A data quality framework for engineering asset management

Lin, Shien; Gao, Jing; Koronios, Andy

doi:10.1080/14484846.2008.11464549

Cited by 18 publications

(28 citation statements)

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“…Asset managers have attempted for many years to create AM systems which produce quality data but end up having data that are embedded with significant amount of meaningless data or missing required information (Lin et al, 2008;Brous et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Business value of integrated BIM-based asset management

Munir

Kiviniemi

Jones

2019

ECAM

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Purpose Currently, building information modelling (BIM) is largely seen as a 3D model, not as an information model or information management tool. This wrong perception of BIM and low interest in 3D asset management (AM) is one of the major reasons for the slow adoption by clients in the architectural, engineering and construction (AEC) industry. The purpose of this paper is to identify the techniques and strategies of streamlining AM systems for BIM-based integration, and how the information is captured from physical assets towards BIM-based integration for clients to derive value from BIM investments. Design/methodology/approach A qualitative case study strategy was used to study the strategic implementation process of integrating BIM with AM systems and the business value of BIM in AM by a large asset owner in the UK. Findings The paper identifies key strategies in the adoption of BIM-based processes by an asset owner, the implementation process, the challenges and the benefits attained. Several barriers were identified as the challenges of adopting BIM-based processes in AM: complexity and cost associated with BIM; irrelevance of 3D geometric data in AM processes; nature of asset ownership structure; managing the asset handover process; managing change within the organisation. Organisations will have to consider the following issues in streaming asset information with BIM: the development for a clear strategy prior to adoption; connecting the strategy to the business goals; and conducting the discovery exercise to identify organisational information needs. Originality/value The research addresses a significant gap in the development of techniques and strategies for asset owners to streamline BIM with AM systems and derive business value from such integration. The research context is a case study involving a large owner-operator in the UK that has been able to derive value from BIM systems in their AM processes. The key value of the paper is improving asset owners’ understanding of BIM in AM by demonstrating the implementation strategies, linkage to organisational objectives, challenges, value management process and business value of BIM in AM. Another contribution of the paper is improving the understanding of BIM, which is usually viewed as 3D models and that 3D geometric data do not have much value for AM tasks.

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Section: Introductionmentioning

confidence: 99%

Business value of integrated BIM-based asset management

Munir

Kiviniemi

Jones

2019

ECAM

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“…This study uses a sequential mixed-method approach involving interviews with 20 data collectors and a quantitative survey of 109 data collectors in a water utility. It examines the interactive effect of managerial pressure, technological input control and self-concordance on data collection performance.The need to improve the quality of manually-acquired data on assets is wellknown in the reliability literature [1][2][3][4]. Manually-acquired data includes data gathered as a result of inspections, as a part of repair work, and during asset operation by personnel whose main role is to operate or maintain assets.…”

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confidence: 99%

“…The need to improve the quality of manually-acquired data on assets is wellknown in the reliability literature [1][2][3][4]. Manually-acquired data includes data gathered as a result of inspections, as a part of repair work, and during asset operation by personnel whose main role is to operate or maintain assets.…”

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confidence: 99%

Are managerial pressure, technological control and intrinsic motivation effective in improving data quality?

Molina

Unsworth

Hodkiewicz

et al. 2013

Reliability Engineering & System Safety

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should be careful of the degree to which "push" factors, such as managerial pressure and technological input control, are relied upon. While they may be helpful for motivating those data collectors who are not intrinsically motivated, they are either not helpful or may discourage those data collectors who are intrinsically motivated. Instead, self-concordance may act as a longer-term, more stable approach to increasing the motivation of data collectors and thus increasing the quality of data that enter reliability systems. This study uses a sequential mixed-method approach involving interviews with 20 data collectors and a quantitative survey of 109 data collectors in a water utility. It examines the interactive effect of managerial pressure, technological input control and self-concordance on data collection performance.The need to improve the quality of manually-acquired data on assets is wellknown in the reliability literature [1][2][3][4]. Manually-acquired data includes data gathered as a result of inspections, as a part of repair work, and during asset operation by personnel whose main role is to operate or maintain assets. These manually collected data are used, in conjunction with sensor data, to develop a picture of asset health and performance which informs decisions about asset renewals, repairs and replacements [5]. Recent integrations and critiques of the data quality literature [6,7] showed that although many proposed solutions to poor quality data have involved cleaning the data once they have been collected [8] many are also aimed at influencing the input of the data -this occurs through either changing external factors that influence the data collector such as managerial and technology structures [e.g., 4, 5], or changing the motivation of the data collector [e.g., 6, 9, 10]. As yet though, little empirical research has rigorously examined the effects of the factors affecting the input of the data. Given the importance of high quality data in reliability systems, and the cost of cleaning the data after their collection, this neglect is surprising. This research therefore empirically tests the effect of the most common three factors (the manager, the technology used in inputting the data, and the intrinsic motivation of the data collector), as well as examining the effect of the interplay between them on the quality of manual data collection.Although there has been considerable increase in the use of sensors and the volume of data collected by them, using operators and maintenance staff (data collectors) to collect data on assets is still a common practice. Manual data 2 collection leverages the experience of the data collector. It often requires them to provide an assessment of the asset's condition, identify a failure mode, or make a prediction as to remaining asset life in addition to recording observations or actions taken. However, when data collectors record their observations consideration needs to be given to psychological factors to ensure that appropriate factors are in place to encoura...

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“…Collecting relevant, fit-for-use data from all stakeholders in E-Maintenance system and cleaning data from errors Aljumaili et al, (2015), Jardine et al, (2006), Tretten et al, (2014), Jantunen et al, (2010), Tsang et al, (2006), Lin et al, (2006), Bergquist and Söderholm (2014), Karim et al, (2016),…”

Section: Literature Reviewmentioning

confidence: 99%

Advantages and potential challenges of data management in e-maintenance

Razmi-Farooji

Kropsu-Vehkaperä

Härkönen

et al. 2019

JQME

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Purpose The purpose of this paper is twofold: first, to understand data management challenges in e-maintenance systems from a holistically viewpoint through summarizing the earlier scattered research in the field, and second, to present a conceptual approach for addressing these challenges in practice. Design/methodology/approach The study is realized as a combination of a literature review and by the means of analyzing the practices on an industry leader in manufacturing and maintenance services. Findings This research provides a general understanding over data management challenges in e-maintenance and summarizes their associated proposed solutions. In addition, this paper lists and exemplifies different types and sources of data which can be collected in e-maintenance, across different organizational levels. Analyzing the data management practices of an e-maintenance industry leader provides a conceptual approach to address identified challenges in practice. Research limitations/implications Since this paper is based on studying the practices of a single company, it might be limited to generalize the results. Future research topics can focus on each of mentioned data management challenges and also validate the applicability of presented model in other companies and industries. Practical implications Understanding the e-maintenance-related challenges helps maintenance managers and other involved stakeholders in e-maintenance systems to better solve the challenges. Originality/value The so-far literature on e-maintenance has been studied with narrow focus to data and data management in e-maintenance appears as one of the less studied topics in the literature. This research paper contributes to e-maintenance by highlighting the deficiencies of the discussion surrounding the perspectives of data management in e-maintenance by studying all common data management challenges and listing different types of data which need to be acquired in e-maintenance systems.

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A data quality framework for engineering asset management

Cited by 18 publications

References 0 publications

Business value of integrated BIM-based asset management

Business value of integrated BIM-based asset management

Are managerial pressure, technological control and intrinsic motivation effective in improving data quality?

Advantages and potential challenges of data management in e-maintenance

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