2.5.4 Data Required for Decision-Making


Data Required for Decision-Making

This subsection emphasizes the significance of data collection for effective decision-making in asset management programs, particularly using a Reliability Centered Maintenance (RCM) approach. It outlines essential data requirements for different maintenance types, such as condition-based, interval-based, and reactive-based maintenance, and highlights the importance of complete and reliable data. Additionally, the document discusses desirable data that can enhance decision-making by providing clarity, supporting different agency departments, generating accurate work orders, managing asset risks, and tracking the asset's full life cycle.

Data-driven decisions depend on asset data to guide effective investment choices. Effective data collection practices allow an agency to execute strategic RCM maintenance and streamline work order distribution. With accurate data, metrics can be derived, and performance measures compared to assess the effectiveness of a maintenance strategy and identify areas for improvement. Ancillary asset data can be categorized as either essential or desirable, depending on the management approach. Regardless of the data type, it is crucial that the data be complete and reliable. Generally, the RCM process required the following essential data for effective management.

Table 2.5 – Essential Data by Maintenance Approaches for Ancillary Assets

Maintenance TypeAsset TypeAsset LocationAsset Unique IDCondition Data
Interval-based maintenanceXXX-
Condition-based maintenanceXXXX
Reactive-based maintenanceXXX-

There are various strategies and technologies that support data acquisition that can be found in Chapter 7 and in the Handbook for Ancillary assets (FHWA-HIF-19-006). Other desirable data can augment decision-making by:

  • Providing additional clarity and accuracy to the essential data collected.
  • Supporting different departments within an agency.
  • Assisting in generating accurate work orders.
  • Helping manage asset risks.
  • Tracking the asset’s full life cycle to make informed decisions.

The above list is not all-inclusive but provides clear examples of reasons why additional data collected in the field could be beneficial to an agency. Each of these items is described in more detail in the handbook. Other desirable data attributes have been referenced elsewhere (HMEP 2013), and may also be considered, including:

  • Maintenance intervals.
  • Frequency of failure.
  • Allocated risk factors.
  • Maintenance requirements.
  • Engineering specific data