Asset Management Plan Research Articles

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 Several critical issues are identified: fragmented governance, insufficient transparency and coordination, outdated asset inventories, and misalignment between asset management practices and broader development goals.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.

A performance-driven framework with a system-of-systems approach for augmented asset management of railway system

The railway system is a complex technical system-of-systems (SoS). To address the complexity of the railway system, a holistic approach is needed that facilitates the development of an appropriate asset management regime. A systems-of-systems (SoS) approach considers the complex nature of the railway system, comprising interconnected subsystems like rolling stock and infrastructure. Neglecting these interdependencies risks sub-optimization of the overall system performance. Asset management of the railway system utilising a SoS approach ensures the focus of asset management on overall system requirements. The efficiency and effectiveness of the railway system is based on aspects such as availability, reliability, and safety performance. To enhance these aspects, monitoring, and improvement of key performance indicators (KPIs) emphasizing increased capacity and reduced operational costs is essential. The KPIs offer quantifiable parameters for performance optimization. Augmenting asset management through data-driven technologies can improve the efficiency and effectiveness of asset management. However, challenges persist in the implementation of data-driven solutions due to the railway system’s complexity and lack of a holistic perspective. A systematic performance-driven framework with a system-of-systems approach for augmented asset management of railway system provides handrail for the utilisation of data-driven technologies with railway system requirements at the centre while developing an asset management regime. The proposed framework aims to establish a clear relationship between system KPIs, and the performance of sub-systems and components aiding railway organizations in asset management design and implementation. This paper explains the important components of the proposed framework and demonstrates the application the framework for asset management and maintenance planning of high value components in the fleet of railway rolling stock. Adoption of the proposed framework is expected to enhance asset management through development and implementation of data-driven solutions that are aligned with system KPIs, to support asset management decision making.

Ensemble Machine Learning Classification Models for Predicting Pavement Condition

Forecasting pavement performance condition is essential within the pavement management system to optimize decisions with regard to planning maintenance and rehabilitation projects. Accurate forecasts facilitate timely interventions and assist in formulating cost-effective asset management plans. Data-driven machine learning models that utilize historical data to improve forecasting precision have gained attention in the field of asset management. Although numerous studies have employed regression-based models to forecast pavement condition, transportation asset management often operates according to condition index ranges rather than exact values. Therefore, classification models are suitable for predicting pavement condition grades and determining the appropriate maintenance type for pavement assets. This research focuses on developing five machine learning classification models to predict pavement condition: random forest; gradient boost; support vector machine; k-nearest neighbors; and artificial neural network. To enhance prediction performance, these models are integrated using ensemble methods, including voting and stacking. The classification models are developed using a dataset from the Georgia Department of Transportation that documented the condition of asphalt pavements for predefined maintenance sections between 2017 and 2021. A voting ensemble model constructed with the two best-performing individual classification models reached the highest accuracy rate at 83%. Although the performance of individual models fluctuates, ensemble models consistently produce a top-tier performance regardless of the variations in data sampling. Therefore, ensemble methods are recommended for developing pavement condition prediction models to improve accuracy and achieve a more consistent quality of predictions. The findings of this research will provide transportation agencies with information to help them strengthen their forecasting practices in relation to pavement condition, thereby improving their maintenance planning and cost savings.

Smart Strategies for Managing Regional Assets: Best Practices in Increase Local Revenue

This study aims to analyze the optimization of asset management consisting of the process of planning, implementing, and supervising regional property. The research method uses a qualitative approach with a phenomenological orientation. The data obtained from the study were analyzed using interactive models. The results showed weaknesses in the stages of planning, implementation, and supervision of assets. Therefore, the strategy to increase regional original revenue (PAD) through the optimization of regional property (BMD), the Gorontalo Provincial Government can take a series of innovation-oriented actions including: 1) A comprehensive asset audit of all assets owned including land, buildings, facilities, and inventory. This audit should include an assessment of the value, condition, and potential reuse. 2) Development of an asset management plan. 3) Involving private parties and communities to identify opportunities for cooperation in asset management. 4) Utilization of software technology to help monitor inventory, maintenance, and sustainability of assets. 5) Data-driven asset management by identifying new trends, market needs, and revenue potential to aid smarter decision-making processes related to asset utilization. 6) Promotion and marketing of assets that can be carried out by regional apparatus organizations (OPD) if they have facilities that can be leased or used commercially to help increase revenue. 7) Improved policies and regulations including procedures for leasing, maintaining, and reusing assets, as clear and flexible policies can support innovation and better asset utilization. The weakness in this study is mainly localisation, which is related to areas that predominantly prioritize local cultural values as the main foundation in their interaction with the surrounding environment. This is due to limitations in consideration of cultural variations that may exist outside the scope of those regions in this study. Therefore, the results of this study require careful consideration when applied in areas that have different cultural diversity or backgrounds.
 Keywords: goverment entities, asymmetric behavior, stabilization

RESEARCH ON ASSET MANAGEMENT IN PRIVATE UNIVERSITIES IN CHINA (THE CASE STUDY OF THE YUNNAN COLLEGE OF BUSINESS MANAGEMENT)

The purpose of this study is to examine asset management practices in Chinese private universities using Yunnan College of Business Management as a case study. The study draws on four theoretical frameworks: legitimacy theory, stakeholder theory, agency theory, and resource-based view theory. Qualitative research methods, including interviews and document analysis, are used to collect data from relevant stakeholders, such as administrators, faculty, and students. The study finds that while Yunnan College of Business Management has implemented various asset management strategies, there are still opportunities for improvement in areas such as asset utilization and maintenance. The results also suggest that the university should consider the interests of various stakeholders, including students, faculty, and donors, to improve the legitimacy and sustainability of its asset management practices. Based on these findings, this study recommends that the university develop a comprehensive asset management plan that is consistent with its mission and values, adopts advanced technology for asset tracking and maintenance, and establishes a transparent and accountable asset management system. This study contributes to the literature on asset management in the higher education sector and provides practical recommendations for improving asset management practices in Chinese private universities.

Measuring the Performance of a Strategic Asset Management Plan through a Balanced Scorecard

The purpose of this paper is to propose a tool to measure the performance of a Strategic Asset Management Plan (SAMP) based on a Balanced Scorecard (BSC). The SAMP converts organizational objectives into asset management objectives, as well as specifies the role of the asset management system, providing support to achieve asset management objectives. The SAMP becomes the heart of the organization and integrates the long-term, medium-term, and short-term plans. In the SAMP, the balance among performance, costs, and risks are taken into consideration in order to achieve the organization’s objectives. On the other hand, the SAMP is a guide to set the asset management objectives while describing the role of the Asset Management System (AMS) in meeting these objectives. Since the SAMP is the central figure of AMS, it is important to measure its performance and should be built and improved through an iterative process. This indicates that it is not just a document, it is “the document” that should be treated as a “living being”, which needs to adapt to internal and external changes quickly. The BSC is an excellent tool where, through the appropriate Key Performance Indicators (KPIs), the progress can be measured, and is supported by four perspectives: Financial, Customer, Internal Business Process, and Learning and Growth.

Assessing social equity considerations within transportation asset management

The impacts of transportation systems on people and communities are substantial but challenging to quantify. It requires significant effort and attention for transportation agencies to improve how social concepts such as equity are effectively incorporated into transportation asset management (TAM) plans and practices. This study, therefore, looked at TAM plans and practices to assess the current application of equity and suggested ways to improve equity incorporation into TAM at the state level. A content analysis was done on the 2019 TAM plans from all U.S. states, D.C., and Puerto Rico, and the results showed that equity-related words were rarely mentioned. This indicated that TAM is one of the sectors where equity is still marginalized, and there is a strong need to improve the incorporation of equity into TAM. The study then identified several equity definitions and measures that fit the context of pavement management and, as a result, TAM. Pavement management was used for demonstration purposes as pavements are a major type of asset managed by all state Departments of Transportation (DOTs). Definitions and measures were identified by reviewing documents from the USDOT and FHWA and three sustainability rating systems widely used in transportation: Envision, Greenroads, and INVEST. In addition to these definitions and measures, this study discussed several available guides and tools that would help agencies bring equity considerations into TAM, specifically at the state level. The results indicated that although some resources provide equity definitions and measures that can be used in TAM, more work is needed to develop them to match the practices and approaches used in this sector.

Transportation Asset Management Decision Support Tools: Computational Complexity, Transparency, and Realism

Asset management decision support tools determine which action (maintenance, rehabilitation, or reconstruction) is applied to each facility in a transportation network and when. Sophisticated tools recognize uncertainties and consider emerging priorities. However, these tools are often computationally complex and lack transparency, the models are difficult to evaluate, and the outputs are challenging to validate. This paper explores computational complexity, transparency, and realism in transportation asset management decision support tools to better understand how to select the right tools for a particular context. Descriptions of how state departments of transportation in the United States make use of optimization in their mandated transportation asset management plans to make decisions are used to understand the needs of states. This qualitative analysis serves as a review of the goals and practices of state agencies. An existing asset management tool is then used to demonstrate the tradeoffs involved in accurately capturing the decision-making process and complexity. The results provide examples of strategies that agencies can use when selecting decision support tools and for researchers and tool developers working toward developing the right tool for an application.

DENSE SENSING ON ROAD BRIDGES NETWORK: NEW APPROACH TO DATA‐INFORMED ASSESSMENT

AbstractHazards, ageing, damaging, and traffic loads increase are some of the growing risks that bridge infrastructures in Europe face throughout their design life. Key issues of the asset management plan are the safety assessment of existing structures, the prioritization of the interventions and the optimization of the maintenance schedules. As such, to enable appropriate diagnostics and cost‐effective decisions on assessment/repairs, timely and accurate information from monitoring, inspection and testing is essential. This paper outlines the strategic role of SHM systems to identify the actual condition and future performance of the infrastructures, supporting an effective maintenance plan to meet the target reliability requirements. This contribution describes the use of dense sensing on an Italian bridges network of over 100 bridges, to account for monitoring in safety assessment, facilitating the application of proactive maintenance strategies through a deeper knowledge of the structural behaviour over time. Multiple case studies are detailed, where MEMS sensors have been installed with the purpose of monitoring the evolution of deterioration process, supporting a data‐informed structural diagnostics as well as providing evidence of the effectiveness of strengthening interventions.

Multi-Sensor and Multi-Scale Remote Sensing Approach for Assessing Slope Instability along Transportation Corridors Using Satellites and Uncrewed Aircraft Systems

Rapid slope instabilities (i.e., rockfalls) involving highway networks in mountainous areas pose a threat to facilities, settlements and life, thus representing a challenge for asset management plans. To identify different morphological expressions of degradation processes that lead to rock mass destabilization, we combined satellite and uncrewed aircraft system (UAS)-based products over two study sites along the State Highway 133 sector near Paonia Reservoir, Colorado (USA). Along with a PS-InSAR analysis covering the 2017–2021 interval, a high-resolution dataset composed of optical, thermal and multi-spectral imagery was systematically acquired during two UAS surveys in September 2021 and June 2022. After a pre-processing step including georeferencing and orthorectification, the final products were processed through object-based multispectral classification and change detection analysis for highlighting moisture or lithological variations and for identifying areas more susceptible to deterioration and detachments at the small and micro-scale. The PS-InSAR analysis, on the other hand, provided multi-temporal information at the catchment scale and assisted in understanding the large-scale morpho-evolution of the displacements. This synergic combination offered a multiscale perspective of the superimposed imprints of denudation and mass-wasting processes occurring on the study site, leading to the detection of evidence and/or early precursors of rock collapses, and effectively supporting asset management maintenance practices.

Modelling principal-agent dilemma for management of resilience in interdependent infrastructure systems

Temporal dilemmas are observed between the short-term incentives of the asset managers, as agents, versus the long-term resilience driven goals of the organisations, who own and operate the infrastructure systems as principals. That is, the incentive mechanisms designed based on the short-term tenure of an asset manager may structurally deprive the infrastructure owner/operator organisations from directing resources towards their long-term resilience driven goals. Lack of long-term incentives by asset managers adds another layer to managment of resilience in the context of infrastructure systems in their life cycle. We proposed a framework for integrating resilience into asset management of infrastructure systems and modelled it to explore the impact of such temporal dilemmas in principal-agent relations and potential strategic responses. The model reflects the interdependency of the sub-systems of infrastructures to assess each sub-system's periodic coupled resilience. The proposed integrated simulation reflects coupled impacts of shocks and stressors within regular asset management regimes. The simulation results provide quantitative evidence of how managerial and political dimensions can impact management of resilience in the context of infrastructure systems and open another discourse towards considering the complexities of organising for resilience in socio-technical systems.

Asset Management in the Waste Treatment Environment

Abstract The main aim of the research study was to determine what drives the asset management inadequacy in the waste-water treatment utilities, as to ascertain the main drivers of the equipment failures are, and whether the maintenance strategies respond or are adequate to deal with the failure rate of equipment in the waste-water treatment utilities. The environmental impact driven from the non-compliant final effluent discharged into the water resources in the country resulted from the equipment failures in the plant. The maintenance strategies, asset policy, asset management plan, including the rehabilitation programme and all statutory regulation compliance are evaluated and analysed to ensure they are responding or aligned to the growing challenges faced by the waste-water treatment environment. Using the quantitative approach, the three groups; the asset management team, quality and process team and operations team of participants were to be selected. A convenient approved sampling method, the survey questionnaire was electronically shared to respond to the Covid-19 restriction and the structure telephonic interview so ascertain the level of understanding of the current asset management strategies, policies and procedures, the equipment failures impact to the utilities compliance and the further corporate risk and environmental impact from inadequately manages waste-water treatment utilities. The findings confirms that there is indeed lack of the synergies in the waste treatment environment emanating from a few aspects i.e., lack of due diligent in the application of maintenance strategies in the waste treatment works, lack of in-depth understanding of the impact from equipment failures in the waste treatment process, and the misalignment of the rehabilitation program, leading to gross environmental impact and corporate risk. It was further recommended the collaborations in the maintenance strategies development and review process. The rehabilitation programme should be aligned to the requirements of the process.

Improvement of maintenance and asset management strategy in a maritime terminal under regulatory and physicochemical testing criteria

This work was developed in the gas and oil sector, more specifically in the equipment that make up the Coveñas maritime terminal, one of the main oil terminals in Colombia. This work sought to improve the maintenance strategy and asset management of all terminal equipment through criteria based on international standards such as International Organization for Standardization 14224, which guides the ranking of equipment; also considered were the results of physicochemical tests that were applied to sample materials obtained from the equipment in the field. With this information, a road map was drawn up to establish a maintenance and asset management plan to improve the efficiency of the marine terminal’s operation. The physical variables measured in this equipment and its parts, evidenced the importance of carrying out projects with interdisciplinary approaches, to enhance the achievement of the objectives set. In this specific case, a considerable improvement in maintenance work execution hours was shown.

Evaluation of Flood-Vulnerable Pavement Network in Support of Resilience in Pavement System Management

Transportation systems play a pivotal role in the nation’s economic and societal development. Transportation assets, such as pavements and bridges, are inevitably subject to the effects of climate and environment. These stressors mainly include flood, precipitation, heat, wildfire, and wind. Specifically, more extreme weather events, such as hurricanes and snowstorms, have occurred in recent years. This requires stakeholders to better prepare transportation infrastructure resilience in planning, design, construction, and management. Its importance is manifested through nationwide policy and state-level practice. For example, in Bipartisan Infrastructure Law, the FHWA directs all state DOTs to incorporate resilience in their transportation asset management plans. A critical element in resilience is to evaluate the effect of climatic or environmental stressors on facilities’ performance. This study examines the impact of flood on the TxDOT-managed pavement network. It proposes a method to evaluate the flood-vulnerable pavement network in the context of resilience. First, the GIS tool is used to overlap a 100-year frequency flood with the road network to identify weak pavement sections subject to flood risk. Second, a simulation is run to determine sections affected by flood over a 10-year analysis horizon. Then, different deterioration models are used to predict the network-level pavement performance, to reflect (1) normal deterioration without flood, (2) optimistic accelerated deterioration under flooding, and (3) pessimistic accelerated deterioration under flooding. It is found that the network-level pavement will experience varying levels of performance reduction, owing to a 100-year flood impact. The quantified performance change can serve to enhance infrastructure resilience preparation for more reliable pavement system management.

A BIA-Based Quantitative Framework for Built Physical Asset Criticality Analysis under Sustainability and Resilience

Asset-intensive industries, such as the construction industry, have experienced major catastrophes that have led to significant operational disruptions. Physical asset failure has been the primary cause of these disruptions. Therefore, implementing proper asset management plans, including continuity plans, is crucial for the business continuity of companies active in these industries. However, companies often face severe resource limitations when implementing these plans for all of their physical assets. Therefore, those critical physical assets that are vital for providing their key products should be identified. Moreover, sustainability and resilience are inseparable parts of organizations’ strategies, including strategic asset management plans. Therefore, any proposed ranking methodology for physical asset prioritization should encompass sustainability and resilience measures to ensure its practicality. This paper proposes a novel framework for physical asset criticality analysis based on the so-called business impact analysis to ensure the continuity of providing products/services through the continuity of physical assets. A hybrid fuzzy BWM-TOPSIS method is first applied to identify the key products. Then, a hybrid fuzzy DEMATEL-Bayesian network is applied based on proper sustainability and resilience factors to determine the critical physical assets, while interdependencies among these factors are well captured. The normalized expected asset criticality index is defined to guide managers in taking appropriate directions while developing asset management plans. A case study of a gas company is provided to show the applicability of the proposed decision model. The data needed for each step of the framework is gathered through experts’ judgments, historical data available on the sites, or quantitative risk assessment scenarios.

Network Deterioration Prediction for Reinforced Concrete Pipe and Box Culverts Using Markov Model: Case Study

Reinforced concrete (RC) pipe and box culverts are widely used as an alternative to bridge structures in road transport networks around the world. The deterioration of the RC culverts is a complex problem caused by combined humanmade and natural processes with various influential factors. Visual inspection is often used to monitor the deterioration of culverts, and the inspection results are used to rate condition of culverts by using a discrete condition rating system. The objective of this case study was to investigate the deterioration of RC culverts at the network and cohort levels by using a Markov model and culverts’ influential factors and inspected condition data. The Markov deterioration model can forecast the future deterioration of a culvert network, which can be used for asset management planning of the culvert network. A real case study with a regional local government in Australia was used to demonstrate the application of this study. The results of network deterioration modeling showed that the deterioration rates of culverts varied with culvert type (pipe and box culvert), built year, demographic location, and pipe size. However, annual average daily traffic (AADT) affected only box culverts. Deterioration prediction was found to be sensitive to the time length of evidence data, which highlights the importance of keeping records of maintenance and rehabilitation activities for producing accurate modeling data.

Energy forecasting to benchmark for federal net-zero objectives under climate uncertainty

Climate variability creates energy demand uncertainty and complicates long-term asset management and budget planning. Without understanding future energy demand trends related to intensification of climate, changes to energy consumption could result in budget escalation. Energy demand trends can inform campus infrastructure repair and modernization plans, effective energy use reduction policies, or renewable energy resource implementation decisions, all of which are targeted at mitigating energy cost escalation and variability. To make these long-term management decisions, energy managers require unbiased and accurate energy use forecasts. This research uses a statistical, model-based forecast framework, calibrated retrospectively with open-source climate data, and run in a forecast mode with CMIP5 projections of temperature for RCPs 4.5 and 8.5 to predict total daily energy consumption and costs for a campus-sized community (population: 30 000) through the end of the century. The case study of Wright Patterson Air Force Base is contextualized within the existing executive orders directing net-zero emissions and carbon-free electricity benchmarks for the federal government. The model suggests that median annual campus electric consumption, based on temperature rise alone, could increase by 4.8% with RCP4.5 and 19.3% with RCP8.5 by the end of the century, with a current carbon footprint of 547 million kg CO2e. Monthly forecasts indicate that summer month energy consumption could significantly increase within the first decade (2020–2030), and nearly all months will experience significant increases by the end of the century. Therefore, careful planning is needed to meet net-zero emissions targets with significant increases in electricity demands under current conditions. Policies and projects to reduce the carbon footprint of federal agencies need to incorporate forecasting models to understand changes in demand to appropriately size electric infrastructure.

Condition Assessment Planning: Where to Start and Where to Take It

Utilities should select condition assessment approaches that align with their budgets and goals to target priority assets and develop effective asset management plans.

The Implementation of Hibah Ruqba in Malaysia Asset Management Planning Industry in Aordane to Maqasid Shariah

The Implementation of Hibah Ruqba in Malaysia Asset Management Planning Industry in Aordane to Maqasid Shariah

Incorporation of National Pavement Performance Measures Into Decision-Making Process

State highway agencies (SHAs) are required by Federal Highway Administration (FHWA) to develop Transportation Asset Management Plans (TAMP) in which the performance targets for roadway assets are determined. FHWA also developed national performance metrics to use in determining performance targets for pavements. However, for pavement assets, most SHAs employ their own performance metrics for pavement condition data reporting and performing maintenance and rehabilitation (M&R) analysis. Therefore, there is a need to incorporate national performance measures into SHAs’ pavement management system to support the development of TAMP. In this study, new pavement performance metrics were proposed to assist SHAs in conducting M&R analysis. The relationships between the new metrics and existing pavement metrics used in the State of Tennessee were investigated. Comparison between the new metrics and the State’s metrics was made with reference to network-level condition summary and performance prediction models. Results indicated that the new performance metrics appeared to be stable and less sensitive to sectioning method. Similar time-series patterns of network-level performance deterioration were found between the State’s metrics and the new metrics. It is recommended that performance models be established based on the new metrics, as the deterioration rates from the two metrics were not well correlated.