The Transformable Factory: Adapting Automotive Production Capacities

In the complex field of fast changing market conditions and decreasing predictability of market development, a factory has to adapt its production capacities quickly and with minimal effort. This paper introduces a novel methodology, recommending the best method of capacity planning to change produc...

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Bibliographic Details
Published inProcedia CIRP Vol. 41; pp. 171 - 176
Main Authors Creutznacher, Thomas, Berger, Ulrich, Lepratti, Raffaello, Lamparter, Steffen
Format Journal Article
LanguageEnglish
Published Elsevier B.V 2016
Subjects
Factory
Flexibility
Production capacity
Simulation
Factory
Production capacity
Simulation
Flexibility
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Summary:In the complex field of fast changing market conditions and decreasing predictability of market development, a factory has to adapt its production capacities quickly and with minimal effort. This paper introduces a novel methodology, recommending the best method of capacity planning to change production volumes and to ensure optimal operation in a car factory with respect to several Key Performance Indicators (KPIs), e.g. OEE or energy efficiency. The plant's volume transformability will be increased by not only one, but rather many different possible responses to be analyzed by simulation: the so-called production variants. An algorithm determines the most dominant, pre-analyzed production variants from a data base and visualizes the responding strategies based on the predicted KPIs. After a user- and scenario-based weighting by a so-called Balanced Performance Indicator (BPI), an optimal production variant will be selected. Above all, this assistance system provides more variety and transparency for adapting capacities and supports the factory planning both for greenfields by improved testing und comparability of production variants and for brownfields by giving recommendations for action during run-time. Additionally, real measured data can be retrieved in a closed-loop into the data base to design a continuous learning system. The concept has been methodically implemented and validated in a virtual material flow simulation of an automotive body shop cell and is linked to a real demonstration facility at the Brandenburg University of Cottbus-Senftenberg. Test runs confirm significant saving potential in energy consumption and OEE losses for the given target capacity.
ISSN:2212-8271
2212-8271
DOI:10.1016/j.procir.2015.12.138