In the past, research and development (R&D) would design a new vehicle, then partner with manufacturing to the point of that vehicle’s launch. This operational model no longer applies. As vehicles increase in complexity, manufacturers have the opportunity to continually learn from vehicle sensor and driver data. The expectation is fast becoming that vehicle performance and customer experience are updated and improved – using software – long after the vehicle is driven off the forecourt. In short, R&D today is continuous, with opportunities and challenges at every turn.
Massive amounts of data are collected throughout the lifecycle of a vehicle. But whether from smart machines in factories, sensors in vehicles, or interactions between brands and customers, the majority of this data sits in isolation today. The vehicle moves along a physical path as it is planned, designed, produced, sold, and used by the end customer. But the data generated and used at each point remains behind – rarely if ever impacting any other part of the lifecycle.
R&D must balance long-lead-time electromechanical development with the expectation of frequent updates and new features delivered by software. Risk levels across vehicle portfolios are at an all-time high, and it is practically impossible to effectively assess and understand that risk using experience and human cognitive capabilities alone.
A new, connected model is needed to compete in today’s digital economy. A digital fabric that connects data from disparate processes, to create a complete and accurate picture across the entire enterprise. Many are looking to machine learning and AI as the silver bullet to build and retain competitive advantage. But the truth is that automotive businesses must first create the right context and data environment for these technologies to deliver the intended business value.
Harnessing the data from R&D can help manage risk, predict the impact of changes, reduce project lead times and ultimately reduce costs.
In this brochure, we give you ideas on how to utilize your data in R&D to win the race for the car of the future.