There’s a lesson to be learned from Tesla and its ascent to the status of most valuable car manufacturer in the world. The leapfrog, often experimental progress made by Tesla and CEO Elon Musk, is based on select few methodologies that originate in the world of software development. As we look at the top four opportunities for manufacturers today, we must recognize why these methodologies are so important as well as what it will take for manufacturers to adopt them.

Capabilities should improve throughout lifecycle of vehicle ownership

Most software products and the Tesla in-car experience are constantly being improved and evolved – even after the vehicle leaves the factory floor. Ongoing improvement of fundamental capabilities (e.g. ability to unlock autonomous driving) as well as extension of in-car infotainment with newer, better versions makes the ownership of the vehicle feel fresh and increase the value. Furthermore, these improvements are beamed to the customer through software updates at no cost. Manufacturers need to establish and improve software development best practices that would allow this type of evolutionary approach to in-car experience throughout the lifecycle of ownership.

Modular design should allow for driver experience to receive more focus and funding

Manufacturers should adopt best practices from agile software development – the recognition that all progress will be iterative and based on learning along the way. In other words, modularization of the vehicle is essential so that parallelized efforts can be concurrently carried out as the overall machine is being designed and assembled. For example, a lot of the outlandish capabilities developed for the Model S and Model 3 were a result of experiments. Experimentation, however, can only flourish in organizations that do not punish failure, and empower their teams to reach beyond their comfort zones instead.

Systems of propulsion, suspension, in-cabin comfort, and entertainment have largely stagnated unless forced to evolve by legislative pressure and emissions control. Manufacturers should consider modular design so that more time and effort can be spent on fine-tuning the in-cabin experience versus the commoditized capabilities of the vehicle.

The vehicle should get smarter with use

Historic driving and usage data opens immense possibilities for machine learning capabilities to further enhance the driver experience. For example, Tesla models automatically guess if an individual is going to work based on day of week and time of day. Access to the driver’s phone calendar can allow vehicles to anticipate the needs and recommend routes with least traffic, potential parking garages at destination, as well as actions that may need to happen prior to the destination.

Integration with other smart systems, such as home security, heating and cooling, smart fridges, and other gadgets open up opportunities to identify tasks, remind of important changes, and improve the quality of life for the owner.

Ownership and maintenance should be predictable and transparent

Unexpected or frequent failure, even if under warranty, is one of the most frustrating aspects of car ownership due to the disruption and financial burden that it carries. Car manufacturers have circumvented negativity through all-inclusive maintenance plans (e.g. BMWs 50,000 mile all-service-included plan), however this still does not provide total trust and reliability of the relationship between the owner and the vehicle.

A better alternative is to apply yet another best practice from software development which is related to Unit Testing. Unit Tests in software help engineers define the expected functionality of a module and then very quickly identify and report if a feature is not behaving the way it is supposed to.

An example of the ideal car experience should include diagnostic functions and report to the driver in human-accessible format the health and status of all subsystems. The standard “check engine” light is the most ominous and uninformative warning that a vehicle can possibly throw, yet not a single manufacturer has invested the time and effort to proactively diagnose and communicate experienced issues. Lastly, predictive maintenance – the vehicle’s ability to identify when it is about to fail – would elevate the experience to yet another level.

To summarize, manufacturers must adopt the best practices of software development and use the build-measure-learn loop to rapidly refine the vehicle. The legacy model of designing, sourcing, building, and shipping needs to change if automakers want to retain market share against disruptors like Tesla.

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