Evaluation of data quality in dimensioning capacity - Lea Vliegen; Nikolas Ulrich Moroff; Katharina Riehl (Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 29, 2020)
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.
urn:nbn:de:gbv:830-882.0115389; hdl:10419/228914; https://econpapers.repec.org/RePEc:zbw:hiclpr:29
Keywords: 650; Logistics; Industry 4.0; Digitalization; Innovation; Supply Chain Management; Artificial Intelligence; Data Science