Value Engineering (VE). Several sources affirm that integrating VE with Lean can enhance, streamline, and amplify efforts to implement Lean principles within an organization. Conversely, Lean can improve the effectiveness of VE initiatives. Based on a comprehensive literature review utilizing a natural language processing algorithm, this study focuses on understanding the intersection between these concepts. The review identified eight main sub-topics across three key areas: Lean Healthcare and Performance, Customer Value Improvement, and Process and Product Optimization. The practical implications of integrating Lean and VE in product development include faster time-to-market, enhanced manufacturability, and improved product quality while reducing start-up issues and development costs. This integration fosters a more collaborative environment, aligning teams with customer needs and expectations. The unique contribution of this study lies in demonstrating how Lean and VE, when applied together, form a holistic and robust framework that optimizes efficiency and ensures superior functionality and cost-effectiveness throughout the product development lifecycle.
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Business and Organizational Perspectives
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Frontmatter
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An Asset Management Approach to Beekeeping for Enhanced Pollination and Agriculture Sustainability
Azucena Marques, Miguel Vilas-Boas, Nuno Marques de AlmeidaThis chapter explores the application of asset management principles to beekeeping, focusing on enhancing pollination services and agricultural sustainability. The study identifies key areas such as disease control, hive monitoring, and beekeeping practices, which are crucial for the survival and productivity of honeybees. The research emphasizes the importance of strategic planning, decision-making, and risk management in maintaining healthy bee colonies. It also highlights the economic and environmental benefits of sustainable beekeeping, including increased crop yields and biodiversity conservation. The chapter concludes that adopting an asset management approach can significantly improve beekeeping practices, ensuring the survival of bees and enhancing agricultural productivity. By integrating these principles, beekeepers can better manage their hives, reduce losses, and increase the overall value of their operations.AI Generated
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AbstractIntensive agriculture has led to increasingly homogenous landscapes in Europe and North America, decreasing the biodiversity of rural areas. Plant-pollinator interactions are affected by crop diversification, flower availability, and environmental conditions. Pollinators are crucial in maintaining the ecosystem, responsible for about 70% of crop species worldwide and approximately 35% of total food production. The decline in insect pollination can result in significant economic losses, food shortages, and the extinction of several species. In the European Union, almost four-fifths of wildflowers and crops in temperate regions depend to some extent on insect pollination. Among more than 200,000 known species of pollinators, the honeybee Apis mellifera is one of the most efficient and essential pollinators. Bees can carry many pollen grains and are highly dependent on floral resources, making them necessary and effective service providers. Beekeeping offers invaluable benefits to agriculture by supporting crop production, thereby increasing the yields of pollinator-dependent crops. Beekeeping is a source of income for millions worldwide, and pollination services are worth over $215 billion annually. In the United Kingdom, Apis mellifera provides pollination for approximately 34% of commercial crops and plays a fundamental role in supporting biodiversity. In 2007, the value of pollination as a contribution to the UK agricultural market was £430 million. Honeybees provides different products such as honey, beeswax, pollen, honeydew, and propolis. The beekeeping activity contributes to biodiversity and forest conservation. Bees are a natural asset and a key to sustainability and life on earth; however, they are at risk, and action is needed to protect them and optimize the value they can derive from them. This paper discusses the opportunities for approaching this complex problem using asset management principles and techniques. -
Overview of the Use of Data Assets in the Context of Portuguese Companies: Comparison Between Micro, SMEs and Large Companies
André Guimarães, Daisy Enrique, Daniel Gaspar, Pedro Reis, Antonio J. Marques CardosoThis chapter delves into the strategic use of data assets within Portuguese companies, comparing the practices of micro, small, medium, and large enterprises. It examines the critical role of data in modern business operations, highlighting how companies leverage data for decision-making, cost reduction, and innovation. The study reveals significant differences in data management approaches across company sizes, with larger firms adopting more advanced technologies and security protocols. Smaller companies, while facing resource constraints, focus on basic data collection and quality management. The chapter also explores the impact of digital transformation initiatives, such as the Industry 4.0 Program, on the adoption of data-driven technologies. Through a survey of 390 Portuguese companies, the research identifies key trends and challenges in data asset management, offering insights into how companies can enhance their data strategies to stay competitive in a rapidly evolving digital landscape.AI Generated
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AbstractIn the digital age, data has become a crucial asset for businesses, serving as a valuable resource for production and decision-making. Effective data management is essential for enhancing companies’ competitiveness and operational efficiency. While big data holds the potential to drive innovation, particularly for small and medium-sized companies (SMEs), many companies struggle to leverage this digital capability effectively. This article examines the nuances of data management practices in Portugal, highlighting the differences between micro, small, medium, and large companies. To achieve this, data were collected from 390 Portuguese companies, and a Kruskal–Wallis Test was conducted to determine if significant differences exist in data utilization across company sizes. The results indicate substantial disparities in the use of data for developing new services and in the application of technologies for Data Storage Security, Security for Data Exchange with Partners, and Cloud Computing Security among micro, small, medium, and large companies. These findings underscore the importance of tailored strategies to improve data management practices and enhance the digital capabilities of companies of all sizes.
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- Title
- Case Studies in Digital Transformation
- Editors
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Adolfo Crespo Márquez
Turuna Seecharan
Georges Abdul-Nour
Joe Amadi-Echendu
Jay Lee
- Copyright Year
- 2026
- Publisher
- Springer Nature Switzerland
- Electronic ISBN
- 978-3-032-05592-7
- Print ISBN
- 978-3-032-05591-0
- DOI
- https://doi.org/10.1007/978-3-032-05592-7
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