Applying sequential bifurcation and feed-forward simulation to reduce the prescriptive response time of a digital twin

Abstract

Recent advances in digital twin research have increasingly emphasized their role in real-time decision support for production and logistics systems, yet significant methodological gaps remain in enabling timely prescriptive analytics. This study addresses these gaps by proposing and evaluating an integrated approach that accelerates a digital twin’s ability to generate effective, computationally feasible decisions under dynamic operating conditions. This research envisions the digital twin as a decision support system capable of timely, relevant, and effective decision-making. To achieve this promise, the digital twin can describe its environment, predict future events, prescribe a course of action, and then actualize this course of action in the physical system. To ensure the decision is timely, relevant, and effective, the ability to quickly prescribe courses of action becomes paramount. This research addresses the fundamental trade-off between decision speed and depth of insight. Navigating this trade-off is facilitated by effective use of models to produce timely information, rapid identification of critical factors that drive decisions, and implementation of decisions when sufficient insight is available.

To address this need, the study integrates sequential bifurcation and feed-forward discrete event simulation to accelerate the digital twin’s ability to prescribe effective actions in real time. Sequential bifurcation efficiently narrows the decision space by identifying key input factors, while feed-forward simulation decomposes long simulations into shorter segments to enable earlier, partial decisions. Together, these methods significantly reduce prescriptive response time without compromising decision quality, supporting the digital twin’s role as a responsive decision support system. The paper also formalizes the trade-off between decision speed and depth of insight, offering a layered decision-making strategy to balance these competing demands. Experimental results demonstrate substantial improvements in decision timeliness and operational performance.