LEVERAGING ENGINEERING ASSET DATA: STRATEGIC PRIORITIES, DATA TYPES AND INFORMATIONAL OUTCOMES
A common complaint heard within the engineering asset community is that while the capacity for data storage increases, the quality of ever increasing amounts of data remains poor. We propose a new model of engineering asset data management that helps explain why data collected by organizations frequently fails to assist in effective engineering asset management. The model situates a four component typology of engineering data between institutional drivers (e.g. organizational culture; organizational strategy; organizational life-cycle; consequence of asset failure) and asset management outcomes. We argue these outcomes (regulatory compliance; time-based maintenance; condition-based asset management; capacity development) are functions not only of the data collected by an organization, but its capacity to leverage that data. We develop a model suggesting that institutional drivers dictate the data requirements of engineering asset intensive firms, typically at the cost of data requirements for different phases in the assets life-cycle. This paper will assist practitioners to re-conceptualize the manner in which they view their data, the manner in which it is utilized, and provide a better understanding of data and its intended outcomes. This will allow a better prioritization of data collection activities and offer an improved insight into ways in which engineering data may be better transformed into informational and knowledge outcomes.
Data Quality Organisational Lifecycle Data typology
Murphy GD Chang A Barlow M
CRC for Integrated Engineering Asset Management (CIEAM) 2 George Street Queensland University of Tec Faculty of Business, 2 George Street Queensland University of Technology, 4000, Australia Australian Nuclear Science and Technology Organisation (ANSTO), PMB 1 Menai NSW 2234 Australia
国际会议
北京
英文
1136-1142
2008-10-27（万方平台首次上网日期，不代表论文的发表时间）