Data-driven cycle time prediction of fitting and welding stations in steel fabrication

Abstract

The construction industry's lack of materials, resources, and financial assets streamlined a shift toward using digital lean principles to obtain precise management over the limited resources. Steel fabrication companies rely heavily upon the enormous equipment to get promising results.  However, implementing lean principles in the fabrication process is not straightforward due to the non-repetitive nature of steel construction products. Hence, the time-based modeling for such a process lacks accuracy and reliability, especially for manual steel fabrication processes.  Accordingly, the current study aims to achieve a practical and accurate estimation of fabrication time aspects.  This study targets modeling manual steel fabrication processes (fitting and welding workstations) in terms of processing times (cycle time and value-added time). The proposed approach builds a machine learning (ML) model to estimate the identified processing time aspects. For performance assessment, the typical correlation analysis and linear regression (LR) approach was used as a benchmark to quantify the ML model's pros and cons in terms of practicality and accuracy. The required data source for this study is a steel fabrication industry partner. The results of this study show ML superiority in accuracy over LR processing time predictive models, particularly when predictive parameters increase ML presents a 13.2 % improvement in mean squared error compared to the LR predictive model. LR models need fewer data and are not computationally expensive like ML models, making them more practical. Additionally, the study introduces a precise and practical time estimation approach. Such an approach provides precious input for simulation models which support evidence-based decisions and benefits quantification of plans.