Abstract

Large-scale complex supply networks emerge as companies procure goods from each other; simultaneously creating value and risk. Although operations literature offers many sophisticated dyadic models and case studies to describe inter-organisational risk and coordination, there is a dearth of knowledge on the large-scale network structure on which firms rely. In this talk we will first explore this gap by assembling an empirical dataset on the global automotive production network and examine how the structure of supply relationships in the network impact systemic properties such as resilience and performance. We will then debate how predictive models can be developed to estimate hidden structure when empirical data is unavailable or incomplete. We will conclude with a discussion on how supply network analytics can be advanced with the rise of digital manufacturing technologies and the challenges involved in such a step change.