Pioneering people-focused business intelligence tool supports project and asset performance.
h’alt™ is an innovative machine learning-based mentor designed to help wind power specialists make the right decisions at the right time across a project’s lifecycle.
“In the fast-moving renewables sector, people must be adaptable, resilient and equipped to make informed decisions,” says Rakesh Maharaj, Futurist and Chief Technical Officer at h’alt™.
“To deal with the pace at which we’re moving, we have to accelerate the rate at which we recognise the issues people face on the ground,” he says. “And when we can do that, we can address those issues, systemically.”
h’alt™ empowers individuals on the ground to make better, more informed, decisions that enhance performance and mitigate risk. The tool’s game-changing mentor function actively directs busy professionals to expert, role-based content that strengthens both individual and collective decision making.
This content, which focuses on front-end loaded decision making, covers all aspects of a project’s lifecycle and is based on information gathered during real world investigations, audits and transformation projects.
“The power of decision making often lies within informal networks,” says Maharaj. “And with h’alt™, you can leverage that power by informing the informal network, so teams across a project use the same language, and know what factors to consider and what the outcomes should be.”
Unlike most other applications of machine learning technologies in the renewables sector, h’alt™ focuses on human capital, rather than project assets.
“It’s unique in allowing you to understand whether your organisational capacity matches operational demand in terms of your people,” says Khalida Suleymanova, Chief Implementation Officer at h’alt™.
“The intelligence it collects, and the way in which it gathers and feeds back this intelligence, means improvements can be made based on reality, rather than perception.”
As well as supporting holistic in-workflow decisions, h’alt™ monitors and assesses teams’ patterns of engagement. This means it can collect and disseminate valuable data, enabling organisations to identify key risk indicators.
“Using a tech-based but people-focused approach, h’alt™ helps organisations work better together, improves competence and gathers performance-critical data,” says Suleymanova. “As it learns from its users, the system also builds and distributes organisational knowledge, which is vital for on-site decision making in the fast-paced, complex and uncertain environments facing the wind power sector.”
h’alt™ is integrated into Microsoft Teams, making it easy to implement and convenient to access. The tool supports local policies, procedures and work instructions, and allows organisations to link to their own resources and content.
h’alt™ is already being deployed in one of the world’s largest OEMs, and a leading energy association is supporting the platform’s implementation within its jurisdiction.
For more information, please contact:
• Khalida Suleymanova, Chief Implementation Officer: khalida@h-alt.io
• Rakesh Maharaj, Futurist and Chief Technical Officer: rakesh@h-alt.io
Connect with us on our social media channels: https://www.linkedin.com/showcase/h-alt-io/
About h’alt™
h’alt™ is a machine learning-based mentor, integrated into Microsoft Teams, to build high functioning teams. h’alt™ helps renewable energy professionals, experienced or junior, make the best decisions across the asset lifecycle by giving them access to role-based content when they need it.
The tool promotes front-end loaded decision making, uses safety as a driver for performance, and stimulates a unified way of executing projects.
For more information, visit: https://h-alt.io
About the team behind h’alt™
h’alt™ has been developed by the same team of innovators that established ARMSA Academy, which delivers evidence-based learning solutions to improve performance through better informed individuals. ARMSA consultants have been supporting the energy sector in improving effectiveness, efficiency and safety since 1996.
For more information visit: https://armsa.academy
