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Evaluation of Data Quality in Dimensioning Capacity

: Vliegen, Lea; Moroff, Nikolas Ulrich; Riehl, Katharina

Volltext ()

Kersten, W. ; TU Hamburg-Harburg; TU Hamburg-Harburg, Institut für Logistik und Unternehmensführung:
Data science and innovation in supply chain management : How data transforms the value chain
Berlin: epubli, 2020 (Proceedings of the Hamburg International Conference of Logistics (HICL) 29)
ISBN: 978-3-753123-46-2
Hamburg International Conference of Logistics (HICL) <2020, Online>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IML ()
Supply Chain Design (SCD); measuring data quality; methods for evaluating datasets; Big Data Analytics; applicability, research project E²Design; research project E²Design

Purpose: This paper aims to give an overview of the current state of research on measuring data quality. The identified methods will be applied to the task of dimensioning capacities (e.g. warehouse capacities) in the field of supply chain design (SCD) to further increase trust in decision support and to make full use of the potential of analytics.
Methodology: The data requirements for SCD decisions are identified through the combination of findings of a research project and additional literature research. Moreover, an overview on measuring data quality will be given according to a literature study. Based on the required data, the applicability of methods to measure data quality will be analyzed and an application concept developed.
Findings: The quality of decisions can only be as good as the quality of the data they are based on. The article provides an overview of methods for evaluating datasets and develops an approach for measuring and evaluating data quality for the specific case of capacities in the SCD process.
Originality: The adaption of approaches of measuring data quality to the problem of dimensioning capacities in SCD ensures an adequate evaluation of whether the data fulfills the required quality for the planning tasks.