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Enabling automated engineering’s project progress measurement by using data flow models and digital twins

Ebel, Helena; Riedelsheimer, Theresa; Stark, Rainer

A significant challenge of managing successful engineering projects is to know their status at any time. This paper describes a concept of automated project progress measurement based on data flow models, digital twins, and machine learning (ML) algorithms. The approach integrates information from previous projects by considering historical data using ML algorithms and current unfinished artifacts to determine the degree of completion. The information required to measure the progress of engineering activities is extracted from engineering artifacts and subsequently analyzed and interpreted according to the project’s progress. Data flow models of the engineering process help understand the context of the analyzed artifacts. The use of digital twins makes it possible to connect plan data with actual data during the completion of the engineering project.
Published in: International journal of engineering business management, 10.1177/18479790211033697, Copernicus