Hochkamp, FlorianFlorianHochkampRabe, MarkusMarkusRabe2022-11-042022-11-042022-09Hamburg International Conference of Logistics (HICL) 33: 91-117 (2022)http://hdl.handle.net/11420/13905Purpose: Our research emphasizes the importance of considering outliers in production logistics tasks. With a growing amount of data, we require data mining to cope with these tasks. We underline that the widespread exclusion of outliers in data pre-processing for data mining leads to a loss of information and that using outlier interpretation can be used to address the issue. Methodology: The paper discusses the data pre-processing of data mining in production logistics problems. Methods of outlier interpretation are collected based on a literature review. In addition to the literature-based investigation, the work relies on a case study that illustrates the individual evaluation of outliers. Findings: This work shows that outliers take a special focus on the information generation. Within data pre-processing, a distinction must be made between an outlier as a defect and an outlier as a special datum. This can be conducted by methods presented in the literature. Originality: This paper adds to existing literature in the research field of insufficiently analyzed outlier interpretation and shows a need for research in data pre-processing of data mining.enhttps://creativecommons.org/licenses/by-sa/4.0/Advanced Manufacturing; Industry 4.0InformatikHandel, Kommunikation, VerkehrConference Paper10.15480/882.4689https://www.epubli.de/shop/buch/changing-tides-the-new-role-of-resilience-and-sustainability-in-logistics-and-supply-chain-management-wolfgang-kersten-9783756541959/13093910.15480/882.4689Kersten, WolfgangWolfgangKerstenJahn, CarlosCarlosJahnBlecker, ThorstenThorstenBleckerRingle, Christian M.Christian M.RingleOther