Unitywater, a leading water utility service, engaged us to construct a first-pass value framework in order to support the quantificationof risks, benefits, and costs. This framework served as a foundation for optimizing maintenance task frequencies. The goal was to align the optimization outcomes with Unitywater’s organizational perspective on risk, thereby overcoming previous discrepancies experienced during a maintenance optimization initiative.
The primary objective was to create a comprehensive value framework that facilitates the monetization of risk magnitude. This would ensure that optimization results align with the subject matter experts’ views on risk within Unitywater. Subsequently, applying this framework to an existing sewage pump station model, we aimed to optimize maintenance task frequencies.
The project utilized Unitywater's existing data, and integrated it with a model of a sewage pump station. The model was connected to the developed value framework allowing an accurate application to real-world scenarios within Unitywater’s operational context.
To achieve the desired outcomes, a task frequency optimization solver was employed. This solver is generates cost and risk profiles, determines optimal task frequencies, and establishes allowable boundaries for timings. These bounds provide Unitywater with the flexibility to adjust task frequencies without significantly impacting lifecycle costs.
The outputs generated from the solver included dashboards displaying detailed cost and risk profiles, along with a set of optimal task frequencies. Each output provided bounds within which maintenance tasks could be adjusted, thus offering a balanced approach to risk and cost management.
The outputs were primarily used to allow asset strategists to justify changes proposed to planning teams around maintenance task frequencies. By adopting the optimal frequencies recommended by the solver, Unitywater can compy with their risk management framework while also maintaining cost efficiency.
Unitywater’s data, coupled with unique asset models, was processed through our specialized asset modeling methodology. Following this, the assets were run through the task frequency optimization solver. The results were subjected to validation, discrepancies were identified and corrected, and subsequently, the model and underlying data were updated. This iterative process culminated in a final set of actionable recommendations.
Despite acknowledging areas needing improvement in data and model valuations, the project was deemed a success by Unitywater. It demonstrated the practical applicability and robustness of the value framework and highlighted several opportunities for future enhancements.
