Interconnected digital twins and the future of digital manufacturing: Insights from a Delphi study
Marc Van Dyck, Dirk Lüttgens, Frank T. Piller, Sebastian Brenk
Abstract
Abstract Digital twins (DTs) are virtual representations of real‐world entities like production assets, processes, or products. They are updated at a defined fidelity and frequency along the entire life cycle from development and engineering over the production or implementation of a product or process until its usage stage. Interconnected digital twins (IDTs) are DTs shared and connected across organizations with the objective to create holistic simulation and decision models of an entire physical system. In this paper, we investigate how IDTs shape future digital manufacturing scenarios and impact innovation management. We present the results of a real‐time Delphi study, analyzing quantitative and qualitative estimates on a set of 24 projections, forecasting the future of digital manufacturing with a projection horizon towards 2030. Using this data and 22 additional use cases of IDTs in manufacturing companies, we present a baseline scenario where our Delphi panel reached a consensus, representing a likely future of digital manufacturing in 2030. By analyzing projections where our expert panels' evaluations vary widely, we identify key design decisions that may impact innovation management along the dimensions of variation, choice, and control in digital manufacturing. We explain how IDTs will impact external knowledge inflows, the emergence and governance of industrial data spaces, and the potential of data‐driven and AI‐enabled applications for prediction and regulation to drive better decision‐making and continuous innovation.