Pricewaterhouse Coopers

THE CHALLENGE
Planning a 170 km Arabian oasis of shimmering glass and steel

The planned emissions-free Saudi eco city would feature distinct city modules, each replete with complex assets such as housing, offices, schools, hospitals and more - while preserving 95% of nature in an area spanning vast swathes of the Saudi Arabian region.

As part of the monumental feat of civic engineering required to pull-off a truly unprecedented project, PwC - enlisted by NEOM - sought BSC global’s assistance to ensure each city module underwent the most meticulous planning able to couch for an inescapable set of limiting factors, including floorspace, population capacity and, of course, cost.

CONTEXT
Gaining quick traction amid the sands of time

An already tall order, the project brief was made all the more challenging still, owing to an unforgiving timeline, investor expectations, plus the intricate complexities inherent in confidently allocating the right number and combination of planned assets and facilities to each and every city module.

SOLUTION
The science part: Combining Qerent & ‘Genetic Algorithms’

To meet the requirement, BSC Global set about producing a unique, customized Qerent model based around a Value Framework specified by PwC. This Qerent model would incorporate what’s known as a Genetic Algorithm, designed to optimise city-asset allocation within the defined set of constraints, while maximising a projected benefit score calculated using key benchmark metrics such as Internal Rate of Return (IRR).

Finally, the Qerent model, never-before designed or deployed, would export key results that would surface for intuitive interpretation on NEOM’s own analytics dashboards.

SUMMARY OUTCOMES
Deep, detailed insight made beautifully accessible

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The final Qerent model, custom-designed by BSC, continues to help PwC and NEOM’s leadership team access previously-impossible, meaningful insights for deciding optimal placement of city assets across diverse scenarios.

Further, the Qerent model, working in unison with the BSC-made ‘Genetic Algorithm’ allows for dynamic scenario planning easily adjusted to factor key constraints and weightings of benchmark metrics.