Abstract
A car line can offer more than \(10^{100}\) variants, and for each customer order a concrete selection of features and parts needs to be done. The respective selection rules are interconnected and are subject to constraints imposed by different car lines. We address the problem of finding and fixing logical errors in these interconnected selection rules within different contexts of allowed feature combinations. Previous work has focused on text-based or matrix-like representations which presented challenges regarding cognitive complexity, size of the representation, and usability. We present an integrated visualization of the combined practical effects of item selection and feature combination rules. The implemented tool detects logical errors and supports user workflows to fix the data visually.
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This work has received funding by the BMWK-funded project SofDCar (19S21002D).
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Bischoff, D., Küchlin, W., Kopp, O. (2023). POSEIDON: A Graphical Editor for Item Selection Rules Within Feature Combination Rule Contexts. In: Noël, F., Nyffenegger, F., Rivest, L., Bouras, A. (eds) Product Lifecycle Management. PLM in Transition Times: The Place of Humans and Transformative Technologies. PLM 2022. IFIP Advances in Information and Communication Technology, vol 667. Springer, Cham. https://doi.org/10.1007/978-3-031-25182-5_1
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DOI: https://doi.org/10.1007/978-3-031-25182-5_1
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