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Maintenance management is perceived as fundamental for ensuring that physical assets contribute to business outcomes. In this context, the purpose of this paper is to elaborate a novel maintenance management framework for asset management (MMFAM) based on ISO 55000 series guidelines.

The paper proposes an MMFAM that intends to translate the technical requirements set out in the ISO 550001 standard to the maintenance stage of asset management. First, the framework is modeled using consideration based on the analysis of the ISO 55000 series and the review of previous maintenance management frameworks. Then, support tools are presented that collaborate for the implementation of the processes and activities provided for in the MMFAM.

The proposed MMFAM was modeled using a Business Process Model and Notation since it is a standardized graphical notation for process modeling and contributes to the understanding of the framework. In addition, the applicable tools were presented for the practical implementation of MMFAM processes and activities.

The paper is expected to contribute to maintenance practitioners and researchers in understanding and disseminating a novel maintenance management framework that is in line with asset management through the international ISO 55000 series.

As it is essentially multidisciplinary and complex, asset management has still been little explored. Moreover, the requirements for implementing an asset management system, as explained in the ISO 55001 standard, only provide what needs to be done, but not how to do it. Accordingly, the paper fills a gap in maintenance management literature, as it addresses the ISO 55000 series for asset management in depth.

The Maintenance Management Framework for Asset Management (MMFAM) is a recently modeled framework to ensure the alignment of maintenance management with physical asset management based on the ISO 55000 series for asset management. In this context, the purpose of this paper is to discuss the applicability of the MMFAM considering the operational context of a hydroelectric power plant.

The paper adopted the case study method for the discussion of the applicability of the MMFAM to a real operational context. A hydroelectric power plant was chosen as the scope of the case study due to its relevance since the electricity sector is an example of an asset-intensive industry in which asset management performance is fundamental. To gain a detailed understanding of the organization, data were collected through direct requests to the plant, informal meetings with technical collaborators, a technical visit to the hydroelectric plant and on-site data collection. Then, the MMFAM processes were demonstrated based on this information and the results supported the discussion of the MMFAM applicability.

The case study provided a deeper understanding of the processes included in the MMFAM. In addition, the results suggested the applicability of the framework to other organizations besides the hydroelectric sector due to its generic approach and the possibility of choosing appropriate tools to support and implement the MMFAM processes.

The case study is expected to contribute to the practical understanding of the MMFAM processes within an operational context and assist maintenance professionals and researchers in their implementation in other organizations.

Although the literature provides different maintenance management frameworks, their practical discussion based on a real operational context is still a gap. Accordingly, this paper discusses the MMFAM under a case study method to expand its understanding beyond theory and contribute to practical comprehension in depth.

Stakeholders of the Architecture, Engineering and Construction (AEC) sector require information on the buildings economic performance throughout its life cycle. This information is neither readily available nor always accurate because building management (BM) professionals still face difficulties to fully incorporate the life cycle cost (LCC) concept into their daily practice. The purpose of this paper is to identify and contribute to solving these difficulties.

This paper provides a background knowledge review and set the ground for a structured research roadmap and a management framework that highlight the links and limitations to be addressed within and between LCC and BM. A six-stage method was used for developing conceptual frameworks targeting six goals: establishing a point of departure; mapping sources of information; literature research; notion deconstruction and conceptual categorization; overview of the applicable background knowledge; and structuring of a framework for LCC-informed decisions in BM.

Management solutions for the built context are necessarily connected with LCC and BM current concepts such as asset management, project, program and portfolio management, facility management and data management. These management approaches highlight the importance of incorporating life cycle concepts and promote LCC effective application within the AEC sector.

This paper identifies and discusses current limitations on the information availability for the economic performance of buildings throughout its life cycle. This work also identifies LCC-related topics that need to be further explored or addressed by both the scientific community and practitioners to overcome these limitations and facilitate the integration of the LCC concept into BM activities.

The purpose of this paper is to design a framework for asset data management in power companies. The authors consider asset data management from a strategic perspective, linking operational-level data with corporate strategy and taking into account the organizational context and stakeholder expectations.

The authors conducted a multiple case study based on a literature review and three series of in-depth interviews with experts from three Russian electric power companies.

The main challenge in asset data management for electric power companies is the increasing amount and complexity of asset data, which is frequently incomplete or inaccurately collected, hard to translate to managerial language, focused primarily on the operational level. Such fragmented approach negatively affects strategic decision-making. The proposed framework introduces a holistic approach, provides context and accountability for decision-making and attributes data flows, roles and responsibilities to different management levels.

The limitations of our study lie in the exploratory nature of case study research and limited generalization of the observed cases. However, the authors used multiple sources of evidence to ensure validity and generalization of the results. This article is a first step toward further understanding of the issues of transformation in power companies and other asset intensive businesses.

The novelty of the framework lies in the scope, focus and detailed treatment of asset data management in electric power companies.

The purpose of this paper is to explore certain resources, capabilities and competencies needed to improve the performance of physical asset management (PAM).

The analytic hierarchy process (AHP) is used to select and prioritize the most appropriate factors for improving performance. A multi-criteria approach is used to analyze and compare the importance of 6 main criteria and 18 subcriteria identified from a survey of relevant literature.

The study revealed that not all factors are viewed as having equal importance in improving PAM performance, as three of the main factors attained greater importance among the six factors.

This study explored the factors required for managing assets only within the third stage of asset lifecycle, that is, the utilization stage. It is recommended that future studies be conducted in such a way as to determine the importance of similar factors in the other stages of the asset lifecycle, or to identify new factors and add new criteria.

Knowledge of the differential impacts of the factors on the performance of PAM can impact asset managers and decision makers in their allocation of resources and focus their work on the highest-ranked rather than the lowest-ranked factors. Also, AHP used provides an effective mean for asset managers to identify priorities among decision criteria in their organization.

To date, no study has explored the impact of six combined factors on the performance of PAM. Previous studies have found that these factors each had equal importance. However, their relative ranking in practice and when they appear together have remained unrecognized.

The purpose of this paper is to study the current research approaches in asset management (AM), to evaluate some of the prevalent research methods in AM studies and to summarize the result into a building-block research that may provide design guidelines in AM studies.

AM publications were selected for this study using by online search engines and the publications were classified based on the appropriate research approaches. The results will be discussed and a suitable building-block research for AM studies will be constructed based on the identified research approaches.

The paper identifies, analyses and validates the research approaches found in a sample of online AM publications. The research-approaches and their associated methods will be discussed to develop understanding of the context of these approaches in AM research.

The paper limits the study in publications within the AM field in the petroleum industry. However, the research methods that are presented covers the most common research methods found in publications. Thus, although the sample of publications may not represent the entire population, the same approach and result can be used in similar topics and conditions.

Researchers or practitioners can benefit from the building blocks of research to develop a research design for AM studies. Moreover, the paper also provides information on common research methods and data gathering techniques that can be used for similar studies.

The purpose of this study is to identify the key data categories and characteristics defined by asset information requirements (AIR) and how this affects the development and maintenance of an asset information model (AIM) for a blockchain-based digital twin (DT).

A mixed-method approach involving qualitative and quantitative analysis was used to gather empirical data through semistructured interviews and a digital questionnaire survey with an emphasis on AIR for blockchain-based DTs from a data-driven predictive analytics perspective.

Based on the analysis of results three key data categories were identified, core data, static operation and maintenance (OM) data, and dynamic OM data, along with the data characteristics required to perform data-driven predictive analytics through artificial intelligence (AI) in a blockchain-based DT platform. The findings also include how the creation and maintenance of an AIM is affected in this context.

The key data categories and characteristics specified through AIR to support predictive data-driven analytics through AI in a blockchain-based DT will contribute to the development and maintenance of an AIM.

The research explores the process of defining, delivering and maintaining the AIM and the potential use of blockchain technology (BCT) as a facilitator for data trust, integrity and security.

While most efforts to combat climate change are focussed on energy efficiency and substitution of fossil fuels, growth in the built environment remains largely unquestioned. Given the current climate emergency and increasing scarcity of global resources, it is imperative that we address this “blind spot” by finding ways to support required services with less resource consumption.

There is now long overdue recognition to greenhouse gas emissions “embodied” in the production of building materials and construction, and its importance in reaching targets of net zero carbon by 2050. However, there is a widespread belief that we can continue to “build big”, provided we incorporate energy saving measures and select “low carbon materials” – ignoring the fact that excessive volume and area of buildings may outweigh any carbon savings. This is especially the case with commercial real estate.

As the inception and planning phases of projects offer most potential for reduction in both operational and embodied carbon, we must turn our attention to previously overlooked options such as “build nothing” or “build less”. This involves challenging the root cause of the need, exploring alternative approaches to meet desired outcomes, and maximising the use of existing assets. If new build is required, this should be designed for adaptability, with increased stewardship, so the building stock of the future will be a more valuable and useable resource.

This points to the need for increased understanding and application of the principles of strategic asset management, hitherto largely ignored in sustainability circles, which emphasize a close alignment of assets with the services they support.

Arguably, as the built environment consumes more material resources and energy than any other sector, its future configuration may be critical to the future of people and the planet. In this regard, this paper seeks to break new ground for deeper exploration.

The purpose of this paper is to identify and categorise problems in knowledge management of industrial maintenance, and support successful maintenance through adapting the SHEL model. The SHEL model has been used widely in airplane accident investigations and in aviation maintenance, but not in industrial maintenance.

The data were collected by two separate surveys with open-ended questions from maintenance customers and service providers in Finland. The collected data were coded according to SHEL model -derived themes and analysed thematically with NVivo.

The authors found that the adapted SHELO model works well in the industrial maintenance context. The results show that the most important knowledge management problems in the area are caused by interactions between Liveware and Software (information unavailability), Liveware and Liveware (information sharing), Liveware and Organisation (communication) and Software and Software (information integrity).

The data were collected only from Finnish companies and from the perspective of knowledge management. In practice there are also other kinds of issues in industrial maintenance. This can be a topic for future research.

The paper presents a new systematic method to analyse and sort knowledge management problems in industrial maintenance. Both maintenance service customers and suppliers can improve their maintenance processes by using the dimensions of the SHELO model.

The SHEL model has not been used in industrial maintenance before. In addition, the new SHELO model takes also interactions without direct human influence into account. Previous research has listed conditions for successful maintenance extensively, but this kind of prioritisation tools are needed to support decision making in practice.

The purpose of this paper is to describe a risk-based methodology developed to identify the severity of impacts to various types of infrastructure located within the Tennessee State Park system when exposed to extreme weather events. Infrastructure systems, composed of various assets, are central to the economic, environmental and cultural functioning of the society. Understanding the potential impacts to these assets from various threats is fundamental to prudent strategic, operational and financial decision-making. Among infrastructure, systems of interest are those managed and operated by park services. Such systems are particularly exposed to extreme weather, given the recreational activities that they provide.

This paper describes a risk-based methodology developed to identify the severity of impacts to various types of infrastructure located within the Tennessee State Park system when exposed to extreme weather events. It consists of the following steps: identifying extreme weather event types experienced in Tennessee; assessing damage to various types of park system infrastructure caused by these events; and deriving an overall impact score associated with specific types of park system infrastructure when exposed to certain types of extreme weather scenarios.

In applying this methodology, tornadic events were found to be most impactful, whereas drought and heat events had the least effect on park infrastructure. Dining and lodging infrastructure were found to incur the most damage, regardless of the weather event type.

The approach as described in this paper is transferable to other park systems as well as public sector assets in general.