Re-Ramp-Up Management of Scalable Production Systems in the Automotive Industry

Kampker, Achim; Kreisköther, Kai; Lutz, Nicolas; Gauckler, Valentin; Hehl, Martin

doi:10.1109/icitm.2019.8710702

Cited by 6 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This industry currently faces fundamental changes due to the digitization of its production processes [25], where data-driven analyses are already firmly integrated into industrial manufacturing processes [24]. Recent success stories show how such analysis efforts result in substantial manufacturing improvements.…”

Section: Introductionmentioning

confidence: 99%

ManuKnowVis: How to Support Different User Groups in Contextualizing and Leveraging Knowledge Repositories

Eirich

Jäckle

Sedlmair

et al. 2023

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

We present ManuKnowVis, the result of a design study, in which we contextualize data from multiple knowledge repositories of a manufacturing process for battery modules used in electric vehicles. In data-driven analyses of manufacturing data, we observed a discrepancy between two stakeholder groups involved in serial manufacturing processes: Knowledge providers (e.g., engineers) have domain knowledge about the manufacturing process but have difficulties in implementing data-driven analyses. Knowledge consumers (e.g., data scientists) have no first-hand domain knowledge but are highly skilled in performing data-driven analyses. ManuKnowVis bridges the gap between providers and consumers and enables the creation and completion of manufacturing knowledge. We contribute a multi-stakeholder design study, where we developed ManuKnowVis in three main iterations with consumers and providers from an automotive company. The iterative development led us to a multiple linked view tool, in which, on the one hand, providers can describe and connect individual entities (e.g., stations or produced parts) of the manufacturing process based on their domain knowledge. On the other hand, consumers can leverage this enhanced data to better understand complex domain problems, thus, performing data analyses more efficiently. As such, our approach directly impacts the success of data-driven analyses from manufacturing data. To demonstrate the usefulness of our approach, we carried out a case study with seven domain experts, which demonstrates how providers can externalize their knowledge and consumers can implement data-driven analyses more efficiently.

show abstract

Section: Introductionmentioning

confidence: 99%