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Level: Basic
TCM Section(s):
3.2. Asset Planning
3.3. Investment Decision Making
Venue: 2020 AACE International Conference & Expo
Abstract: When organizations face disaster-response situations that trigger large-scale maintenance and repair work, their data management systems need to be able to adapt to a significant increase in work and often changes to capital portfolios. Many data systems are designed for steady-state work, from large capital portfolios to routine maintenance work on smaller scales. After a major disaster occurs, the priorities of the organization change, resulting in shifting resources, adjusted priorities for capital and maintenance work, and modifications to existing processes and procedures. In emergency situations, an added complexity includes oversight from government regulators requiring an increase in progress reporting and data requests, requiring standardized definitions among regions and programs. A key success factor in this effort is the adaptability and usability of the existing data management systems.
By improving the data management structure for post-disaster efforts, the general data (i.e. production rates, cost per repair, resources required per repair) can be used in future maintenance efforts to budget and forecast. Therefore, the data being created during these post-disaster situations becomes useful as a benchmarking and/or historic data tool. This paper will discuss strategies that help companies scale their existing tools and reporting during post-disaster periods in a way that can be leveraged for future benefit to the organization. A case study will present how a large public utility was able to adapt to a significant increase in repair work following a major disaster event and leverage the influx of information for long-term improvements to their asset management and capital planning systems.
TCM Section(s):
3.2. Asset Planning
3.3. Investment Decision Making
Venue: 2020 AACE International Conference & Expo
Abstract: When organizations face disaster-response situations that trigger large-scale maintenance and repair work, their data management systems need to be able to adapt to a significant increase in work and often changes to capital portfolios. Many data systems are designed for steady-state work, from large capital portfolios to routine maintenance work on smaller scales. After a major disaster occurs, the priorities of the organization change, resulting in shifting resources, adjusted priorities for capital and maintenance work, and modifications to existing processes and procedures. In emergency situations, an added complexity includes oversight from government regulators requiring an increase in progress reporting and data requests, requiring standardized definitions among regions and programs. A key success factor in this effort is the adaptability and usability of the existing data management systems.
By improving the data management structure for post-disaster efforts, the general data (i.e. production rates, cost per repair, resources required per repair) can be used in future maintenance efforts to budget and forecast. Therefore, the data being created during these post-disaster situations becomes useful as a benchmarking and/or historic data tool. This paper will discuss strategies that help companies scale their existing tools and reporting during post-disaster periods in a way that can be leveraged for future benefit to the organization. A case study will present how a large public utility was able to adapt to a significant increase in repair work following a major disaster event and leverage the influx of information for long-term improvements to their asset management and capital planning systems.