| description abstract | The collection of infrastructure performance data is critical for agencies to cost-effectively maintain and preserve their existing assets. This paper presents a decision-support tool developed to support a state planning agency seeking to select a cost-effective data procurement strategy; specifically, the agency is considering whether to continue collecting and processing infrastructure performance data in-house or outsource to a third-party vendor. The probabilistic tool integrates uncertainty estimation via statistical methods and elicitation of expert judgement with Monte Carlo simulations to compute the probabilistic life-cycle cost of alternative data procurement strategies. For this particular case study, the expected cost to continue data collection and processing activities in-house is higher than the cost to outsource such activities. More importantly, the case study results lead to other important insights and contributions that are more generalizable to other contexts. For example, the labor resources required to collect, process, and maintain infrastructure condition data is the largest driver of total life-cycle costs for in-house data collection. Furthermore, because the decision to outsource data collection is made well before an agency selects a vendor, and such cost estimates are frequently unknown, there is a higher level of uncertainty and potential risk associated with outsourcing these activities. These conclusions, as well as the methods and framework presented in this paper, should assist planning agencies as they develop their data procurement strategy. | |
