Optimized Factory Planning and Process Chain Formation Using Virtual Production Intelligence Max Hoffmann, Kai Kreisköther, Christian Büscher, Tobias Meisen, Daniel Schilberg and Sabina Jeschke RWTH Aachen University IMA/ZLW & IfU Dennewartstr. 27 52068 Aachen, Germany Abstract—The increasing complexity of products creates new challenges in production planning. Hence, the method- ology of process development has to be designed valuable. An innovative approach to reach efficient planning consists in the virtualization of planning processes. The concept of the “Dig- ital Factory” enables a preliminary evaluation of the planning success. In the present work, a framework is presented, which allows for the integration of dedicated applications into an integrative data model to gain a holistic mapping of the production. Using Intelligence approaches, data can be analyzed to provide decision support and optimization potentials. The advantages involved are demonstrated by a production structure planning approach in connection with a process chain optimization. I. I NTRODUCTION In order to stay competitive within an economic en- vironment, which is characterized by globalization and networking, so-called key competencies are indispensable in the process of modern factory planning scenarios. These competencies comprise of leading positions in cost minimization, quality and an individual compliance of the consumer’s interests [1]. In the modern factory planning process, the formation and application of these core competencies contribute to the “concurrent strat- egy” of modern enterprises [2]. According to Frese, the concept of competitive strategy hereby constitutes how the involved institutions will determine their corporate strategic product-market-combinations in order to reach a competitive advantage against the concurrence. In contradiction to the classical procedures of the fac- tory planning process, which are described by [3], modern planning projects have to take into account cost and resource reducing procedures that enable an individual and modular design of planning processes. This demand requires both a quantitatively established support of deci- sions during the planning process provided by Intelligence solutions [4] as well as an integrative mapping of the production process within an overall context of the factory [5] [6]. Originally published in: Enabling Manufacturing Competitiveness and Economic Sustainability (2014), Proceedings of the 5th International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV 2013), Munich, Germany, October 6th-9th, 2013 In practice of factory planning, the specific knowledge of experts plays a decisive role. Based on special expe- rience values, existing planning solutions are evaluated qualitatively in order to adapt the results into tailor made solutions of new planning projects [7]. According to this procedure, ancient planning results and specific knowledge are taken into account to decide, which planning steps have to be taken next. However, this does not lead to sustainable systematics in solving problems of the process design [8] [9]. The gained knowledge cannot be generalized to provide common solutions for similarly natured problems as, during the planning process, concrete decisions are made using specific knowledge that is based on rather qualitative evaluations. This leads to a central problem in the systematisation of production planning. There are only a few approaches which provide a quantitatively based evaluation of the planning success during the dedicated steps of the planning process. These dedicated steps of planning were intro- duced by [10] within the Condition Based Factory Plan- ning (CBFP). CBFP hereby serves encapsulated functions to perform the essential steps of factory planning inde- pendently. However, the interconnections between these planning modules are mostly based on qualitative models and on a subjective assessment made by the factory plan- ner. Under these circumstances, neither a quantitatively based evaluation of the planning success nor a systematic decision support during planning is realisable. A promising approach, which supports the realisation of an active decision support tool, is based on so-called Intelligence concepts. These concepts are designed to pro- cess information in order to gain a deeper understanding of the underlying processes within a manufacturing enterprise [11]. According to Kemper, there is no clear definition of the Intelligence term, but there are a few approaches to define the term using other technologies. Thus, the Intel- ligence concept can help filtering information from huge data amounts, can allow fast and flexible data evaluation, can serve as an early warning system or is capable of saving information and knowledge. In terms of production planning, Intelligence concepts are in charge of collecting historical production data to treat the information in a structured way. Consequently, the factory planner is able to access the information in an appropriate form [12]. To enable the transformation of big data into a struc- tured data model, information has to be gathered from