Increasing and changing product requirements demand a permanent readiness for efficient factory adaption. Considering necessary construction adaptions as well as conventional construction planning processes in the context of the factory adaptation process, standard planning methods are unable to support in a fast and efficient way. By application of the BIM method a Building Information Model, which contains all the needed information in one database is generated. BIM is a method for achieving targets, such as selecting the right type of construction technology or building material to evaluate the adaptation measure. The challenges by putting all information in one model are dealing with the amounts of data, identifying data quality and determining the current use case which should be examined. In this publication, the necessary data base is identified and implemented into a Building Information Model to investigate the right fastening system for an Industry 4.0 case study. The use case describes an integration of a robot into a factory in the life cycle phase operation to evaluate, which fastening system is most suitable as a part of taking adaption intelligence of a building to a higher level.

The more and more rising complexity of the industrial environment is triggering companies in a way that is more challenging than ever before. Not only are factory planning projects difficult to handle because of the dynamics and complexity also the necessary planning of the accompanied building gets more and more difficult. To handle this complexity and reduce time and effort for planning as a major factor of success the mainly separately done planning aspects needs to be synchronized. This paper will show an approach of a hybrid factory-building planning method in order to be able to shorten planning time and effort. By using a constraint solving technique the necessary planning tasks are aligned partly automatically and will be processed as a useful planning workflow in form of a gantt diagram for the overall project management.

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In the last few years, particular focus has been devoted to the life cycle performance of fastening systems, which is reflected in increasing numbers of publications, standards and large-scale research efforts. Simultaneously, experience shows that in many cases, where fastening systems are implemented – such as industrial facilities – the design of fasteners is governed by fatigue loading under dynamic characteristics. In order to perform an adequate design and to specify the most efficient and appropriate fastening product, the engineer needs to access and process a broad range of technical and commercial information. Building information modelling (BIM), as a data management method in the construction industry, can supply such information and accommodate a comprehensive design and specification process. Furthermore, the application of BIM-based processes, such as the generation of a BIM-model, allows to use the important information for the construction as well as the life cycle management with different actions and time dependencies of the asset and its components. As a consequence, the BIM model offers the potential to correlate different data relevant for achieving the goals of the respective application, in order to ensure a more effective and correct design of the fastening. This paper demonstrates such a BIM-based design framework for an Industry 4.0 case, and in particular, the installation of a factory robot through post-installed anchors under fatigue-relevant loading in concrete.

The focus of this paper is an efficient data usage in order to investigate the economic efficiency of a building element. Decisions in construction management are related to the life cycle of a building. In combination with numerous influencing factors there is a need for a decision support approach, which enables the user to ensure data is available and can be used efficiently to identify the best decision. To meet these challenges, this paper presents a data-based approach for combining different datasets to ensure a comprehensive base for multi-criteria decision support in construction management.