Why tackle an increasing product complexity with data?

Product complexity, no matter whether within the consumer or automotive industry, increases. This is easily understood if you look at the overall number of domains and the resulting number of interfaces or connections a product has. A modern vehicle includes an increasing amount of domain besides for instance the conventional power train, chassis and interior domains. If the number n of these domains increases, the overall number of „connection“ between these increases with n(n-1)/2 and thus non-linearly. These „connections“ relate to the necessary alignments within the development stages and also the interfaces soft- and hardware have to provide on the road.

Let us assume the vehicle complexity increased with the amount of ECUs it contains – leaving the trend towards a centralized ECU aside. The following plot illustrates that and directly leads to the question: Did your development methods gain efficiency proportional to n(n-1)?


Key to a comprehensive analysis is the underlying data. Not any data, but well structured semantic data from the product life cycles that are already out in the field. These contain all the answers you need, to understand the actual requirements for your product.


To extract the information you need for your upcoming development decision, you need no be able to analyze the data. A brief plot can be a start, but there are more tools to explore the data lake.


Development cycles are getting shorter and that collides with the increasing complexity. Thus, efficiency has to be increased by automating processes where possible and taking development short cuts with decisions wisely derived from data.