Investigation of automation opportunities in warehouse management in construction supply chains using convolutional neural networks

Abstract

The building industry is strongly reliant on materials, which account for 55%-60% of its expenses. However, due to outdated and time-consuming approaches, inadequate inventory management prevails. This is where developing technologies such as Deep Learning (DL) might help uncover solutions. Surprisingly, very little scientific research on DL has been conducted for this purpose. As a result, this study looks into the prospect of automating construction warehouse management by employing CNN for object detection and counting. During the initial investigation, 23 studies out of 26 used Convolutional Neural Networks (CNN) for image processing and object detection. Secondly, a model was developed and compared its accuracy to that of human counting and discovered that the model outperformed people. Industry professionals were interviewed to discuss the findings. Industry professionals highlighted the advantages and disadvantages of using such an automated system in construction warehouses. In conclusion, this study shows that the CNN base model outperforms people in counting materials, and the proposed automated inventory management system has significant industry potential.