Methodology For Asset Management Research Articles

Improving access to infusion services: Results of an operational sprint.

e13522 Background: As a large clinical academic cancer center growing from 40,772 unique patients in 2021 to 44,416 patients in 2022, access for patients to begin therapy in the infusion suite was delayed by 3.2 days. In addition to delays in care the center experienced decreased patient satisfaction related to timeliness of scheduling and an inflexible scheduling template that did not allow for the maximization of chair assets. An infusion sprint was launched to address the challenges with the main goal of providing a seamless patient experience while ensuring that all 60 chair assets were maximally utilized. The multi-disciplinary team sought to accomplish this through a disciplined evaluation and intervention program with the goals of increasing access, improving patient satisfaction and growing operating income. Methods: In February 2022, a multi-disciplinary team was assembled to develop and implement a plan to increase access to the infusion suite over a period of 6 months. The team utilized LEAN-Six Sigma principles to characterize current state, desired future state and tactical interventions with metrics. Access data, available infusion appointments and activities occurring in chairs were analyzed with the goal of maximizing utilization. This was accomplished through the development of an active asset management methodology that compared available infusion appointment hours in the schedule to the actual completed hours which allowed the center to determine the growth opportunity within the current chair count. Financial performance was reviewed, and a pilot of an AI-based scheduling platform was launched. Results: Data collected in December 2022 showed that the pilot increased average daily chemotherapy infusion patient volume by 10% and overall volume by 11% over prior fiscal year from October 2021 to September 2022. Daily scheduling escalations manually resolved by leadership decreased 54% between May 2022 and September 2022. Newly created chair utilization methodology showed increased utilization from 62% to 82% in 7 months and patient satisfaction increased from 76.97% to 81.18% for “Timeliness of Scheduling.” These initiatives also resulted in an 18% increase in infusion/pharmacy income from operations from October 2021 to September 2022. All of this was accomplished without adding staff to the workforce or chairs to the infusion suite. Conclusions: A focused, intentional, multi-disciplinary infusion sprint conducted over 6 months resulted in increases of patient access, increased patient satisfaction and revenue. External validity is limited by its one site design with future investigation planned at other sites within the cancer care network to determine if results can be reproduced.

Productive Asset Management Methodology Technique in Virtual Machines to Increase CPU Utilization

One of the technologies with the quickest growth is cloud computing, which is utilized in many applications where customers can access virtual machines (VMs) that are offered by cloud service providers in data centers. The ideal distribution should meet both the needs of users and service providers. A2OA, an adaptive Archimedes optimization algorithm, is the suggested mechanism for allocating resources in cloud computing. The optimization problem will be solved and user tasks will be distributed using this adaptive approach. The adaptive approach will combine the Seagull Optimization Algorithm with the Archimedes Optimization Algorithm (AOA) (SOA). The update procedure in the AOA will be improved. in cooperation with the SOA. The tasks will be distributed to the user optimally based on the proposed A2OA. In MATLAB, the suggested methodology will be put into practice, and results will be examined. Performance metrics such make span, load standard deviation, load ratio, user provider satisfaction level, response time, and convergence analysis will be used to assess how well the suggested methodology performs. The suggested approach will be contrasted with the traditional approaches, including the Genetic Algorithm (GA), Particle Swarm Algorithm (PSO), and Whale Optimization Algorithm (WOA), respectively. Key Word: Virtual machines, adaptive Archimedes optimization algorithm (A2OA), Seagull Optimization Algorithm (SOA), Genetic Algorithm (GA), maximum power, load ratio

State-of-the-art review on asset management methodologies for oil-immersed power transformers

Owing to their vital function and high cost, oil-immersed power transformers represent key links in electricity grids. While extensive effort has been invested by industry in developing various condition monitoring and fault diagnosis apparatuses, it is not economically feasible to apply rigorous inspection and extensive testing to all power transformer fleets in the network. The fundamental purpose of asset management is to balance cost and reliability of the system. Maintaining the reliability of power transformers involves regular condition monitoring, fault diagnosis and overall health assessment. In addition, estimating the remnant life of power transformers can help asset managers plan capital investments. By reviewing the methods used for power transformer asset management, assets integrity team can decide the most suitable maintenance strategy for each transformer in the network. This paper is aimed to present state-of-the-art review for power transformer asset management, health assessment and remnant life methodologies. While many review papers on transformer fault diagnosis have been published in the literature, not much attention was given to present a state-of-the-art review on the three modules of the transformer asset managements that include fault diagnosis, reliability assessment and life management modules. These three modules are comprehensively presented in this paper that also presents future challenges and improvement in this field of research.

An asset management methodology for value-for-money reinstatement of pavement markings

The reinstatement of pavement markings after resurfacing will typically entail like-for-like re-marking of the resealed road sections. However, there are cases where sections in between planned reseal (infills) may be left untouched or are not required in the renewal contract irrespective of the service life and condition of the infill markings. This gives room for poor marking continuity and reduction of the markings service level. As the asset management of long-life high-performance markings includes a duty of care around cost-effectively realising value from planned maintenance, there is a need to consider infills when reinstating long-life markings on resurfaced road sections. This paper presents a method for determining infill sites for cost-effective renewal to forestall returning to the same road in the future, thereby managing customer delay and realising cost savings without compromising road safety. The method considers tactical assessment merits and life-cycle costs to demonstrate its suitability to solving the problem. Various marking reinstatement cases were investigated where the results showed method applicability and highlighted sensitivities to re-mark length and installation time. The method supports the asset owner/manager to plan pavement markings renewals at the tactical/section level and could benefit from further studies that consider more complex scenarios, extended variables and uncertainties.

Artificial intelligence-based human-centric decision support framework: an application to predictive maintenance in asset management under pandemic environments.

Pandemic events, particularly the current Covid-19 disease, compel organisations to re-formulate their day-to-day operations for achieving various business goals such as cost reduction. Unfortunately, small and medium enterprises (SMEs) making up more than 95% of all businesses is the hardest hit sector. This has urged SMEs to rethink their operations to survive through pandemic events. One key area is the use of new technologies pertaining to digital transformation for optimizing pandemic preparedness and minimizing business disruptions. This is especially true from the perspective of digitizing asset management methodologies in the era of Industry 4.0 under pandemic environments. Incidentally, human-centric approaches have become increasingly important in predictive maintenance through the exploitation of digital tools, especially when the workforce is increasingly interacting with new technologies such as Artificial Intelligence (AI) and Internet-of-Things devices for condition monitoring in equipment maintenance services. In this research, we propose an AI-based human-centric decision support framework for predictive maintenance in asset management, which can facilitate prompt and informed decision-making under pandemic environments. For predictive maintenance of complex systems, an enhanced trust-based ensemble model is introduced to undertake imbalanced data issues. A human-in-the-loop mechanism is incorporated to exploit the tacit knowledge elucidated from subject matter experts for providing decision support. Evaluations with both benchmark and real-world databases demonstrate the effectiveness of the proposed framework for addressing imbalanced data issues in predictive maintenance tasks. In the real-world case study, an accuracy rate of 82% is achieved, which indicates the potential of the proposed framework in assisting business sustainability pertaining to asset predictive maintenance under pandemic environments.

Battery asset management with cycle life prognosis

Battery Asset Management problem determines the minimum cost replacement schedules for each individual asset in a group of battery assets that operate in parallel. Battery cycle life varies under different operating conditions including temperature, depth of discharge (DOD), charge rate, etc., and a battery deteriorates due to usage, which cannot be handled by current asset management models. This paper presents a new battery asset management methodology where battery cycle life prognosis is integrated with parallel asset management to reduce lifecycle cost of the Battery Energy Storage Systems (BESS). For the battery failure time prognosis, a nonlinear physics-based battery capacity fade model is developed and incorporated in parallel asset management model to update battery capacity over time. Experiment results have shown that the developed battery asset management methodology can be conveniently used to facilitate BESS asset management decision making thereby decreasing asset lifecycle costs.

Replication Methods for Financial Indexes

In this paper, we first present a review of statistical tools that can be used in asset management either to track financial indexes or to create synthetic ones. More precisely, we look at two important replication methods: the strong replication, where a portfolio of very liquid assets is created and the goal is to track an actual index with the portfolio, and weak replication, where a portfolio of very liquid assets is created and used to either replicate the statistical properties of an existing index, or to replicate the statistical properties of a custom asset. In addition, for weak replication, the target is not an index but a payoff, and the replication amounts to hedge the portfolio so it is as close as possible to the payoff at the end of each month. For strong replication, the main tools are predictive tools, so filtering techniques and regression play an important role. For weak replication, which is the main topic of this paper, in order to determine the target payoff, the investor has to find or choose the distribution function of the target index or custom index, as well as its dependence with other assets, and use a hedging technique. Therefore, the main tools for weak replication are modeling (estimation and goodness-of-fit) and optimal hedging. For example, an investor could wish to obtain Gaussian returns that are independent of some ETFs replicating the Nasdaq and S&P 500 indexes. In order to determine the dependence of the target and a given number of indexes, we introduce a new class of easily constructed models of conditional distributions called B-vines. We also propose to use a flexible model to fit the distribution of the assets composing the portfolio and then hedge the portfolio in an optimal way. Examples are given to illustrate all the important steps required for the implementation of this new asset management methodology.

Network Rail: a new approach to coastal asset management – North Wales case study

Network Rail Wales Route, in partnership with JBA Consulting, has recently developed a new coastal asset management methodology for assessing the risks posed by coastal processes to the Network Rail Wales Route’s portfolio of coastal, estuarine and river defence assets and the rail track infrastructure that they protect. The system has been designed in accordance with best practice guidance, to provide a consequence-based asset risk assessment. This considers the physical condition of the asset in combination with the risk from coastal erosion, wave overtopping and still water level flooding. In order for the coastal asset management plan to be effective over an extended time frame, these risks are analysed in the present day and for three future time epochs: 2025, 2055 and 2115. Analysing the future risk profile allows the development of a long-range view of likely future maintenance and capital liabilities. The coastal asset management system developed includes a number of innovative data capture, analysis and presentation techniques. These tools simplify large volumes of data relating to complex processes. This analysis provides the quantitative evidence base necessary for Network Rail to develop a holistic, whole-portfolio asset management plan to help better in allocating and prioritising funding for defence improvements.

A clean fight: environmental management strategies to drive growth, efficiency and competition in CSG

During the past 12 months, operators in LNG–CSG have had little option but to purely focus on cost efficiencies to remain profitable after the oil price crash of 2014 (Macdonald-Smith, 2015). This is set to continue with oil hovering around the $40 per barrel price mark, and as more CSG projects transition into operations in the next five years, an alternative approach to operating must be explored to further promote competitiveness and long-term sustainability in this volatile market (Cassidy and Kosev, 2015). The purpose of this paper is to critically analyse an approach to CSG operations that considers environmental aspects as an effective alternative in addressing the range of operational challenges to improve economic outcomes for operators. Essentially, the approach highlights opportunities to improve yields and further reduce costs along the gas extraction/processing value chain while meeting the critical compliance checklist in CSG. This paper outlines the key hallmarks of an effective adaptive environmental management approach from well site to water treatment operations. It then turns to consider each of the key components that make up this approach, including the role of leadership, contracting strategies, asset management methodologies, treatment technologies, effective remediation and recovery management, and the benefits that efficient operations deliver to surrounding communities. This paper also examines key case studies that are delivering substantial savings for CSG operations in the Bowen and Surat basins. These case studies share a similar theme in their intention to generate and implement improvements in efficiency and value delivered by effective water and environmental management, each within their own context.

Life Cycle Asset Management Methodologies for Buildings

AbstractAlthough asset management methodologies and tools related to the civil infrastructure domain have existed for decades, there are many challenges in applying these same techniques to buildings. This is because of the complexity and diversity of building structures, and their ownership profile. Numerous influences including scarcity of energy and material resources and land are creating greater awareness of the need for structured building management to reduce total life cycle needs. This reduction in consumption needs to be balanced with the requirements of the building occupant, such that building services are provided at a certain level of quality over a specified lifespan. Whether the building reaches that life and how it performs in service depends on maintenance, repair, and modernization during that timeframe, or restoration and recapitalization near the end of that timeframe. Optimal facility asset management needs to consider minimizing the life cycle costs related to these activities, whil...

Failure prediction and optimal scheduling of replacements in asbestos cement water pipes

Asbestos cement (AC) pipes are among the oldest assets in many water supply networks. With increasing failure rates and cost consequences, asset management tools are required to pre-empt unplanned failures and schedule future replacement at the most cost-effective time during service. This paper develops a physical probabilistic failure model for AC pipes under combined internal pressure and external loading. Uncertainty in the degradation process is accounted for using the Weibull extreme value probability distribution. Monte Carlo simulation is then used to estimate the probability of pipe failure as ageing proceeds. In the final stage of the model, a cost/benefit analysis is conducted to determine optimal scheduling of future inspection and replacement activities. The end result is a potentially useful asset management methodology in which both the probable physical lifetime and the economic lifetime of an AC pipe can be estimated.

1623 トンネル非常用設備の最適点検・更新政策(S36-1 トンネルの空気力学,換気,火災(1),S36 トンネルの空気力学,換気,火災)

In this paper, an asset management methodology of highway facilities, which are characterized by the multiple components of function- and structure-parts, is presented. The states of function-parts are described by the 0-1 variables designating whether the respective part is active or inactive, while those of the structure-parts are represented by the multiple rating index. In managing the multi-component facilities, the repair and rehabilitation policies of the structure parts as well as the synchronizing policies of the replace timing of functional- and structure-parts should be simultaneously investigated. In this paper the optimal synchronizing model is formulated to investigate the optimal repair and synchronizing repair policies of the respective parts and the optimal inspection and repair timing of the systems as a whole. The model presented in the paper is applied to the asset management of the anti-fire facilities on the expressways.

Asset Management Evaluation: A Pilot Case Study

The long term management of a production asset raises several major issues, among which rank the technical management of the plant, its economics, and the fleet level perspective one has to adopt. Decision makers are therefore faced with the need to define long term policies (up to the end of asset operation) that take into account multiple criteria including safety (which is paramount) and performance. In this paper we first remind the reader of the EDF three-level methodology for asset management. As introduced in PVP 2003 and PVP 2004, this methodology addresses the component/technical level (how to safely operate daily and invest for the future), the plant level (how to translate technical decisions into plant-wide consequences including economic performance), and the fleet level (how to manage a large number of similar assets). We then focus on the software tool that implements this methodology in order to allow decision makers to define, evaluate, and analyze long term plant operation and maintenance policies. Lastly we show how the methodology and the software tool were used on a pilot case study. The technical and economic results obtained at the plant level are described as well as the conclusions one can draw from them in order to help decision makers evaluate and analyze long term asset management strategies.

DTI APPROVES BRAEMAR FIELD DEVELOPMENT PLAN

Battery Asset Management problem determines the minimum cost replacement schedules for each individual asset in a group of battery assets that operate in parallel. Battery cycle life varies under different operating conditions including temperature, depth of discharge (DOD), charge rate, etc., and a battery deteriorates due to usage, which cannot be handled by current asset management models. This paper presents a new battery asset management methodology where battery cycle life prognosis is integrated with parallel asset management to reduce lifecycle cost of the Battery Energy Storage Systems (BESS). For the battery failure time prognosis, a nonlinear physics-based battery capacity fade model is developed and incorporated in parallel asset management model to update battery capacity over time. Experiment results have shown that the developed battery asset management methodology can be conveniently used to facilitate BESS asset management decision making thereby decreasing asset lifecycle costs.