Cost-benefit analysis of automating modular construction manufacturing for affordable housing - Ouda, Enaam; Haggag, Mahmoud (Frontiers in Built Environment, 2025)
The potential of modular construction to deliver affordable housing can be enhanced through automation. However, high upfront investment for automation raises concerns about its economic feasibility. This study aims to evaluate the economic viability and sustainability of automating modular construction manufacturing for affordable housing delivery by conducting a cost-benefit analysis. The study employs a two-stage quantitative approach. The first stage involves using simulation results from a previous study conducted by the authors to analyse the production time of manual and automated factory setups, while the second stage entails collecting and comparing the costs and metrics associated with both setups, focusing on labor wages, robot and machinery costs, and energy consumption. Results show that automation reduces production time by almost 40% per unit, labor wages by 69.7% per unit, and energy consumption cost by 11.6% per unit. While adopting automation requires a high investment in robotic systems—approximately 321% higher than the cost of manual setup—and increases annual maintenance costs by the same amount, the long-term savings and increased efficiency demonstrate the economic viability of automation, with an estimated payback period of around 3 years. The study concludes that automation offers substantial economic and operational benefits for modular manufacturing, offering valuable insights for stakeholders in the modular construction industry aiming to optimize their production systems for affordability and sustainability. The analysis is limited to the direct costs and benefits of the main manufacturing process, excluding idle time, indirect costs, and environmental and quality impacts. Future research should expand the model’s scope to include full factory operations, MEP integration, logistics, and full assembly; apply probabilistic sensitivity analysis to capture uncertainty; and assess the environmental and life-cycle impacts of automation.