Search results

Inspection and maintenance of plant and machinery has traditionally been based on prescriptive industry practices. However, increased experience and a greater understanding of operational hazards is leading sections of industry to take a more informed approach to planning inspection and maintenance, targeting resources to reduce the risk to as low as reasonably practicable. The purpose of this paper is to present an approach to asset management to minimize risks in the most cost effective way.

The approach shown optimizes run‐repair‐replace decision‐making in the integrity management of assets with the ultimate aim of maximising the impact of money spent on risk mitigation actions. The risk‐based approach, as opposed to the more conventional approaches, assesses failure in its wider context by considering not just the likelihood of failure, but also the consequences should the failure event occur.

The risk‐based methodology presents a cost‐effective way to minimise life cycle costs in the management of assets whilst maintaining reliability or availability targets, and operating within safety and environmental regulation.

In this paper, for demonstration, a wind turbine system consisting of a number of components including structural components is used. However, the methodology can be extended to any system in which components can be analyzed to provide the required inputs to the risk model.

At a time when competitive pressures force asset managers to prioritize their maintenance, the risk‐based methodology presented here is a rational, efficient and somewhat flexible way to asset integrity management.

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.

Sustainable production and manufacturing is an emerging concept for commercially successful performance. A question is how core asset processes should be streamlined to ensure performance quality and to portray their business roles within sustainability requirements. The purpose of this paper is to address this issue from asset operations and maintenance (O&M) perspective.

Issues discussed here are based on findings from a project launched during 2000‐2003 in Norway with major emphasis on performance integrity, consistency, and quality.

Through the findings of the original study, the paper elaborates on the important “Business – Production or Manufacturing asset – Operations and Maintenance” performance architecture in sustainable business environments.

The paper aims at stimulating production and manufacturing cluster to initiate joint R&D efforts to further explore and research this interesting and important subject matter that seems to be sensitive from socio‐economical viewpoint.

The paper provides a framework to explore and specify critical elements for a risk‐based industrial asset management practice in dynamic and complex settings of sustainable environments. This is critical for many commercial businesses with the ongoing debates on the proper balance between profits and fundamentals, and to develop effective performance measurements and auditing systems for asset O&M process to ensure integrity, consistency and quality in performance.

Many underlying issues regarding sustainability performance in production and manufacturing environments still remain ill‐defined. This paper clarifies how the emerging sustainable business concept can influence performance in industrial production and manufacturing assets, and how to outline a framework to streamline O&M performance.

This paper extends the authors’ previous research work investigating resilience for municipal infrastructure from an asset management perspective. Therefore, this paper aims to formulate a pavement resilience index while incorporating asset management and the associated resilience indicators from the authors’ previous research work.

This paper introduces a set of holistic-based key indicators that reflect municipal infrastructure resiliency. Thenceforth, the indicators were integrated using the weighted sum mean method to form the proposed resilience index. Resilience indicators weights were determined using principal components analysis (PCA) via IBM SPSS®. The developed framework for the PCA was built based on an optimization model output to generate the required weights for the desired resilience index. The output optimization data were adjusted using the standardization method before performing PCA.

This paper offers a mathematical approach to generating a resilience index for municipal infrastructure. The statistical tests conducted throughout the study showed a high significance level. Therefore, using PCA was proper for the resilience indicators data. The proposed framework is beneficial for asset management experts, where introducing the proposed index will provide ease of use to decision-makers regarding pavement network maintenance planning.

The resilience indicators used need to be updated beyond what is mentioned in this paper to include asset redundancy and structural asset capacity. Using clustering as a validation tool is an excellent opportunity for other researchers to examine the resilience index for each pavement corridor individually pertaining to the resulting clusters.

This paper provides a unique example of integrating resilience and asset management concepts and serves as a vital step toward a comprehensive integration approach between the two concepts. The used PCA framework offers dynamic resilience indicators weights and, therefore, a dynamic resilience index. Resiliency is a dynamic feature for infrastructure systems. It differs during their life cycle with the change in maintenance and rehabilitation plans, systems retrofit and the occurring disruptive events throughout their life cycle. Therefore, the PCA technique was the preferred method used where it is data-based oriented and eliminates the subjectivity while driving indicators weights.

This work is part of a research project that seeks to gain insight into the applicability of different actor analysis methods. This paper aims to describe the analysis of the predictive value of two different actor analysis methods applied to the redesign of the water management of a Dutch polder.

The paper's approach is a parallel application and comparison of the results of two separate actor analysis methods: conflict analysis and transactional analysis.

Transactional analysis was more accurate regarding the prediction of the real world outcomes than conflict analysis.

The choice of analysis method may be of significant influence on the quality of understanding of the risks involved with different stakeholders. There may not be a single silver bullet.

An asset manager needs to apply multiple actor analysis techniques to fully grasp the impact of the threats posed by the stakeholders on the asset and its risk register.

The parallel application of the two methods and comparison of their predictive value is new.

The purpose of this paper is to present a conceptual risk assessment technique to aid facility managers with developing robust asset management programs. It proposes to evaluate three discrete risk sensitivity levels relative to the recognition of stochastic costs on expected budgetary outcomes. The paper expands the domain of available risk assessment techniques toward estimating impacts from uncertainty on desired levels of service.

The paper explores the concept that facility managers can cost‐effectively manage robust asset management programs. This is accomplished by evaluating simple relationships between risk‐sensitive decision‐response alternatives and systems degradation characteristics. The resulting parameters are aggregated to estimate expected budgetary outcomes for robust asset management programs.

The paper reveals that if facility managers assume risk‐avoidance positions, they can positively affect expected budgetary outcomes for robust asset management programs. Facility managers can manage the magnitude of these adverse impacts by mitigating stochastic costs associated with the completion of unscheduled asset management activities.

The expected implications are enhancements to the abilities of facility managers to cost‐effectively manage stochastic costs relative to risk sensitivity and desired levels of service. However, because of the chosen research approach, the research results may lack general usability. Therefore, researchers are encouraged to test the proposed propositions further.

The practical implications allow facility managers to cost‐effectively manage adverse impacts on expected budgetary outcomes for robust asset management programs.

The expected contribution is a tool for facility managers to manage uncertainty when allocating limited financial resources among the competing corrective, maintenance, and rehabilitation activities within robust asset management programs.

The paper fulfills an identified need to study how facility managers can do more with less. This need to be cost‐effective requires facility managers to recognize stochastic costs on the expected budgetary outcomes for robust asset management programs.

Managers are increasingly looking to adopt the Internet of Things (IoT) to include the vast amount of big data generated in their decision-making processes. The use of IoT might yield many benefits for organizations engaged in civil infrastructure management, but these benefits might be difficult to realize as organizations are not equipped to handle and interpret this data. The purpose of this paper is to understand how IoT adoption affects decision-making processes.

In this paper the changes in the business processes for managing civil infrastructure assets brought about by IoT adoption are analyzed by investigating two case studies within the water management domain. Propositions for effective IoT adoption in decision-making processes are derived.

The results show that decision processes in civil infrastructure asset management have been transformed to deal with the real-time nature of the data. The authors found the need to make organizational and business process changes, development of new capabilities, data provenance and governance and the need for standardization. IoT can have a transformative effect on business processes.

Because of the chosen research approach, the research results may lack generalizability. Therefore, researchers are encouraged to test the propositions further.

The paper shows that data provenance is necessary to be able to understand the value and the quality of the data often generated by various organizations. Managers need to adapt new capabilities to be able to interpret the data.

This paper fulfills an identified need to understand how IoT adoption affects decision-making processes in asset management in order to be able to achieve expected benefits and mitigate risk.

The purpose of this paper is to explore and compare the asset management policies and practices of six Australian states – New South Wales, Victoria, Queensland, South Australia, Western Australia and Tasmania – to improve understanding of the policy context to best shape policy focus and guidelines. Australian state-wide asset management policies and guidelines are an emergent policy domain, generating a substantial body of knowledge. However, these documents are spread across the layers of government and are therefore largely fragmented and lack coherency.

The comparative study is based on the thematic mapping technique using the Leximancer software.

Asset management policies and guidelines of New South Wales and Victoria have more interconnected themes as compared to other states in Australia. Moreover, based on the findings, New South Wales has covered most of the key concepts in relation to asset management; the remaining five states are yet to develop a comprehensive and integrated approach to asset management policies and guidelines.

This review and its findings have provided a number of directions on which government policies can now be better constructed and assessed. In doing so, the paper contributes to a coherent way forward to satisfy national emergent and ongoing asset management challenges. This paper outlines a rigorous analytical methodology to inform specific policy changes.

This paper provides a basis for further research focused on analyzing the context and processes of asset management guidelines and policies.

The use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence, the purpose of this paper is to present a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.

The paper builds upon our previous survey of this field which was carried out for the ten‐year period 1995‐2004. Like the previous survey, it uses Elsevier's Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case‐based reasoning (CBR), fuzzy logic (FL), knowledge‐Based systems (KBS), data mining, and hybrid AI in the four application areas are identified.

The survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.

This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.