A cross-sector analysis of human and organisational factors in the deployment of data-driven predictive maintenance

Abstract

Domains such as utilities, power generation, manufacturing and transport are increasingly turning to data-driven tools for management and maintenance of key assets. Whole ecosystems of sensors and analytical tools can provide complex, predictive views of network asset performance. Much research in this area has looked at the technology to provide both sensing and analysis tools. The reality in the field, however, is that the deployment of these technologies can be problematic due to user issues, such as interpretation of data or embedding within processes, and organisational issues, such as business change to gain value from asset analysis. 13 experts from the field of remote condition monitoring, asset management and predictive analytics across multiple sectors were interviewed to ascertain their experience of supplying data-driven applications. The results of these interviews are summarised as a framework based on a predictive maintenance project lifecycle covering project motivations and conception, design and development, and operation. These results identified critical themes for success around having a target-or decision-led, rather than data-led, approach to design; long-term resourcing of the deployment; the complexity of supply chains to provide data-driven solutions and the need to maintain knowledge across the supply chain; the importance of fostering technical competency in end-user organisations; and the importance of a maintenance-driven strategy in the deployment of data-driven asset management. Emerging from these themes are recommendations related to culture, delivery process, resourcing, supply chain collaboration and industry-wide cooperation.

Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies

“…Combining big data from contemporary technologies (e.g., smartphones) with current low technology services (e.g., water and electricity utilities) is a way to achieve digital transformation and create sustainable societies (George et al 2014). To this end, Golightly et al (2017), describe how different sectors (e.g., power generation, transport, manufacturing) are adopting big data and analytics tools for data-driven decisions and solutions. Their findings show that organizations need to go beyond their common data-driven design approaches, improve users' ability to interpret data and use it to take decision-driven approaches.…”

“…Finally, the special issue includes two papers that take a qualitative approach to examine the role of big data and their value in public and private sector organizations. The works from Okwechime et al 2017and Golightly et al (2017) provide a comprehensive picture and complement each other as they provide insight on how big data can be used in public and private organizations settings, respectively, for increased performance, better services, and improved solutions to existing problems.…”

“…The findings show that knowledge finding and reframing, along with inter-organizational collaborations and ex-post evaluations are needed for organizations to learn how to use and introduce big data, thus providing the groundwork for smart cities to fully explore and exploit the potential of big data. Golightly et al (2017) examine the role of big data in the industry and private organizations, in order to identify the factors that support or inhibit the design and deployment of predictive asset management. They differentiate from previous studies in the area of predictive management that focus on the technology (i.e., sensors and algorithms), by addressing user and organizational issues.…”

Modelling decision-making within rail maintenance control rooms

“…Infrastructure sensing and analysis requires the understanding of heterogeneous data sources and technical components (Ranjan et al 2017); so, the organisations adopting advanced maintenance analytics need greater degrees of technical knowledge to interpret the data (Aboelmaged 2014) that often spans organisational boundaries. The human factors implications of such innovation are substantial, encompassing the user-centered design of technology, knowledge and change management, and training (Dadashi et al 2014;Golightly et al 2018).…”

“…Those points where coping strategies are applied indicate where automation may offer significant benefits. It also suggests that the design of HMI should support these functions and, similar to Golightly et al (2018), rather than a black box of 'red', 'amber', 'green', the automation should support exploration of the reasoning behind decisions so that both the cause, and potential rectifying action, can be understood.…”