Investigating a DSM/graph modeling approach for the interdisciplinary design of data-centric complex systems – a case study on autonomous public transportation

As systems become increasingly data-centric, interdisciplinary engineering design faces growing complexity and interdependencies. This paper investigates how a combined Design Structure Matrix (DSM) and graph-based modeling approach supports interdisciplinary decision-making by revealing critical data dependencies, compared to standalone DSM or graph models. Based on a case study on autonomous public transportation and expert input, the results illustrate complementary insights enabled by the combined approach and discuss its implications for industrial system design.