What can mechanical engineering learn from Tesla’s success in monetizing their data? A lot can be learned if you drill down to these principles behind Tesla’s data-driven services.
At first glance, the differences between mechanical engineering and Tesla seem wide. Tesla mass produces electric cars for the mass market, while mechanical engineering builds specialized machines and plant systems for manufacturers, which is about as niche as you can get.
But, a recent Gartner report, What Manufacturers Can Learn from Tesla on Data Monetization, looks past the differences and shows some fundamental lessons that mechanical engineering might use to create and sustain their own revenue-generating data-driven services.
The so-called Industrial Internet of Things is making it easier to collect data. Machine sensors capture all sorts of measurements. So, you have the data. Now what? The temptation is to sell it. But, let’s look at what Tesla does it with its data.
Don’t sell the data – use the data.
Tesla’s cars vacuum up all sorts of data from their cars’ surrounding environment with sensors and cameras. This data is used to develop and refine their self-driving assist system, which Tesla sells to its customers as a value-added service. It’s a simple software update, with no additional hardware installed, yet, it’s sold to customers for additional $10,000.
Tesla turns this stream of data into a new service that triggers customer engagement or enables the customer to get more from their product.
As a machine manufacturer, your products might have sensors gathering data that affect the machine’s performance, like vibration frequency. The data from the machine’s vibration can be analyzed by a machine learning algorithm. That algorithm would monitor the machine’s condition and detect minimal deviations that could impact the machine’s efficiency or health. This data could be sold to your customers as a data-driven condition monitoring service for its machines or plant systems.
Products are Platforms for Data Services
Tesla developed a platform that clearly separates hardware and software. The company heavily invested in powerful vehicle hardware, which bit into their vehicle sales revenue in the short-term. But the vehicle’s software can be updated and upgraded for years, creating a steady revenue stream from value-added services in the long-term.
By allowing customers to drive their vehicles for a longer time, Tesla can offer its current catalogue of data-driven services, while creating new services. This boosts Tesla’s total product profitability, despite the low initial revenue from car sales.
The learning? Plan your product hardware capabilities to enable flexibility and ample opportunities for selling product software upgrades and digital services along the whole product lifetime. Tesla did this with its ‘Acceleration Boost,’ which costs an additional $2,000.
As mentioned earlier, a machine manufacturer can use sensors to collect machine data, like vibrations, and offer a condition monitoring service for a real-time view of the machine.
As historical machine data is built up, another algorithm can analyze the real-time data and find deviations from the historical data that could lead to a machine breakdown. This could be offered to customers as a data-driven predictive maintenance service.
Later, another data analysis algorithm can be deployed on the same sets of data to look for deviations in the machine’s performance that could impact the quality of the product that the machine is producing. That could also be offered as a separate predictive quality service.
Develop a Culture of Innovation
Every organization innovates differently. Tesla has admitted that it doesn’t rely on market research for its disruptive products and services. Instead, the company has created a culture that embraces innovation.
Bringing diverse perspectives together and collaborating on data-driven initiatives can create the right environment for innovation. Accepting failures as necessary learning opportunities on the path to creating successful services is also invaluable.
For mechanical engineering, the most potent method is getting manufacturing domain experts and data scientists into the same room to formulate specific use cases. These use cases are then tested to see if they work.
We’ve developed our own 3-step method for identifying uses cases, testing prototypes, and operationalizing services that have worked for our own customers. Collaboration is a requirement though.
Final Thoughts
Tesla might seem far removed from the specific business demands of mechanical engineering, but there are guiding principles that can be adapted for creating data-driven successful services.
- See your machine or plant system data as an opportunity to create a value-added service
- Create long-lasting products that are also platforms for digital services
- Innovation doesn’t happen in a vacuum – foster a culture of collaboration.
Make these principles your own, and if you need some guidance, contact us for a free consultation.
