One of Lotus F1 Team's car. Screenshot from the team's website.
Formula One (F1) wouldn't be the first thing you would associate analytics to but it is increasingly becoming a data-driven sport.
"We're in the business of high performance and agility, and having the right information supports that," said Thomas Mayer, Lotus F1 Team's (Lotus) COO. "While racing teams in the past relied on the intuition of experienced engineers to build the fastest car, they now use the insights gathered from analysing data from their cars to support their engineers' intuition by removing ambiguity."
An F1 car is never a completed product as the car needs to tuned before each race to meet the requirements of the different race tracks. Moreover, 70 percent of each car produced by Lotus has a limited lifespan. These two factors resulted in the team having to work on 23,500 car parts drawings in last year's F1 season, and Mayer expects this number to increase in subsequent F1 seasons. "To do more in the same amount of time (ie. the length of an F1 season), we need a solution that enables us to collaborate and analyse our data to have increased insights as to when new parts are needed. This would help us establish more educated production cycles, prioritising the right parts in the right order," he said.
The Lotus team thus decided to deploy Microsoft Dynamics AX across its organisation in a phased approach, as part of its partnership with Microsoft that was established in 2012. Currently in the second phase of the roll out, the Dynamics AX is now tied with "everything from design release to consumption of parts on the car at the track, including project-based manufacturing and design engineering," said Mayer.
This has resulted in manufacturing efficiency for the team. "Today, we don't have more parts on hand than we need. Optimally, we're working towards a just-in-time manufacturing process as we have an understanding of where each part is in its lifecycle," he explained. In line with that, the team plans to use predictive analytics to create patterns and matching, as well as leverage machine learning to plough this insight back into a car's development in future.
A speedy network to enable timely decisions
One of Williams Martini Racing's F1 cars. Credit: BT
Since speed is of essence in F1, engineers need to receive and analyse between 60 to 80 GB of raw data in near real time per race to enable them to make timely decisions. This is one of the key challenges faced by all racing teams as F1 is held in various parts of the world, including places that are far from the teams' headquarters. To overcome this challenge, the Williams Martini Racing team (Williams) is relying on BT's high performance network services for connectivity.
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