Die synergetische Fabrikplanung dient der Intensivierung der Zusammenarbeit in Fabrikplanungsprojekten. Eine Weiterentwicklung dieses Vorgehens zur Beherrschung wachsender Datenmengen existiert jedoch bislang nicht. Jenseits der Fabrikplanung hat sich für diese Herausforderung das Building Information Modeling (BIM) etabliert. In diesem Beitrag wird BIM der synergetischen Fabrikplanung gegenübergestellt, um Anforderungen und Potenziale bei der Integration in die Fabrikplanung abzuleiten.
Synergetic factory planning is used to strengthen collaboration in factory planning projects. However, there has not yet been any further development of this approach for dealing with an increasing amount of data. Beyond factory planning, Building Information Modeling (BIM) has established itself for this challenge. This paper compares BIM with synergetic factory planning in order to derive requirements and potentials for integration into factory planning.
Modern factory planning requires a holistic perspective taking economic as well as environmental sustainability over the entire factory life cycle into account. As a complex socio-technical system, the factory life cycle consists of multiple life cycles of the inherent factory elements. A holistic understanding of the individual life cycles and their interdependencies is missing for both planning and operation of a factory. Therefore, the goal is to develop a system understanding about life cycle-oriented factory planning and to analyze the contribution of relevant factory elements to the sustainability of a factory. As a result, a knowledge base for life cycle costing and assessment of the entire factory is established using an impact path model. The qualitative model supports factory planners in deriving planning measures for the sustainable design of a factory and in determining data requirements for the quantitative evaluation of the economic and environmental sustainability of a factory. It shows that the production and logistics concepts essentially define the sustainability potential during planning, while the resulting life cycle behavior of the process facilities and workers is responsible for the majority of costs and environmental impacts of a factory. Factory planners must therefore become aware of the implications of planning decisions on factory operation when developing concepts in the future.
Production companies operate in an increasingly dynamic and unpredictable market environment. The ever shorter innovation cycles and the associated decreasing product life cycles require production systems and processes to change continuously. Furthermore, rising complexity in market enfactories through product variety leads to an increased coordination effort in factory operations. To stay competitive, decision making in factory planning as well as production planning and control (PPC) must be done target-oriented and in the shortest time possible. A fundamental basis in this respect is a reliable and consistent database, needed for the overall and holistic improvement of the logistics performance of a company's internal supply chain. However, the collection and preparation of data is associated with a high expenditure of time and thus represents a great challenge. Additionally, required data in practice is often inhomogeneous or inconsistent, which makes it difficult to track ongoing processes precisely. Due to the continuous availability of exact positions at any time, Real-Time Locating Systems (RTLS) offer the potential to provide movement data of essential elements of the production system such as material supply, resources and workforce with comparatively little effort. To demonstrate the resulting potentials for factory planning and PPC, use cases for RTLS have been identified and implemented in the Learning Factory of the Institute for Production Systems and Logistics (IFA). After a short introduction of the IFA Learning Factory and the principle of Real-Time Locating Systems, the use cases in factory planning, factory operation and production monitoring are described. Having summarized the achieved benefits provided for the training participants in the IFA Learning Factory a summarizing conclusion is given, underlining the great possibilities of real-time localization in production environments and providing an outlook on further research activities.
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