The automotive sector generates enormous amounts of data. And the amount of this data will only continue to grow as self-driving and connected cars collect real-time data about customer habits and preferences. The ability to transform this data into relevant insights depends on the company’s approach to innovation.
Compared to phone applications, malfunctioning connected car software can have a dangerous impact on safety while driving. Therefore, the production and innovation cycles of a vehicle are interconnected and must pass through many quality assurance checkpoints before being sold. But as customers become accustomed to rapidly evolving digital technologies and markets continue to evolve, the automaker and his OEMs need to shorten these cycles without compromising safety and security.
A digital twin is a virtual analogue of the physical vehicle’s software and mechanical and electrical components that can convey real-time inspection data, maintenance history, warranty data and defects, and there are many emerging emerging that can help fill this gap. One of the technologies, says Uvarova. .
To drive continuous improvement in products and services, it means that working methodologies must complement the technologies used in modern software-defined vehicle innovations. Uvarova points out that an agile working methodology that manages projects through iterative phases with cross-functional collaboration and continuous improvement feedback loops fits in with the latest innovation practices and benefits OEMs.
“Many departments need to work together, and they have to work very quickly, to reliably support innovation and bring the latest generation of cutting-edge Software Defined Vehicles to market. in a way.”
What traditional OEMs often lack is cross-departmental collaboration, as many processes continue to operate top-down and are confined to silos.
“Many great innovations come from interactions, collaborations, synergies and sometimes partnerships between different departments of the same company,” says Uvarova.
Data silos, where orphaned processes and data streams cannot be easily shared across departments and phases of operations, often lead to inefficiencies and duplication of effort. Thayer said that historically, many industries, including automotive, have excelled at working in these silos. But working with agility, creating connected products, and getting the most out of the data they generate requires collaboration and data sharing.
