1st Interdisciplinary Workshop on

Firm-Level Supply Networks

Reconstruction and Dynamics

Vienna, 28. 30. November 2022


Supply chains underpin much of the functioning of the economy, including critical sectors such as food, medicine, defence and engineering. Increased complexity, the adoption of lean management principles, and interdependence have resulted in increased vulnerability to disruptions that often spread through global networks, halting the flow of materials and services. Supply chain risk has been identified as a top business risk factor in a variety of industrial sectors and has been prioritised by numerous governments. The climate crisis and increased geopolitical instability are expected to exacerbate the challenges faced by production networks.

Although there are already long-established academic fields that investigate supply networks, research is scattered across multiple disciplines, ranging from Manufacturing Engineering and Supply Chain Management to Complex Systems and Macroeconomics. To tackle modern challenges, however, the models that focus on understanding the supply chains of a single firm are too constrained in scope, and traditional macroeconomic models are too aggregate.

Our view is that we need a new generation of models founded on detailed, firm-level data which incorporate realistic firm behaviour, but also operate on a large scale, making predictions of economy-wide effects. What firm-level data on supply networks is available, and how can we reconstruct the missing pieces? What do we know about firm-level supply chain behaviour, and what do we learn from specific industries? What have we learned, in practical applications, from models calibrated on firm-level supply networks data?

In this first interdisciplinary workshop on firm-level supply networks, our aim is to answer these questions by marrying “micro” and “macro” perspectives, providing the foundations for a longer-run research agenda devoted to the development of realistic models based on global firm-level supply networks datasets.


Complexity Science Hub

INET, University of Oxford

Complexity Science Hub