Ferrari, established in 1939, is a renowned luxury car manufacturer recognized for its commitment to delivering unique, high-quality, and personalized driving experiences with a strong focus on safety. Given their dedication to staying at the forefront of performance and consistently offering new and reliable models, they decided to obtain an advanced and smart testing system.
In pursuit of this goal, the customer selected our company as the vendor for this initiative. Andersen was chosen due to our relevant track-record and knowledge across all relevant domains.
In general, we were entrusted with:
The project aimed to address a critical aspect faced by the customer – i.e. their need for thorough testing rounds when developing new car designs.
Such testing is executed by the customer’s dedicated department, equipped with dyno stands (short for “dynamometer”), where various readings and characteristics are collected, including:
These stands are sophisticated setups that feature numerous sensors to measure acceleration, emissions, and other parameters.
A significant challenge experienced by the customer was caused by the susceptibility of this equipment to malfunctioning issues. Even a single failure in a single sensor could lead to a testing lockdown, hindering development processes. Despite the equipment featuring telemetry to monitor and self-diagnose, including capabilities like a rotating wheel shaft and voltage sensors, this limitation persisted.
Thus, the primary objective was to leverage the collected data to establish a predictive maintenance system for the department responsible for testing new cars. For the customer, that would signify a major step forward, as compared with habitual reactive (post-breakdown) and planned (scheduled at certain intervals) testing programs.
As a result of our involvement, a revolutionary predictive maintenance system has been implemented. This system relies on a self-learning data model utilizing ML. The model analyzes deviations, self-learns over time, and enhances the accuracy of its forecasts with each breakdown. These forecasts will generate recommendations for repairs and replacements before any breakdown occurs, optimizing the efficiency of the testing process.
Discovery phase activities
The Discovery phase initiated by our experts sought to:
The Discovery phase itself, which was six weeks long, involved a dedicated PO assigned by the customer, and the following staff members assigned by our company:
Discovery phase deliverables
Joint efforts made by Ferrari in collaboration with Andersen resulted in obtaining the following high-quality deliverables constituting a viable and promising foundation for the development stage.
Since this project was envisioned as an ML-driven initiative from the outset, data-related project facets were of particular importance here, including:
Following the completion of the Project Discovery phase, our tech specialists initiated the development of the planned system. In order to ensure timely and budget-friendly progress, we proposed a managed delivery framework. This approach strikes a balance between budget management and flexibility in feature implementation, enabling the estimation of scope with necessary adjustments in response to blockers, insufficient detail, lack of information, and other factors.
This has empowered the customer to capitalize on the following advantages:
The entire development process was smooth and fully transparent for the customer, with regular meetings, submitted reports, and knowledge transfer sessions. As a result, the customer was aware of the development progress of the ML-driven system from the very beginning. This transparency greatly facilitated their efficiency in using it. As of now, while some ML activities are still in progress, the dyno stands solution and its algorithms are already contributing to optimized testing workflows for new car designs.
Thanks to our contribution, the following results have been achieved:
What happens next?
An expert contacts you after having analyzed your requirements;
If needed, we sign an NDA to ensure the highest privacy level;
We submit a comprehensive project proposal with estimates, timelines, CVs, etc.
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