How to scale a 3D concrete printing facility? A stochastic decision-support framework for production investment
Alexander N. Walzer (Zurich University of Applied Science (ZHAW))
Mariia Kozlova (LUT University)
Ashish Mohite (Hyperion Robotics)
Julian S. Yeomans (York University Toronto)
Daniel M. Hall (TU Delft - Design & Construction Management)
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Abstract
The construction sector faces persistent challenges in scaling emerging technologies such as 3D Concrete Printing (3DCP), despite their potential to reduce material waste and accelerate build times. This paper addresses a key barrier to adoption: economic uncertainty in the development and deployment of 3DCP production systems. Drawing on a case study of a commercial 3DCP facility, we develop a three-stage stochastic decision-support framework to guide scaling efforts. The first stage quantifies cost uncertainties in hardware, software, and material systems. The second stage evaluates strategic development pathways under multiple future scenarios. The third stage integrates investment costs to support full cost-benefit assessments. Anchored in the Resource-Based View (RBV), our approach identifies how firms can mobilize technological, financial, and human resources in uncertain environments. Methodologically, we combine Monte Carlo simulations with Simulation Decomposition (SimDec) to enable multivariate cost-benefit analysis. The result is a practical toolkit for managers navigating early-stage innovation in construction production. This research contributes to scholarship on technology adoption, strategic investment under uncertainty, and sustainability transitions in construction.
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