Solution

Helicopter Flight Planning

Helicopter Flight Planning

Transitioning from Reactive Requests to Proactive Scheduling
Transitioning from Reactive Requests to Proactive Scheduling
Three workers in orange safety suits walking toward a yellow and white helicopter on an airfield, with trees in the background.
Three workers in orange safety suits walking toward a yellow and white helicopter on an airfield, with trees in the background.

Moving from last-minute changes to stable, optimised monthly flight plans — raising utilisation and cutting waste.

Client(s)

BSP

Sector

Oil & Gas

Challenge

Logistics

Decision lens

Activity Planning

Product

AIS

Geography

Asia-Pacific

The challenge

The challenge

Brunei Shell Petroleum (BSP) relies on helicopters to transport staff to offshore installations. Despite reducing its fleet, seat utilisation remained stubbornly low at only ~60% - even though demand for seats was higher.


This mismatch meant not all high priority offshore work could be executed as planned. The root cause lay in a reactive, demand-driven planning culture: flight requests came late, changes were constant, and planners had little visibility into the trade-offs or impact of decisions. The result was inefficiency, wasted capacity, and higher operational costs.


To break this cycle, BSP wanted to shift from reactive flight requests to a more fixed-schedule approach, creating stable monthly flight plans based on forecasted demand. But given the many possible flight combinations, strict rules, and maintenance requirements, doing this manually was slow, complex, and unsustainable.

Brunei Shell Petroleum (BSP) relies on helicopters to transport staff to offshore installations. Despite reducing its fleet, seat utilisation remained stubbornly low at only ~60% - even though demand for seats was higher.


This mismatch meant not all high priority offshore work could be executed as planned. The root cause lay in a reactive, demand-driven planning culture: flight requests came late, changes were constant, and planners had little visibility into the trade-offs or impact of decisions. The result was inefficiency, wasted capacity, and higher operational costs.


To break this cycle, BSP wanted to shift from reactive flight requests to a more fixed-schedule approach, creating stable monthly flight plans based on forecasted demand. But given the many possible flight combinations, strict rules, and maintenance requirements, doing this manually was slow, complex, and unsustainable.

Solution

Dashboard showing helicopter operations on August 10 with statistics on requests, passengers, crew changes, trips, demand met, and utilization rates. Includes bar and line charts for demand and helicopter usage, a timeline schedule for two helicopters, and a 3D map of flight routes.
Dashboard showing helicopter operations on August 10 with statistics on requests, passengers, crew changes, trips, demand met, and utilization rates. Includes bar and line charts for demand and helicopter usage, a timeline schedule for two helicopters, and a 3D map of flight routes.
Dashboard showing helicopter operations on August 10 with statistics on requests, passengers, crew changes, trips, demand met, and utilization rates. Includes bar and line charts for demand and helicopter usage, a timeline schedule for two helicopters, and a 3D map of flight routes.

We built an AI-assisted flight planning MVP that allows BSP to generate optimised monthly helicopter schedules quickly and transparently.


Instead of manually stitching together flight plans, planners can now upload demand files, set rules and priorities, and let the algorithm generate feasible schedules - all within the constraints of helicopter availability, helideck access, and maintenance windows.


The tool provides full visibility: planners can analyse daily flight loads in charts, drill down into individual flights in Gantt and map views, and see which requests spill over. They can adjust parameters, re-run the plan instantly, and download outputs in BSP formats.


Crucially, planners remain in control - they can override settings, re-prioritise requests, and refine the plan, ensuring a smooth transition to a more fixed, proactive scheduling culture.

Results

Higher utilisation

optimised flight schedules raise seat usage while reducing wasted capacity.

Faster planning cycles

monthly schedules created in hours instead of weeks of manual work.

Confidence in decisions

transparent trade-offs and override options help BSP shift from reactive requests to proactive planning.

Results

Higher utilisation

optimised flight schedules raise seat usage while reducing wasted capacity.

Faster planning cycles

monthly schedules created in hours instead of weeks of manual work.

Confidence in decisions

transparent trade-offs and override options help BSP shift from reactive requests to proactive planning.

Results

Higher utilisation

optimised flight schedules raise seat usage while reducing wasted capacity.

Faster planning cycles

monthly schedules created in hours instead of weeks of manual work.

Confidence in decisions

transparent trade-offs and override options help BSP shift from reactive requests to proactive planning.

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Want to learn more about this solution? Get in touch and ask for a demo.

Want to learn more about this solution? Get in touch and ask for a demo.

Want to learn more about this solution? Get in touch and ask for a demo.

Want to learn more about this solution? Get in touch and ask for a demo.