Those insights improve training quality and, if they’re innacurate or biased, vital data can be omitted, which leads to a decrease in team performance, readiness and preparedness.
RAPTOR uses automated simulation and training data analysis to provide individual performance metrics, then uses those insights to identify and generate automated and tailored training recommendations.
Their progress is tracked, ensuring that the team is constantly improving their training skills and performance, all on their own.
RAPTOR meets these challenges by ensuring every training cycle has a feedback loop and by automated individual training recommendations based on training results as well as currency and proficiency.
It also ensures that the evaluation of skills is objective and free from human-error or bias. Self-service means that trainees can analyze their own performance (no instructor needed).
RAPTOR offers global training data storage which means that all training data is saved for long-term trend analysis.
Thus, users also get answers to questions like:
How often do I have train specific skills to be proficient?
How is my proficiency evolving over time
Average training amount required per trainee?
Which specific skills are trained in which scenario?
German Defence Forces
Estonian Defence Forces
Latvian Defence Forces
RAPTOR can be used as a stand-alone solution that takes training data from a variety of sources, either simulation systems or live equipment.
However, it is also designed to integrate seamlessly with SRP, thereby being able to access the entire scenario repository stored in SRP.