Digitalization-based process improvement and decision-making in offsite construction.

The evaluation of process improvements measures in offsite construction shop floors often relies on experts' opinion, with limited use of empirical data gathered by sensors in real-time. To address this issue, there is a need for methods that integrate expert's tacit knowledge with robust data analysis techniques. This paper describes the application of exploratory data analysis techniques to evaluate improvement suggestions proposed by expert's, supported by data collected by sensors on the shop floor and building information models. The presented method involves a quantitative and qualitative digitalization-based approach where improvement suggestions are modelled and validated though machine learning algorithms and hypothesis testing. The contribution of this study is a method that combines real-time data, building information models, and knowledge modeling from experts to evaluate process improvement on offsite construction shop floors. • A method to assess improvements based on experts input and real-time data. • Machine learning is applied to analyze data from RFID sensors and BIM models. • The automation in workstations is rated based on production balance and efficiency. • Strategies to increase production flexibility are rated using statistical analysis. [ABSTRACT FROM AUTHOR]