Asset Management Practices Research Articles

Analysis of the Impact of Liquidity Policy and Economic Fluctuations on Asset and Wealth Management in the Banking Sector in Indonesia

This study analyzes the impact of liquidity policy and economic fluctuations on asset and wealth management in the Indonesian banking sector. Utilizing a quantitative approach, data were collected from 150 banking professionals through a structured questionnaire and analyzed using Structural Equation Modeling (SEM) with Partial Least Squares (PLS) version 3. The results demonstrate that economic fluctuations significantly impact both asset and wealth management, with a stronger influence on wealth management. Liquidity policy also plays a crucial role in improving asset and wealth management practices, though its impact is moderate compared to economic fluctuations. These findings suggest that banks must adopt robust liquidity management and risk mitigation strategies to navigate periods of economic uncertainty and optimize asset and wealth management practices. The study provides important insights for banking professionals and policymakers aiming to enhance financial stability and operational efficiency in the banking sector.

Rethinking Local Government Asset Management in Indonesia: Governance, Planning, and Policy Evaluation

Effective asset management is essential for enhancing operational efficiency and achieving sustainable development within local governments in Indonesia. This study explores key challenges in asset management, focusing on governance, planning, and policy evaluation. Several critical issues are identified: fragmented governance, insufficient transparency and coordination, outdated asset inventories, and misalignment between asset management practices and broader development goals. Policy evaluation mechanisms are also inadequate, failing to measure asset utilisation effectively. These challenges lead to inefficiencies in resource allocation, underutilisation of assets, and missed opportunities for local economic growth. The findings align with Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities), which promotes efficient resource use, and SDG 9 (Industry, Innovation, and Infrastructure), which supports enhanced infrastructure management. Addressing these goals enables local governments to align asset management strategies with broader sustainability objectives, fostering equitable growth and long-term development. Through a qualitative literature review, this study analyses data from government reports and prior research to highlight these challenges and propose solutions. An integrated approach—aligning governance frameworks, improving asset management planning, and strengthening policy evaluation—can overcome the identified barriers. By adopting this model, local governments can optimise asset management, leading to improved efficiency, better resource utilisation, and sustainable development. Future research should explore applying this framework across regions in Indonesia to validate its effectiveness.

Artificial intelligence in healthcare facilities asset information management: mixed review

Healthcare facilities are pivotal in ensuring continuous access to services, particularly for individuals with complex health conditions. Effective asset information management (AIM) in these facilities through artificial intelligence (AI) can enhance operational efficiency. The exploratory bibliometric and systematic review assesses the status and trends of AI applications in AIM within healthcare facilities. The findings reveal a significant gap between research findings and practical implementation, highlighting the need for further integration and real-world usage of AI-powered solutions in healthcare facilities settings. This study identified notable gaps, including the need for research on utilising AI to enhance asset management in healthcare, including maintenance scheduling and procurement processes. Involving stakeholders, such as healthcare professionals, facility managers, and patients, in effective facility management using AI requires further investigation. Research is needed to evaluate the economic benefits and develop robust ethical guidelines for responsible AI implementation. Notably, previous research has given limited attention to AI for healthcare AIM, with emerging trends focusing more on AI and infrastructure than the ‘asset’ aspect. Implementing AI-powered solutions tailored to the unique needs of healthcare facilities and evaluating cost-effectiveness will lead to improved asset management practices, enhanced decision-making processes, and, ultimately, more efficient and effective healthcare operations.

Village Asset Management Innovations Through Application-Based Systems

Background: Effective asset management in rural areas is vital for transparency and accountability in village governance. Specific Background: This study examines the management of village assets through the Village Asset Management System (SIPADES). Knowledge Gap: Despite its implementation, many villages struggle with systematic asset management, raising concerns about regulatory compliance and efficiency. Aims: The research aims to analyze current asset management practices using SIPADES and identify challenges faced by local administrators. Results: Findings reveal that while SIPADES offers a structured framework, issues with data collection and adherence to regulations hinder its effectiveness. Novelty: This study highlights the need for improved training for local operators in digital asset management. Implications: Enhancing administrators' capacity in SIPADES can lead to better asset management outcomes, promoting accountability in rural governance and aligning practices with national regulations, crucial for sustainable development. Highlights : Structured Framework: SIPADES provides a systematic approach to asset management in villages. Training Needs: Enhanced training for local administrators is essential for effective system utilization. Regulatory Alignment: Addressing compliance issues is crucial for promoting accountability and sustainable development. Keywords: asset management, rural governance, SIPADES, regulatory compliance, transparency

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.

Статус лидерства в управлении корпоративными ресурсами в цифровую эпоху в инвестиционных компаниях Китайской Народной Республики

The use of digital technologies in modern conditions is a prerequisite for success and efficiency in any field of activity. Achieving leadership status in corporate resource management in the digital age relies heavily on information innovations. In order to increase the competitiveness of investment firms managing assets, facilitate greater economic benefits, and ensure the rational use of resources for sustainable development in the digital era, top management must adapt to modern demands and leverage new technologies to boost corporate competitiveness. The stable development of these firms depends on various factors and can be achieved by adapting management concepts, business methods, and functionalities to market development needs. It is necessary to use digital technologies to change asset management practices, identify deficiencies in the allocation of resources at enterprises, as well as explore and improve promising areas of growth of the organization in order not to miss the opportunity to take leadership positions in the industry. The experience and data of «Heilongjiang Longrui Asset Management Co. are selected as an example., Ltd».

Innovative foresight for water utilities asset management using PRISM software

Our analysis aims to better understand water losses by developing a specific methodology for estimating water leak flow rates and durations. Additionally, it aims to improve asset management practices for drinking water utilities by forecasting the costs and benefits of investment and operational decisions on the network. Current utility practices are compared to a "do nothing" policy and to the potential effective asset management policies proposed by an artificial neural network (ANN) based software, PRISM. Applied to four utilities in France, our methodology provides valuable insights for enhancing water loss estimates and finding trade-off asset management policies.

Hotspot analysis for integrated multi-infrastructure asset management.

Urban infrastructure, important for societal functioning, faces challenges from aging assets and increasing service demands. Traditional asset management practices, often conducted in silos, fail to address the interconnected nature of these systems, leading to inefficiencies and heightened system failure risks. This article combines the spatial and temporal aspects of sewer, water, and road networks to facilitate integrated interventions and enable informed decision-making among diverse stakeholders. The outcome of this research is the creation of interactive hotspot maps on a unified platform, highlighting potential areas for integrated intervention across different infrastructures. To enhance the potential for collaboration in integrated interventions, flexibility in intervention planning was incorporated. With increased flexibility in intervention decisions, the potential for collaboration also increased. For the case study, introducing a 5-year intervention flexibility increased the number of collaborative projects between sewer, water, and roads from 0 to 18. The maps can also indicate areas where the application of trenchless technologies are justifiable. Other important information on asset characteristics for the decision-makers, including age, inspection, deterioration, and other relevant spatial and temporal details can also be obtained from the maps. The presented methodology and findings provide practical solution for utilities to manage urban infrastructure networks more efficiently.

Equity in Transportation Asset Management: A Proposed Framework

Transportation asset management has historically overlooked equity considerations. However, recently, there has been a significant increase in concerns about this issue, leading to a range of research and practices aimed at achieving more equitable outcomes. Yet, addressing equity is challenging and time-consuming, given its complexity and multifaceted nature. Several factors can significantly impact the outcome of an analysis, including the definition of equity, the evaluation and quantification of its impacts, and the community classification. As a result, there can be a wide range of interpretations of what constitutes equity. Therefore, there is no single correct or incorrect approach for equity evaluation, and different perspectives, impacts, and analysis methods could be considered for this purpose. This study reviews previous research on how transportation agencies are integrating equity into transportation asset management, particularly pavement management systems. The primary objective is to investigate important equity factors for pavement management and propose a prototype framework that integrates economic, environmental, and social equity considerations into the decision-making process for pavement maintenance, rehabilitation, and reconstruction projects. The proposed framework consists of two main steps: (1) defining objectives based on the three equity dimensions, and (2) analyzing key factors for identifying underserved areas through a case study approach. The case study analyzed pavement condition and sociodemographic data for California’s Bay Area. Statistical analysis and a machine learning method revealed that areas with higher poverty rates and worse air quality tend to have poorer pavement conditions, highlighting the need to consider these factors when defining underserved areas in Bay Area and promoting equity in pavement management decision-making. The proposed framework incorporates an optimization problem to simultaneously minimize disparities in pavement conditions between underserved and other areas, reduce greenhouse gas emissions from construction and traffic disruptions, and maximize overall network pavement condition subject to budget constraints. By incorporating all three equity aspects into a quantitative decision-support framework with specific objectives, this study proposes a novel approach for transportation agencies to promote sustainable and equitable asset management practices.

Data-Driven Reliability Prediction for District Heating Networks

As district heating networks age, current asset management practices, such as those relying on static life expectancies and age- and rule-based approaches, need to be replaced by data-driven asset management. As an alternative to physics-of-failure models that are typically preferred in the literature, this paper explores the application of more accessible traditional and novel machine learning-enabled reliability models for analyzing the reliability of district heating pipes and demonstrates how common data deficiencies can be accommodated by modifying the models’ likelihood expressions. The tested models comprised the Herz, Weibull, and the Neural Weibull Proportional Hazard models. An assessment of these models on data from an actual district heating network in Funen, Denmark showed that the relative youth of the network complicated the validation of the models’ distributional assumptions. However, a comparative evaluation of the models showed that there is a significant benefit in employing data-driven reliability modeling as they enable pipes to be differentiated based on the their working conditions and intrinsic features. Therefore, it is concluded that data-driven reliability models outperform current asset management practices such as age-based vulnerability ranking.

Automated traffic sign change detection using low-cost LiDAR scans and unsupervised machine learning

ABSTRACT Current practices in traffic sign monitoring heavily rely on manual inspections, a method that is both time-consuming and prone to human error. This leads to inefficiencies in the management and maintenance of these critical roadside assets. The objective of this work is to overcome these limitations by proposing a method for automated change detection in traffic signs using low-density LiDAR data. The proposed solution integrates noise elimination, point cloud restructuring, and cross-scan KD-tree generation, followed by the application of unsupervised machine learning techniques for change identification. The effectiveness of this method was verified by testing across three different highways with varying point cloud resolutions. For robust testing, an algorithm was also designed to simulate a broad range of different damage scenarios in traffic signs of different types, sizes, and placements. Testing in different scenarios along almost 15 km of the road revealed impressive results with accuracy and F1 score metrics ranging from 92% to 100%. Moreover, the algorithm was also extremely efficient with an average runtime of just 115” per km of fully automated unattended processing. The change detection potential of the proposed algorithm extends beyond traffic signs, as it could be adapted for many highway elements, enhancing the efficiency of transportation asset management and highway maintenance programmes. The findings indicate that this approach not only fills a significant gap in the current traffic sign monitoring and asset management practice but also offers a promising, comprehensive solution towards automated, cost-effective, and precise monitoring and maintenance of traffic signs, thus addressing a major challenge in this area.

Assessing Regional Asset Management Practices in The Provincial Government of West Sulawesi: Insights from APIP Supervision Based on Permendagri No.19/2016

Clear Description about regional wealth and basis for preparing financial report can be accomplished by good implementation of Regional Asset Management Practices. This research aim to assessing problem on regional asset management in the Provincial Government of West Sulawesi. The Research Method using quantitative analysis from data representation dan qualitative analysis from Permendagri No.19/2016. Conceptual analysis is carried out to data that show the weakness of asset management practices in The Provincial Government of West Sulawesi. These weaknesses include unoptimal of administration implementation by asset users and administrators, the potential loss of official vehicles that are still controlled by moved officials, not documented asset need in the RKBMD, unpaid vehicle taxes, the absence of decree for asset usage, and undocumented asset maintenance by regional asset maintenance cards.

Blockchain Revolution in Asset Management: A Comprehensive Analysis and Implementation Framework

A project that presents the harnesses blockchain technology to develop a Token Asset Management System (TAMS) aimed at revolutionizing conventional asset management practices. TAMS leverages the decentralized and transparent nature of blockchain to create digital tokens representing ownership or rights to various assets. These tokens enable fractional ownership, streamlined trading, and automated execution of smart contracts for dividends and voting. The project's primary objectives are to enhance efficiency, security, and transparency in asset management. By digitizing assets and recording transactions on an immutable ledger, TAMS reduces the need for intermediaries, minimizes administrative costs, and ensures auditability. Smart contracts automate key processes, improving accuracy and trust among participants. The project's implementation involves selecting an appropriate blockchain platform, designing token standards, creating an intuitive user interface, and adhering to regulatory requirements. The potential challenges of scalability and legal compliance are also considered. In summary, the Token Asset Management project exemplifies blockchain's potential to transform asset management. Through tokenization and blockchain's features, TAMS aims to establish a modernized ecosystem that offers efficient, secure, and transparent asset management solutions.

Modelling error chains in offshore wind energy systems: Examining the interplay of human performance and machine state

A dynamic model of the mutual interdependency between humans and machines in offshore wind farms (OWFs) is developed in this paper. The model emphasises the importance of having early indicators to dynamically moderate human behaviour and the need to account for both human and physical subsystems and their mutual interactions for optimal asset management practices. This work simulates three distinct scenarios for: (1) examining the interconnectedness of technical and human dynamics and their implications on error and failure; (2) assessing the impact of production loss on human and organisational behaviour; and (3) evaluating human error probability as an early warning sign of production loss. The findings suggest that adopting an appropriate maintenance culture and considering human error likelihood and production rate in decision-making leads to optimising production and mitigating the risks associated with human error. The research highlights the significance of comprehending the complex interactions between human and machine factors in the operation and maintenance (O&M) of offshore wind turbines (OWTs). The proposed dynamic model helps organisations identify the underlying causes of errors, allowing them to improve their maintenance strategies.

Asset management and risk based process safety management approaches to the energy trilemma

The global energy landscape is transforming towards a carbon-neutral future, necessitating strategic and systematic approaches to address the energy trilemma of security, affordability, and sustainability. Two critical elements of this transformation include the large-scale global adoption of hydrogen for energy use and carbon capture, usage, and storage (CCUS), including carbon sequestration. These efforts require effective management from a lifecycle perspective, considering net emissions and economics, including the life-cycle cost of energy systems, and a risk based process safety (RBPS) management approach that ensures public safety. Embracing asset management principles, which encompass lifecycle management, risk management, and performance optimisation, will be key in facilitating this transition. By analysing energy generation, demand patterns, and storage capacity, optimising the use of renewable energy, systematically, and dynamically monitoring asset performance, and process safety assurance metrics, operators can optimise resource allocation, reduce costs, and improve sustainability and public safety. Furthermore, adopting good risk engineering practices, including RBPS management, will manage risks to as low as reasonably practicable, ensuring a tolerable residual public risk level. This article highlights the correlation between the energy trilemma and asset management practices and suggests practical strategies to manage these major changes by implementing principles of asset management and RBPSmanagement, including a supporting case study.

IoT applications in asset management: A review of accounting and tracking techniques

In the digital age, the confluence of Internet of Things (IoT) technologies with asset management practices heralds a transformative era, promising unprecedented efficiency and strategic advantage. This paper delves into the intricacies of this integration, exploring the evolution, challenges, and potential of IoT in reshaping asset management paradigms. With a keen aim to elucidate the transformative impact of IoT and navigate the complexities of its integration into asset management systems, the study adopts a qualitative research methodology, underpinned by a systematic literature review. This approach enables a comprehensive exploration of the subject matter, ensuring a nuanced understanding of the IoT's potential and pitfalls within the asset management domain. The findings illuminate a landscape where IoT applications significantly enhance asset utilization rates, underscored by empirical success stories and quantifiable improvements in operational efficiency. However, the journey reveals barriers—technical, organizational, and cultural—that impede seamless IoT adoption. Addressing these challenges, the paper offers strategic recommendations, emphasizing the need for strategic alignment, inter-organisational collaboration, and robust data management strategies to overcome integration hurdles. Conclusively, the study not only achieves its aim of highlighting the potential of IoT in asset management but also charts a course for future endeavors in this field. It posits that the strategic integration of IoT technologies stands as a cornerstone for the next generation of asset management practices, promising a future marked by enhanced efficiency, economic viability, and strategic agility. The recommendations provided serve as a compass for organizations embarking on the IoT integration journey, guiding them towards a horizon of transformative potential.

The Influence of Business Analytics and Big Data on Predictive Maintenance and Asset Management

This study investigates the impact of business analytics and big data on predictive maintenance and asset management practices within the energy industry in Indonesia. A quantitative research approach, utilizing a survey methodology, was employed to gather data from stakeholders representing various sectors of the energy industry. The study analyzed the relationships between business analytics, big data, predictive maintenance, and asset management using structural equation modeling (SEM) with Partial Least Squares (PLS) regression. The results indicate significant positive relationships between the utilization of business analytics and big data and various performance metrics, including asset reliability, operational efficiency, and cost savings. Furthermore, organizational factors such as leadership support and data quality were found to play a crucial role in facilitating the adoption and implementation of predictive maintenance strategies. The findings underscore the transformative potential of data-driven maintenance strategies in enhancing operational efficiency, reducing downtime, and improving asset reliability within the Indonesian energy industry.

Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices.

Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun to shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles in order to improve maintenance processes. PdM technologies have the capacity to significantly improve profitability, safety, and sustainability in various industries. Significantly, precise equipment estimation, enabled by robust supervised learning techniques, is critical to the efficacy of PdM in conjunction with DT development. This study underscores the application of PdM and DT, exploring its transformative potential across domains demanding real-time monitoring. Specifically, it delves into emerging fields in healthcare, utilities (smart water management), and agriculture (smart farm), aligning with the latest research frontiers in these areas. Employing the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria, this study highlights diverse modeling techniques shaping asset lifetime evaluation within the PdM context from 34 scholarly articles. The study revealed four important findings: various PdM and DT modelling techniques, their diverse approaches, predictive outcomes, and implementation of maintenance management. These findings align with the ongoing exploration of emerging applications in healthcare, utilities (smart water management), and agriculture (smart farm). In addition, it sheds light on the critical functions of PdM and DT, emphasising their extraordinary ability to drive revolutionary change in dynamic industrial challenges. The results highlight these methodologies' flexibility and application across many industries, providing vital insights into their potential to revolutionise asset management and maintenance practice for real-time monitoring. Therefore, this systematic review provides a current and essential resource for academics, practitioners, and policymakers to refine PdM strategies and expand the applicability of DT in diverse industrial sectors.

Privacy-preserving culvert predictive models: A federated learning approach

In recent years, transportation agencies have shown a growing interest in the adoption of machine learning (ML) models as a means to improve the efficiency of their infrastructure asset management practices. Their limited inventory can, however, hinder the development of robust ML models. Moreover, transportation agencies may be reluctant to share their raw inventory data with others. To address these challenges, we employed a new ML paradigm, federated learning (FL), and demonstrated its potential in the case study of Utah culvert management. The Utah Department of Transportation (UDOT) only owned a limited culvert inventory, but we were able to obtain additional data from five other state DOTs’ inventories. Rather than employing a conventional centralized ML method, we opted for FL. This approach ensured that while UDOT did not directly access raw data from five other DOTs, it could still benefit from their combined insights. The FL-based model with 80.4 % accuracy showed a promising performance between the two centralized learning models developed based on the consolidated and the Utah-only datasets. The federated approach demonstrates that it is possible to develop high-performance ML models without compromising data confidentiality by centralizing data. This pioneering approach sets a precedent for other transportation agencies, suggesting they can navigate data scarcity and privacy concerns while reaping the benefits of collective knowledge.

Review of Asset Management Practice in Indonesian State-Owned Enterprise

This study examines Indonesian State-Owned Enterprises (SOEs) asset management methods, problems, and financial and operational performance effects. The report synthesizes case studies and academic research on how large organizations manage their huge and diverse asset portfolios and how governance, regulatory frameworks, and human resource practices affect their effectiveness. The research begins with case studies of talent management innovations from big Indonesian SOEs. These cases show how proactive human resource approaches can boost company commitment and reduce turnover, improving asset management efficiency. According to the research, governance and legal frameworks influence asset management techniques. Studies show that corporate governance quality affects SOE operational performance. The paper explores how reforms and legislation affect state asset management, highlighting the major changes in SOE governance and legal frameworks, particularly after economic and political reforms. Asset management difficulties for Indonesian SOEs include managing large and diverse asset portfolios, integrating modern management frameworks, and optimizing state asset revenue. According to the study, comprehensive asset management systems, governance transparency, and professional management can address these difficulties. The research examines how asset management strategies affect Indonesian SOE profitability, corporate governance, and performance measures. Strategic asset management boosts financial performance, especially profitability. SOE profitability is greatly affected by current asset and liability management. The study offers advice to Indonesian SOEs and policymakers. Enhancing financial and operational performance requires comprehensive asset management, governance changes, and strategic innovation. These efforts boost Indonesia's economy, demonstrating the importance of asset management in SOE performance.