Implications for Resource Allocation

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Implications for Resource Allocation

While the scope of risk management may be very broad, an organization’s approach to risk management and the outcomes resulting from a risk assessment may nonetheless have important implications for TAM resource allocation. Consequently, it is important to establish a risk management approach and integrate consideration of risk with the resource allocation process.

Multi-objective decision making is a concept in operations research that is implemented in several different forms, from simple consensus-building approaches (e.g. Delphi processes) to more complex software tools. In all cases, it allows consideration of more than one factor or criteria in making a decision.

Specific possible implications of risk management on resource allocation may include, but are not limited to:

  • An organization may identify through its risk management approach areas where better data or improved processes are needed to best address a given risk, in turn impacting the resource allocation process. For instance, if uncertainty concerning future asset conditions is found to be a significant risk, this may result in efforts to improve the deterioration models in an agency’s asset management systems and/or motivate data collection improvements to reduce uncertainty.
  • An organization may identify specific investments of staff time and/or agency funds required to mitigate negative or leverage positive risk. Once specific investments are identified, they can be assessed along with investments in other asset/investment categories. For example, Caltrans defined a separate program for seismic retrofits as described in the Practice Example.
  • If an agency’s allocation of resources hinges on uncertain future values for one or more parameters, it may be necessary to incorporate consideration of uncertainty formally in the decision-making process. This can be accomplished using Monte Carlo simulation or other quantitative approaches to establish the predicted distribution of outcomes. For instance, in performing a life cycle cost analysis to select between project alternatives for a given facility, Monte Carlo simulation can calculate the range of life cycle costs predicted depending on future values for cost escalation, deterioration, or other parameters.
  • In approaching formal accounting for uncertainty, an organization may define different scenarios representing the possible range of outcomes and then determine how best to allocate resources in each scenario before establishing a preferred resource allocation approach. For example, if an agency’s future capital budget is unknown, a decision-maker may wish to define a high, medium and low budget scenario and determine what investments would be made in each scenario in order to most effectively prioritize given uncertainty. Likewise, a scenario analysis approach can be useful in assessing how to allocate resources for improving infrastructure resilience given uncertainty concerning future sea level rise. Typically, the decision maker will review results for different scenarios and make a subjective determination of how to allocate resources considering the relevant factors. The Practice Example describing the analysis of harbor-wide barrier systems for the City of Boston shows one such approach. Recent research in the area of Robust Decision Making (RDM) has focused on developing quantitative approaches to select optimal investments between different scenarios.