Compare independent real-life operational performance data of turbine technologies, manufacturers and models. Perform scenario modelling of performance over time for specified turbine options to enable selection of optimal turbines for a given project.

Benchmark performance of wind energy assets against expected performance as generated by WindRAT and against observed performance of predefined peer groups. Identify over/under performance and facilitate margin improvements. Identify problematic components as key risk drivers to guide inspection and review.

Increase accuracy of asset lifecycle estimates through utilisation of Sciemus operational data to support asset management, O&M strategy and critical spending decisions such as implementation of condition monitoring, proactive/predictive maintenance.

Use advanced statistical methods to determine the relationships between maintenance and performance and the effect on profit to enable identification of optimal maintenance effort for maximum benefit. Design more effective O&M programs to reflect utilization, environmental factors, and inherent component reliability statistics.

Identify problematic components and key risk drivers to focus risk management activity. Put in place required measures to mitigate risk prior to support deployment of risk transfer and warranty programmes.

Use quant-based analysis to achieve optimal levels of risk retention and transfer. Objectively quantify risk within given retained and transferred layers to inform target premium and terms.

Enhance understanding of warranty coverage across the operational life-cycle; for in-warranty, end-of-warranty, and extended warranty assets. Examine warranty options against the risk profile of the asset to determine optimal levels of coverage and robust cost-benefit decision making. Review existing warranties to identify overlaps and gaps in warranty and insurance coverage