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Paper short abstract

Industrial digital twins establish new forms of pre-emptive modelling aimed at the simultaneous integration of remote control and prediction. Drawing on a technography of the an industrial AI platform, the paper examines how digital twin infrastructures stabilize regimes of logistical prediction.

Paper long abstract

Industrial digital twins establish new forms of pre-emptive modelling by aiming at the simultaneous integration of remote control and prediction in industrial practices and processes. Based on bidirectional data processing, models govern physical products, systems, and workflows. In doing so, digital twins stabilize regimes of logistical prediction in which models no longer merely represent futures but intervene in and operationalize them in real time within software infrastructures and global supply chains. Tech companies promote these technologies as technocratic forms of logistical control and prediction that seek to govern processes before they occur, thereby regulating the future in the present.

Beyond techno-libertarian promises and market speculation, industrial digital twins consolidate software infrastructures composed of sensor networks, data bases, industrial platforms, labour, and proprietary data centres. Adopting an infrastructural perspective, this paper examines how pre-emptive modelling through industrial digital twins is co-constituted by industrial software infrastructures. Methodologically, it draws on a technography of a platform that integrates industrial software applications, digital twin models, and developer interfaces to sensor networks and cloud infrastructures. Drawing on developer materials, platform documentation, and corporate publications, the paper analyses how industrial AI platforms structures operational logics and infrastructures of sensing and modelling, thereby stabilizing specific infrastructures and temporal regimes of logistical prediction.