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BMW AG Q-AURA

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BMW Q-AURA (quality, abnormality and cause analysis)

The FAW supports BMW quality management by using data mining methods for optimizing the response time between the identification of errors in the delivered product and the taking of measures in production.



Project leader
a.Univ.-Prof. DI Dr. Wolfram Wöß

Project staff
DI Dr. Christina Feilmayr
DI DI Thomas Leitner
Lisa Ehrlinger

Project start and end date
01.04.2010 - 30.09.2015

Project description
BMW Motoren GmbH Steyr is not only BMW's largest engine production worldwide but also a competence center for the development of diesel engines across the BMW group. Since quality is BMW's highest aim, quality principles are integrated in the whole business process.

With higher manufacturing demands, also those of the quality management arise. As a result of a higher production rate, potentially more faulty engines could be produced, especially if the time between the fault causation in the engine development process and the fault elimination in the production is not decreased. A shortest possible reaction time is crucial to minimize follow-up costs. The FAW institute of the Johannes Kepler University supports BMW quality management leveraging this optimization potential and therefore continuing delivering best possible quality in the case of an increasing production volume. In this project cooperation concepts and methods are developed in order to be able to establish a correlation between delivered products and manufacturing data rapidly and target-oriented.

An initial challenge is the close linking of all relevant data across the process chain. In a complex, and for the size of a concern typical heterogeneous information system landscape, a middleware has to be enriched with meta-information. Afterwards the data is analyzed by using data mining methods in order to detect potential defects at an early stage and to rate their relevance.

QSTEYR (Quality BMW Steyr) is a system that fulfills these tasks for diesel engines of BMW Motoren GmbH Steyr by pro-actively analyzing incoming deficiency reports and revealing the user a critical trend if necessary. Afterwards it is possible to incrementally identify the critical technical engine modifications that may have caused the faults. The figure shows the process of significant faults detection, the determination of affected engines and the identification of relevant technical modifications.

Apart from this automatic analysis method, additional functions are developed that allow the user to specify and execute an event driven analyses and provide structured and visualized results. In both cases, periodic and user-driven analyses, the user is supported with extensively automated processing steps.

Q-AURA (quality, abnormality and case analysis) is developed and implemented as further extension of QSTEYR. Beside diesel engines Q-AURA in addition considers the gas engine production of BMW Motoren GmbH and analysis as well as evaluation features are rolled out to further BMW engine production locations.

In a further development step, the developed and implemented data mining and analysis methods are optimized and refined regarding early detection and an improved accuracy.