ZF focuses on system solutions based on data

Let’s take the Ball Joint for example. By incorporating the ZF Ball Joint sensor into the vehicle and using the ZF data monetization algorithms, the OEM can now provide new solutions to their costumers enabled by ZF. The OEM can make more money from those solutions and ZF can also benefit from that incremental revenue.

There are two main steps in this process. One is identifying the business opportunities. That’s done through our monetization process which looks into different business problems out there and relating it to data that is available from the components. Then, there is the step of developing the specific algorithm that takes the data and translates it into what the condition of the road is. For that, we use a variety of algorithmic techniques including AI when appropriate.

It is not new in the automotive industry but there are actually new business models associated with it. As an example, the OEM can buy the Ball Joint Sensor from ZF and develop all the algorithms to use the data by themselves. This is the traditional model. What’s new is that we have in the future two more potential models: One is a licensing model. The OEM can license ZF’s IP or ZF’s algorithms, run them in their environment and derive the business solutions from the data to sell them. The other is a service model where the OEM transmits the data to ZF who runs the algorithms, sells the business solutions and splits the revenue with the OEM.

What’s unique about ZF’s approach is that we don’t sell data. We sell solutions based on data. And there are the two business models as we discussed: One is an IP software licensing model and the other is a digital service model.

The supplier understands the data from its systems much better than the OEM. Let’s look at the road condition monitoring algorithm of the ZF Ball Joint Sensor again: It’s much more economically efficient for ZF to develop and validate this algorithm once and license it to the OEM than each OEM developing its own road condition monitoring algorithm. Furthermore, some of the new customer for the data are not the traditional OEM customers anymore, and in this case ZF actually has an advantage in accessing these customers because we understand that data and algorithm better, and can represent a larger volume of vehicles.

Overall ZF’s strategy is to enable data monetization over its entire product portfolio. That will make the systems more competitive because they will be smarter. Our target is to become the strongest partner of all the tier-1 suppliers to OEMs when it comes to data monetization. This provides both a boost to our core business because it will make our components more competitive and it will provide us more direct revenue from data monetization.

When it comes to ZF systems they are not ahead of us because nobody understands our systems better than us. And we can monetize the data for our systems. We also have the advantage of partnerships like those with Microsoft. Some of our competitors decided to build stand-alone platforms all by themselves. We avoided that and focused instead on the algorithms and the business problems, and partnerships with market leaders for platforms. We have been very efficient in our focus on maximizing value and minimizing costs.

In Europe, there is a lot of government-driven initiatives for making data available to suppliers for monetization. In the US, the OEMs are very active in marketing their data capabilities. But all in all, it is early days for data monetization in the automotive industry as a whole. There are no major revenue streams at this point. So, it remains to be seen which approach works better – the centralized data exchanges approach in Europe or the OEM driven approach in the United States.

We started during this year with one customer to go over the portfolio and the capabilities and to establish joint roadmaps for data monetization and we will be expanding the numbers of customers we work with in 2021.