Digital project management in machinery and plant engineering
In machinery and plant engineering, just as in the Digital Twin, there is sufficient data. Nowadays, digital planning generates perfect 3D CAD data, simulated in virtual commissioning and with detailed schedules or work breakdown structures and extensive material parts lists. The biggest challenge now is to take this Digital Project Twin from the drawing board to the analog construction site.
No matter how well a project has been planned. On-site conditions or humans create volatile factors that are difficult to consider in advance. In the same way, a Digital Project Twin requires that real project data be permanently captured and added to the virtual copy. Only this way is it possible to obtain as accurate a picture as possible of the current progress and its deviations, problems, and processes.
Now, what is the difference between Twin and Twin?
The Digital Twin aims to reproduce the real factory in production in order of being able to experiment with the virtual clone. The Digital Project Twin, on the other hand, pursues the goal of reflecting as closely as possible the exact project reality of the machine or plant to be built by the time the project is completed. Since the conditions and problems in the project are constantly changing up to the start of production, this is an equally demanding task. However, while modern technologies, such as the automatic creation of point clouds, are already very advanced for the Digital Twin, the foundations for the Digital Project Twin often have to be laid first.
It is obvious that a Digital Twin aims at obtaining a 1:1 image, reducing production times or saving material. Here, the Digital Project Twin rather pursues the intention of saving resources during the set-up process and to show deviations of the reality from the project planning as early as possible. Be it through time savings during assembly, in the collaboration between client, supplier and sub-supplier or indirectly in the project management expenditures itself. It is also conceivable to save paper by digitizing checklists, test or measurement protocols and acceptance processes.
One discipline, however, is shared by both twins. In the area of predictive maintenance, for example, it is possible to calculate failure probabilities through the Digital Twin and with the help of artificial intelligence (AI). A Digital Project Twin could also explain the causes of later failures in production through early, consistent data collection, for example in defect management in the ramp-up phase. Today, there is often still a great deal of confusion about the origins here.