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The interface between construction and production is an area of research with rising importance given its increasing demand for efficiency gains in factory planning and construction planning processes. In fact, nowadays, it is usual for production and surrounding buildings to be planned separately as independent entities. According to the Laboratory for Machine Tools and Production Engineering of RWTH Aachen University, it is against this background that recent factory planning projects have reported cost increases and time delays due to non-transparent information between different planners. Building Information Modelling (BIM) addresses precisely this problem. However, BIM is barely used in projects for production planning of factories. This is critical since factory planning has to deal with more complex planning parameters (due to the technical building equipment) compared to private housing construction or public building construction, where BIM is already being applied increasingly. In order to close this gap, it is first of all important to create transparency within the individual information interfaces between production planning and building planning. This article addresses this issue and identifies major obstacles in interdisciplinary cooperation between building planners and production planners. For this purpose, an interdisciplinary and partially standardised study has been carried out using questionnaires and partly-open expert interviews. The results show scarce implementation in factory planning projects due to (1) missing maturity level specifications and (2) missing data management standards. Both theoretical and practical implications of this study as well as limitations and future directions for research are discussed.

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The interest of manufacturing companies in a sufficient prediction of lead times is continuously increasing -especially in engineer to order environments with typically a large number of individual parts and complex production processes. A multitude of approaches have been proposed in the literature for predicting lead times considering different data and methods or algorithms from operations research (OR) and machine learning (ML). In order to provide guidance at setting up prediction models and developing new approaches, a systematic review of the available approaches for predicting lead times is presented in this paper. Forty-two publications were analyzed and synthetized: Based on a developed framework considering the used data class (e.g. product data or system status), the data origin (master data or real data) and the used method and algorithm from OR and ML, the publications are classified. Based on the classification, a descriptive analysis is performed to identify common approaches in the existing literature as well as implications for further research. One result is, that mostly order data and the status of the production system are used for predicting lead times whereas material data are used seldom. Additionally, ML approaches primarily use artificial neural networks and regression models for predicting lead times, while OR approaches use mainly combinatorial optimization or heuristics. Furthermore, with increasing model complexity the use of real data decreased. Thus, we identified as an implication for further research to set up a complex data model considering material data, which uses real data as data origin.