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Production networks arise from supply and customer relations among firms.These systems are gaining growing attention as a consequence of disruptions dueto natural or man-made disasters that happened in the last years, such as theCovid-19 pandemic or the Russia-Ukraine war. However, data constraints forcethe few, available studies to consider only country-specific productionnetworks. In order to fully capture the cross-country structure of modernsupply chains, here we focus on the global automotive industry as representedby the MarkLines Automotive dataset. After representing this data as a networkof manufacturers, suppliers, and products, we perform a pattern-detectionexercise using a statistically grounded validation technique based on themaximum entropy principle. We reveal the presence of a significantly largenumber of V-shaped and square-shaped motifs, indicating that manufacturingfirms compete and are seldom engaged in a buyer-supplier relationship, whilethey typically have many suppliers in common. Interestingly, generalist andspecialist suppliers coexist in the network. Additionally, we unveil thepresence of geographical patterns, with manufacturers clustering around groupsof suppliers; for instance, Chinese firms constitute a disconnected community,likely an effect of the protectionist policies promoted by the Chinesegovernment. We also show the tendency of suppliers to organize their productionby targeting specific functional modules of a vehicle. Besides shedding lighton the self-organising principles shaping production networks, our findingsopen up the possibility of designing realistic generative models of supplychains, to be used for testing the resilience of the interconnected globaleconomy.

Pattern-detection in the global automotive industry: a manufacturer-supplier-product network analysis