Auto development

Accelerating parts design in the technical arms race to gain a competitive advantage

Formula one, Research & Development, Sport

Formula One is made up of two races—one on the track and one in car development throughout the season.

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Goal

Every F1 team is constantly refining its cars, designing and testing parts in an effort to increase performance. Over the eight-month F1 schedule, the most minute improvements can translate to faster speeds and better finishes on the track. In the race against time between races, increased visibility into which parts have the greatest likelihood to clear the stringent design standards and reduce drag is crucial. For one F1 racing team, the goal was to improve car development by using data to streamline the R&D process for every element of the car.

Insight and Action

To break down the design process for auto parts, QuantumBlack combined data from e-mail, CAD, product life cycle management, HR, and finance to map collaboration dynamics and uncover patterns of performance. We applied hybrid econometric/machine-learning methods to build an explanatory model to identify, optimise, and forecast project performance and to determine the impact of individual characteristics, team structure, and project environment on performance. We then delivered an early-warning capability enabling the team to predict project performance earlier and react to it.

Results

  • 18Percentearlier warning on project performance
  • 40Percentimprovement in investment yield

Thanks to this analysis, the F1 team was able to identify the projects that were less likely to be successful—and do it early enough in the process that finite time and resources could be reallocated to more promising parts.