Asset Life Cycle Research Articles

CHALLENGES IN THE OPERATION AND MAINTENANCE OF ASSETS AND FACILITIES IN ELDERLY CARE CENTRES IN MALAYSIA

Abreast with the rapid growth of older people every year, the number of elderly care centres has increased tremendously to cater to the demands of older people to spend their time after retirement in Malaysia. Effective operation and maintenance activities in an elderly care centre can optimise the service life of assets that indirectly lead to well-maintained facilities and provide the occupants with a safe, comfortable, and efficient living environment. Throughout the asset life cycle, operation and maintenance are the most extended phases, with various core activities that determine the financial health of an organisation. Nevertheless, this area remains relatively underexplored in the existing literature, particularly concerning the challenges posed by Malaysia's ageing population by 2030. Accordingly, this study seeks to investigate the issues of operation and maintenance of assets and facilities in elderly care centres. The method used to collect the data is through site observations and interviews in three elderly care centres, one in Perak and two in Selangor. These findings reveal three significant challenges the operators face: safety inside the accommodation, high operation and maintenance costs, and lack of a structured approach to managing the assets and facilities. Finally, the paper ends with implications of the findings that provide directions for future research.

Artificial intelligence for predictive maintenance in oil and gas operations

Oil and Gas companies are maximizing capabilities using AI in the area of predictive maintenance to create data-driven insights leading towards higher operational efficiencies and safety. AI-driven predictive maintenance means analyzing data collected from various sensors and equipment to predict when a machinery fault might occur. And this approach helps cut back on unanticipated downtimes and drops maintenance costs to increase the life cycle of key assets. AI driven systems with predictive analytics capabilities analyze equipment behavior for patterns, anomalies using AI techniques like machine learning algorithms, deep learning etc. Those methods enable detecting issues in early stages, so intervention can be done before the failure. The accuracy of learning models is enhanced by AI collaborating with IoT devices to provide real-time data and continuous monitoring.

Beyond distribution: The critical role of content tracking

In the dynamic landscape of digital asset management (DAM), managing the life cycle of digital assets extends beyond mere storage and retrieval. This paper emphasises the critical importance of content tracking for safeguarding brand integrity and compliance. It outlines the complexities involved in managing digital assets, from pre-production to postdistribution, highlighting the necessity of tracking usage rights and expiration dates across various platforms and geographies. The piece explores advanced technologies such as visual matching and rights management systems that use artificial intelligence to automate tracking and compliance analysis, thereby mitigating legal risks and enhancing operational efficiency. The paper discusses practical scenarios where content tracking is indispensable, such as managing expiring content, avoiding rights violations and maintaining brand consistency during rebranding efforts. It concludes that in a world where digital content predominates, a robust content tracking system integrated with a DAM system is essential for maintaining control over digital assets and ensuring their compliant and effective use. This not only protects the brand but also optimises content performance, ultimately contributing to the brand’s market success.

Optimizing IT Asset Lifecycle: A Comparative Analysis of Hardware Asset Management in ServiceNow and BMC Helix

Abstract: This research paper presents a comprehensive comparison of the Hardware Asset Management applications offered by ServiceNow and BMC Helix. The study examines key features, asset discovery and tracking capabilities, lifecycle management, integration with other ITSM processes, and reporting tools of both solutions. By evaluating their strengths and weaknesses in managing hardware assets, this paper aims to provide insights for organizations seeking to optimize their IT asset management practices and improve overall operational efficiency.

All along the asset life cycle: Research opportunities for operations and supply chain management

All along the asset life cycle: Research opportunities for operations and supply chain management

Digital twins in the asset life cycle: are we there yet?

An academic–industry team present the results of research conducted into the current benefits being derived from the use of digital twins across the entire life cycle of an asset. The team reviewed the benefits being derived by the asset-management community, as it is through this that through life productivity and effectiveness benefits will be realised. The team conducted a review of relevant literature, surveyed 122 practitioners and worked with their industry partner and a UK professional body to validate their conclusions. Specifically, (a) the authors examined the current application of digital twins in three industry sectors: process industries, infrastructure (civil engineering) and manufacturing. (b) The authors grouped the data according to role types – namely, professionals working predominantly in design and build and professionals in the operation and maintenance part of the life cycle. As regards the sector results, the responses were found to be contradictory. When controlling by role type, the results are more cohesive. Individuals involved in the operation and maintenance stages consistently indicated that digital twins are not delivering on their promise. The authors present a series of problem statements to define the current state, together with some managerial recommendations, and the authors propose further work to establish detailed guidance for infrastructure sector projects.

Advanced materials and deepwater asset life cycle management: A strategic approach for enhancing offshore oil and gas operations

Offshore oil and gas operations are inherently complex, requiring a strategic approach to asset life cycle management to ensure efficiency, safety, and environmental sustainability. This review explores the use of advanced materials in deepwater asset management, highlighting their role in enhancing operational performance and longevity. The review begins by discussing the challenges associated with deepwater operations, including harsh environmental conditions, high pressures, and corrosive fluids. These challenges necessitate the use of advanced materials that can withstand such conditions while maintaining structural integrity and operational efficiency. The review then outlines the strategic approach to asset life cycle management, emphasizing the importance of integrating advanced materials into design, construction, and maintenance processes. This approach includes the selection of materials based on their performance characteristics, compatibility with existing infrastructure, and cost-effectiveness. Furthermore, the review discusses the benefits of using advanced materials in deepwater asset management, including improved corrosion resistance, enhanced structural strength, and reduced maintenance requirements. These benefits translate into increased operational uptime, reduced downtime for repairs, and overall cost savings. Finally, the review concludes by highlighting the importance of collaboration between industry stakeholders, including operators, manufacturers, and research institutions, to drive innovation in advanced materials and deepwater asset management. By embracing a strategic approach and leveraging advanced materials, offshore oil and gas operations can achieve higher levels of efficiency, safety, and sustainability. Keywords: Advanced Materials, Deepwater Asset, Strategic Approach, Life Cycle Management, Oil and Gas Operations.

A Digital Framework for the Implementation of the Circular Economy in the Construction Sector: Expert Opinions

The construction sector plays a significant role in resource consumption and waste generation, making the transition to a circular economy (CE) imperative for sustainability in this sector. This paper focuses on developing a framework for implementing CE principles in the construction sector, guided by expert opinions and insights. The main objective of this study was to enhance existing theoretical frameworks by incorporating feedback from construction experts to improve practical utility and applicability in real-world construction practices. By addressing key areas for enhancement, the revised framework facilitates the adoption of CE practices throughout the asset lifecycle, considering buildings from cradle to cradle. This research’s methodology involved a comprehensive literature review to identify gaps in current frameworks related to CE implementation in the construction sector. Unstructured interviews with twenty construction experts were conducted to gather insights and recommendations for framework improvement. Data analysis highlighted the importance of integrating CE principles at various stages of the asset lifecycle, such as design, construction, and end-of-life phases. The key recommendations from experts include incorporating sustainable approaches, aligning with BIM models, and considering whole-life design aspects to promote circularity in the construction sector. By incorporating expert feedback and industry insights, the framework aims to guide practitioners toward more sustainable and resource-efficient construction practices, contributing to the broader field of CE implementation in the built environment.

BIM – CONSTRUCTION LIFECYCLE

Engineering, construction, and architecture have all been impacted by building information modeling, or BIM. The use of BIM technology during a building's operational phase is only now beginning to gain popularity among building owners looking for creative ways to increase the effectiveness of their facility operations. Lifespan The process of creating, storing, and using building data to manage operations and maintenance of buildings over the course of their operational lives is known as building information modeling, or BIM. Many facets of building operations that gain from improved data are of interest to facility managers. The goal is for stakeholders to produce and utilize consistent digital information at every stage of the asset's lifecycle. BIM Lifecycle is the process of creating, utilizing and maintaining the building information. BIM technology has the potential to significantly increase the efficiency of project maintenance as well as building design, production, construction, operation, and maintenance. Specifically, information management throughout the manufacturing stage optimizes the production process, and collaborative design using BIM technology improves design efficiency. Its data management function increases the effectiveness of building operations and maintenance, and its 3D visualization tool lowers construction errors and encourages efficiency in the constructing phase.

Digital transformation maturity assessment framework for large infrastructure asset owners

This research project developed a framework for assessing digital transformation (DT) maturity in large government-owned corporations (GOCs), focusing on integrating building information modelling (BIM), digital engineering (DE) practices, value of information, data integration aspects, and critical information management competencies, relating to asset management, asset delivery, and asset performance. Recognising DT’s strategic importance in asset lifecycle management, researchers created a practical DT maturity assessment tool applied to an Australian GOC. The framework and Excel assessment tool outline DT maturity across seven categories and 56 indicators, using the analytical hierarchy process (AHP) to rank indicators based on expert input. The tool enables straightforward data input and DT maturity evaluation, helping organisations understand their current DT status and set improvement targets. Workshops with DT professionals validated the tool’s effectiveness through a case study, offering insights into DT maturity levels and a roadmap for technological integration and data-driven decision-making. The research highlights DT’s role in enhancing efficiency, innovation, and safety, emphasising the need for strategic alignment with organisational objectives.

Technological innovations and optimized work methods in subsea maintenance and production

Subsea maintenance and production represent critical aspects of offshore operations, vital for sustaining energy production and ensuring operational efficiency. However, these endeavors face numerous challenges, including the complexities of deepwater environments, harsh weather conditions, and the high costs associated with traditional maintenance methods. To address these challenges, this paper explores the integration of technological innovations and optimized work methods in subsea operations. Technological innovations play a pivotal role in revolutionizing subsea maintenance and production. Remote monitoring and control systems enable real-time data collection and decision-making, enhancing operational visibility and efficiency. Autonomous underwater vehicles (AUVs) offer the capability to conduct inspections and repairs in remote and hazardous environments, reducing human intervention and associated risks. Robotics and automation further streamline maintenance tasks, improving accuracy and reducing downtime. Advanced materials and coatings enhance equipment durability and corrosion resistance, prolonging asset lifecycles and reducing maintenance requirements. In parallel, optimized work methods offer strategic approaches to enhance subsea operations' effectiveness. Predictive maintenance strategies leverage data analytics and machine learning to anticipate equipment failures, enabling proactive interventions and minimizing downtime. Condition-based monitoring facilitates real-time assessment of asset health, enabling timely maintenance interventions and cost savings. Integrated asset management systems provide holistic insights into asset performance and facilitate informed decision-making. Lean operations and continuous improvement methodologies further optimize workflows, driving operational excellence and cost efficiency. Through case studies and industry examples, this paper highlights the successful implementation of technological innovations and optimized work methods in subsea maintenance and production. Furthermore, it explores future trends, regulatory considerations, and the importance of industry collaborations in shaping the future of subsea operations. Ultimately, the integration of these approaches offers a pathway towards enhanced operational efficiency, reduced costs, and sustainable subsea operations. Keywords: Technological Innovations, Optimized Work Methods, Subsea Maintenance and Production.

Enhancing BIM implementation in Spanish public procurement: A framework approach

The European Union supports the use of technology to improve public procurement, acknowledging Building Information Modeling (BIM) as a catalyst for cost-effective public works and innovation. The purpose of this paper is to evaluate BIM implementation practices in Spanish public procurement. The methodology used in this study is a mixed research method involving a questionnaire survey and semi-structured expert interviews. The findings reveal market maturity as a barrier to BIM adoption in Spanish public procurement throughout the asset lifecycle. Selecting the right instrument for implementation proves challenging for public organizations. This study has contributed to the development of more practical and effective strategies to ensure full adoption of BIM within the public procurement sector of Spain. It proposes a framework approach for the pre-contractual phase, helping contracting authorities to make the optimal instrument selection. Two criteria are considered: the maturity of the public client and sector, and the economic value classification of tenders.

Data Management System - Water Supply and Channeling with GIS Mapping

GIS mapping enables visualizing spatial data related to water sources, distribution networks, infrastructure assets, and other pertinent information crucial for effective management. Asset management functionalities enable tracking the condition, maintenance schedules, and lifecycle of water infrastructure assets, ensuring optimal utilization and performance. This paper presents the design and implementation of an integrated data management system tailored for water supply and channeling, augmented with Geographic Information System (GIS) mapping capabilities. The system aims to streamline the management of water resources, optimize distribution networks, and enhance decision making processes for water utility providers and stakeholders. The system architecture comprises several key components: GIS mapping, data collection mechanisms, database management, sensor integration, network analysis tools, asset management functionalities, decision support systems, remote monitoring and control capabilities, and user interfaces. Data collection mechanisms encompass various sources such as sensors, surveys, satellite imagery, and existing databases to gather comprehensive datasets on water sources, quality parameters, infrastructure condition, and demographic factors. A robust database management system is employed to efficiently store, manage, and secure the collected data, facilitating easy retrieval and analysis. Real-time sensor data from water meters, flow meters, and pressure sensors are integrated into the system to provide insights into water usage patterns, network performance, and system health. Network analysis tools utilize GIS capabilities to optimize the layout of distribution networks, identify areas for expansion, and improve operational efficiency Remote monitoring and control capabilities empower operators to monitor and manage water systems from a centralized location, enabling proactive maintenance, leak detection, and emergency response. Security measures are implemented to safeguard sensitive water infrastructure data against unauthorized access, cyber threats, and data breaches, ensuring the integrity and confidentiality of the system. Overall, the integrated data management system presented herein offers a comprehensive solution for optimizing water supply and channeling operations, enhancing efficiency, reliability, and sustainability while minimizing costs and environmental impact

Practical Application of Digital Twins for Transportation Asset Data Management: Case Example of a Safety Hardware Asset

Digital Twins have emerged as a transformative technology with immense potential for enhancing asset data management in various industries. The transportation industry relies heavily on properly functioning and maintaining safety hardware assets, such as crash barriers, guiderails, and road signs, among others, to ensure the safety of road users. However, traditional asset data management practices for ancillary assets often fall short of providing real-time, comprehensive insights into the condition and performance of these critical assets. Conversely, Digital Twins can analyze data for predictive and preventive maintenance using cutting-edge technologies. This will enable asset managers to proactively address potential safety concerns, plan maintenance schedules efficiently, and optimize resource allocation. This can be achieved by leveraging historical asset data and real-time condition updates and providing the Digital Twin with an information-rich analytical model. The key steps to creating an information-rich analytical model are identifying the asset data that should be collected, understanding data architecture throughout the asset lifecycle, and generating asset data governance and stewardship frameworks. As such, this paper investigates the current state of practice for ancillary asset data collection and management and investigates the generation of information-rich analytical models for guiderails, a critical roadway safety hardware. Moreover, this study demonstrates how Digital Twins can be integrated and affect the development of transportation asset management systems. The findings of this article emphasize the significance of adopting Digital Twins in transportation asset data management. The case example showcases a compelling roadmap for other transportation agencies to consider integrating digital twins into their asset management strategies.

Asset Information Model Management-Based GIS/BIM Integration in Facility Management Contract

Achieving efficiency success status inside an organization’s built environment and obtaining a positive return on investments need robust and comprehensive asset management and maintenance processes based on the efficiency of contract information documents within the built asset lifecycle. This paper aims to highlight the appropriate interactive approach for construction projects to build the information flow scope of asset facility management contracts based on GIS (Geographical Information System) and BIM (Building Information Modeling) integration processes and sustainability standards, and project as-built contractual documents to support owners and stakeholders with the intent of improving asset management processes. Expert interviews and contract information flow types in several facility management processes conducted in both local and international facility management organizations were used to assist the information flow scope method. The study classified and built significant integrated information and data flow models for a case study to serve as contract guidelines, including efficiency performance measures and indicators for monitoring procedures, technical evaluation, and financial issues in order to provide high-performance service quality in facility management applications.

Impact factor analysis affecting the operation of asset lifecycle by using asset information modeling and blockchain technology

Purpose Currently, owners find it difficult to manage their assets throughout their project life cycle. The fact that asset information models (AIMs) are mandatory as deliverables for building information modeling-driven projects makes it a key requirement for the client to understand in detail those factors affecting asset operation. Hence, because the Kingdom of Saudi Arabia is the most significant market in the Middle East, this study aims to investigate those factors where blockchain and AIMs could impact the asset management (AM) life cycle. Design/methodology/approach Researchers used a hypothesis-based approach over a systematic literature review and a workshop (descriptive statistics) to understand the current challenges in AM. Later, a second workshop was run to understand the impact factor analysis affecting the operation of the asset life cycle by using asset information modeling and blockchain technology over a multiquantitative method. Findings Results found that factors affecting the operation of assets could be the improvement of trust and stakeholder’s influences; the availability of handover process products’ accurate data; manufacturers providing detailed product models; increasing the speed of preparing holistic and integrated AM systems; improving collaboration between stakeholders; and returning clients’ investments faster. Originality/value Understanding the factors affecting AM life cycle based on the utilization of AIMs and blockchain then allows investors and their team members to work in a secure and collaborative environment that helps them to pre-identify certain risks and improve decision-making in a more effective way, as is required by ISO55000.

Maintenance lifecycle cost analysis through Agent-Based Simulation

Proper maintenance management allows companies to reduce failures and breakdowns while keeping high asset productivity. Several variables and uncertainties affect the selection of the optimal maintenance policy, and the one that might seem more cost effective in the short-term, might reveal having a total higher cost over the complete asset lifecycle. In this sense, the use of simulation to quantify the maintenance costs over the entire asset lifecycle can be helpful, allowing companies to test different scenarios and compare their result in a dynamic way, looking at long-term effects instead of short-term ones. The paper proposes an agent-based simulation model that allows to quantify the total costs associated with different maintenance policies, as well as computing productivity and important maintenance indexes to help companies in evaluating the most suitable maintenance strategy, being it used internally, or as a selling point for maintenance services towards customers.

Information process-system modelling for Circular Economy of manufacturing systems

Recent push towards sustainable development have lead machine tool builders to begin adopting novel business models which are usually peculiar in product-service-systems. In particular, business models as ‘pay-per-use’ introduced the issue of rental of production capacity integrated with the management of the life cycle of assets in a Circular Economy perspective. However, when it comes to machine tool builders, the high degree of customization of capital goods and in particular of multi-stage automated assembly systems that are strongly linked to the characteristics of the final product make this solution more difficult. Hence, the implementation of such business models represents a clear challenge for machine tool builders and manufacturing system suppliers from technological perspective. This work discusses preliminary results in the technological implications of such strategy. A real industrial case related to cross-sector multi-stage automated assembly systems is presented to highlight the technological challenges and potential research directions.

Assessing the prospects of distributed ledger technology (DLT) development as an element of digital ecosystem of railway transport

Purpose: to evaluate the practice of implementing projects for digitalization of the life cycle management processes of railway infrastructure facilities based on distributed ledger technologies (DLT). Methods: general scientific methods such as observation, analysis, comparison, generalization and analogy were used. As a result of the conducted research: the main characteristics of the distributed ledger technologies, which determine the parameters and scope of their use in life cycle management of railway transport facilities, have been systematized; the effects of the implementation DLT as an element of the digital railway transport ecosystem have been identified; recommendations for the implementation and rollout of design solutions using DLT in the railway transport area have been developed. Conclusion: as a result of the study, recommendations for the implementation and rollout of the solutions using DLT have been developed. The findings and conclusions may be useful for railway companies, government agencies and other participants in the railway transport and logistics market when planning and implementing projects for optimizing asset life cycle management.

Deriving inferences through natural language from structured datasets for asset lifecycle management

Industry 4.0 is expected to revolutionize the manufacturing world in terms of improved decision-making leading to increased value extraction from assets. Yet, since its inception, its adoption seems to have not scaled with time as expected. One of the bottlenecks can be due to the knowledge disparity between the Industry 4.0 technology developer and the domain expert/end user, which can lead to higher resources, cost, and time requirements for scalable adoption. Modern day Large Language Models (LLMs) have the ability to process large datasets and can serve as an intermediatory tool for domain end-users. Thus, in this paper, an Industrial-GPT tool is developed to translate natural language queries into meaningful inferences about asset-related data. The tool is evaluated using an exemplary dataset, representing a manufacturing industry with four production lines, multiple assets, readings and inferences from multiple sensors. The experimental results show that reasonable insight may be extracted from the dataset, using prompt engineering, while fine-tuning the model to adapt to business logic can help achieve better and faster inferences. Future research directions are proposed that can be addressed while developing Industrial-GPT-based tools and reducing the development cycle for Industry 4.0 technologies.