The cost of modelling existing industrial facilities currently counteracts the benefits these models provide. 90 % of the modelling cost is spent on converting point cloud data to 3D models due to the sheer number of Industrial Objects (IOs) of each plant. Hence, cost reduction is only possible by automating modelling. However, automatically classifying millions of IOs is a very hard classification problem due to the very large number of classes and the strong similarities between them. This paper tackles this challenge by (1) discovering the most frequent IOs and (2) measuring the manhours required for modelling them in a state of the art software, EdgeWise. This allows to measure (a) the Total Labor Hours (TLH) spent per object type and (b) the performance of EdgeWise. We discovered that pipes, electrical conduit and circular hollow sections require 80 % of the TLH needed to build the plant model. We showed that EdgeWise achieves cylinder detection with 75% recall and 62% precision. This paper is the first to discover the most laborious to model IOs and the first to evaluate state-of-the-art industrial modelling software. These findings help in better understanding the problem and serve as the foundation for researchers who are interested in solving it.