Quality testing of BIM models in Factory Planning
by Franziska Wagner & Marcel Potthoff | February, 26th 2026
Digitalisation has profoundly changed the way buildings are planned, constructed and operated in recent years. In traditional building construction, digital planning using the Building Information Modelling (BIM) method is now standard practice. However, in factory planning, one of the most complex forms of construction, this approach is still in its infancy. Yet it is precisely here that the need is particularly great: production systems, material flows, technical supply facilities and building structures must not only be carefully coordinated, but also function reliably in highly dynamic environments. These are characterised by high time pressure, limited resources and constantly increasing demands for efficiency and flexibility.
This is where the Building Information Cloud (BIC) comes in with quality checks that have been specially developed for factory planning. These are based on findings and methods from the FaBIM research project, which was launched with the aim of applying BIM consistently throughout the entire life cycle of a factory – from early planning and implementation to operation.
While the first BIM-GLW blog post already showed how BIM can holistically change factory planning, this post focuses on one key aspect: systematic and automated quality assurance for models. Without reliable, structured and automatically verifiable data, digital planning remains piecemeal.
Why Quality Checks are so crucial in Factory Planning
A factory is a highly complex organism in which buildings, machines and logistics systems cannot be considered separately from one another. Every machine places demands on the supporting structure, supply technology or spatial structure. Traffic routes must meet certain flatness tolerances so that driverless transport systems (DTS) or autonomous mobile robots (AMR) can operate safely and without disruption. Media connections must be available in the right quantity, in the right place and with the appropriate performance so that production systems can be operated reliably.
Even small errors in these model interfaces can have massive consequences: a machine whose foundation is not sufficiently stable, a traffic route whose floor does not meet the required tolerances, or a missing media supply line that delays the entire start of production. In classic coordination rounds between architects, structural engineers, building services engineers and factory planners, such problems often only become apparent at a late stage – and then lead to expensive redesigns or even structural adjustments.
Digital quality checks provide a remedy here. They identify errors at an early stage, before they become a risk in the real project. While DIN standards, the Model Building Code and workplace guidelines are primarily checked in building construction, factory planning requires additional criteria. Classic standard checks are not sufficient when it comes to machine weights, load distribution areas, technical media or logistical flatness requirements.
This gap was addressed in the FaBIM research project – and the Building Information Cloud has translated the logic developed there into concrete, technically feasible sets of rules.
FaBIM as the basis for new digital Testing Mechanisms
The FaBIM research project aimed to consistently transfer the potential of BIM to factory planning. Not only were processes described, information requirements defined and a common understanding of data developed, but specific quality requirements were also derived. BIM-GLW, Fraunhofer IGCV, ifp consulting and Kohlbecker worked together to determine what information is required in which planning steps and how this information can be represented in digital models.
The Building Information Cloud supported the consortium in its development with its expertise in model-based testing processes and took on the software implementation of the newly developed quality rules. This resulted in a decisive added value: specific requirements from factory planning can now be checked automatically on the basis of IFC files – directly on the BIC software.
The project resulted in three key quality checks:
- checking the stability of production systems,
- checking the evenness of traffic routes,
- and checking the media supply.
Each of these checks addresses a critical aspect of factory planning and ensures that planning reliability is already established in the digital model.
Quality Testing: Stability of Production Systems
The stability of production systems is a key factor in operational safety and personal protection in manufacturing. Large-format or moving systems in particular exert high forces that are transferred to the ground and the fastening elements. Inadequately coordinated foundations or faulty anchoring can quickly lead to damage or safety risks.
As part of FaBIM, the ‘Stability of Production Systems’ quality test was therefore developed to ensure that machines and systems can be installed correctly and safely on the intended foundation. Like all tests, it is based on a comparison between the technical model of production system planning and the technical model of object planning.
First, the system checks whether the respective plant has been correctly modelled on the ground. If the production system is not flush with the floor slab or foundation in terms of height (z-direction), the check issues a corresponding error message. This allows modelled ‘floating positions’ of machines or incorrect height references to be identified immediately.
In the next step, the system reads the relevant attribute values – including the area load (kN/m²) and the total weight (kg) of the systems, as well as the maximum load-bearing capacity and permissible individual loads of the assigned foundations. If any information is missing, the quality check will explicitly point this out. For a reliable check, it is advisable to perform an LOI (level of information) check in advance to ensure that all necessary information is stored in the model.
Finally, the static requirements of the systems are compared with the permissible loads of the foundations. If the area load or weight of a system exceeds the maximum permissible load-bearing capacity, the check generates a warning message. This feedback allows the affected system to be located and checked directly in the model. If all requirements are met, a corresponding positive test result is issued. In this way, the test helps to identify and remedy stability risks at an early stage – long before the first foundations are poured or machines are installed.
Quality Inspection: Evenness of Traffic Routes
A smooth flow of materials is the backbone of any modern production facility. The evenness of traffic routes plays a central role, especially in highly automated factories where AGVs or AMRs are used. Even slight unevenness in the floor can lead to malfunctions, downtime, increased wear and tear or quality fluctuations.
Due to this high relevance, the ‘flatness of traffic routes’ quality check was developed as part of FaBIM. It enables early and automated checking of the floor condition within the BIM model and verifies whether the modelled surfaces comply with the defined tolerances and requirements for the operation of automated transport systems.
As with the first quality check, the first step is to verify that the relevant components have been modelled correctly. In this case, the so-called clearance profile, which serves as a reference object for the flatness requirements, must be modelled flush with the floor covering of the subfloor. If no clearance profile is available or if it is not flush with the floor, the check issues a corresponding error message.
In the next step, the flatness requirements are mapped using stored standards and guidelines. Currently, the quality check takes into account DIN 18202 (tolerances in building construction) and a VDMA guideline with specific requirements for floors in conveyor technology and logistics, among other things. These standards serve as a basis for evaluating the required flatness in the model and must therefore be assigned to both the floor covering and the clearance profile of the traffic route.
In the final flatness comparison, the floor flatness stored in the model is compared with the defined requirements from the standards. A ranking from lower to higher requirements is taken into account. If the clearance profile has a higher requirement than the floor covering can meet, the check generates an error message. This feedback allows the affected area to be directly located and checked in the model. If all requirements are met, a corresponding positive test result is issued.
The test thus helps planners to identify critical areas at an early stage – and ensures that logistics systems do not fail later due to insufficiently level floors.
Quality Control: Media Supply
A stable and needs-based media supply is the basis for reliable operation of modern factories. Machines and systems require a variety of different media, such as electricity, compressed air, water and technical gases, as well as sewage pipes and data interfaces. Even the smallest planning errors – such as missing connection points, incorrectly dimensioned pipes or insufficient capacities – can lead to delays in production start-up or costly adjustments during ongoing operations.
In order to identify such risks at an early stage, the ‘media supply’ quality check was developed as part of FaBIM. It automatically checks whether all the requirements for the building’s connection points stored in the BIM model are met.
This quality check is the most complex of the check rules developed within the framework of FaBIM and can be divided into several check runs. A detailed explanation of the attribute check is not provided here, as it is performed in the same way as the previous quality checks.
The first test run checks whether the media supply required by the production systems is fundamentally guaranteed. To do this, the medium and the flow direction of the media connection (FlowDirection) are read out at the connection points of the production systems. It then checks whether there is a suitable connection point on the building side with a complementary flow direction in the same room. If this is not the case, the quality check issues a corresponding error message indicating for which medium in which room the supply or disposal is not guaranteed.
In the second test run, the connection values of the media supply are checked room by room. This involves comparing the total power consumption (in kW) or the total wastewater production (in l/h) of the production systems with the available capacities of the building connections. In addition to the total check, the maximum values of individual connections are also compared. This allows the system to detect, for example, if the wastewater production of a system exceeds the capacity of a single floor drain and needs to be distributed across several connection points. If one or more requirements are not met, the test generates an error message that clearly identifies the rooms and connection points affected.
Finally, in the third test run, the distance between the connection points of the systems and the corresponding connection points on the building side is checked. Maximum permissible distances can be defined individually for each medium. If the stored limit value is exceeded, the test issues a warning message. This allows unnecessarily long pipe runs or unfavourable positioning to be identified at an early stage.
If all three test runs are successfully completed, a corresponding positive response is issued. This provides clear, digital confirmation that the planned media supply meets the requirements of the production systems.
Conclusion and Research Perspectives: Paths to the Future
The aim of the quality checks developed is to identify and correct any irregularities or planning errors during the digital planning phase before they lead to costly adjustments or operational disruptions in the real factory environment. The use of digital checking mechanisms can significantly increase the reliability of planning data and sustainably improve the quality of the resulting factory implementation.
The research results to date cover only a fraction of the multitude of interfaces between the various planning domains. However, they show that automated quality checks have great potential to make the planning process not only more efficient but also more transparent.
At the same time, new perspectives are opening up for research and development – for example, through the use of AI-supported testing algorithms, semantic model interpretation or learning systems that independently detect planning errors and generate proposed solutions. In the long term, this could lead to a consistently digitally secured planning process in which factory models become not only a database but also an active component of quality assurance.
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