Derivation of the Data Attributes for Identification of Incorrect Events in Supply Chain Event Management

  • Based on the increasingly complex value creation networks, more and more event-based systems are being used for decision support. One example of a category of event-based systems is supply chain event management. The aim is to enable the best possible reaction to critical exceptional events based on event data. The central element is the event, which represents the information basis for mapping and matching the process flows in the event-based systems. However, since the data quality is insufficient in numerous application cases and the identification of incorrect data in supply chain event management is considered in the literature, this paper deals with the theoretical derivation of the necessary data attributes for the identification of incorrect event data. In particular, the types of errors that require complex identification strategies are considered. Accordingly, the relevant existing error types of event data are specified in subtypes in this paper. Subsequently, the necessary information requirements and information available regarding identification are considered using a GAP analysis. Based on this gap, the necessary data attributes can then be derived. Finally, an approach is presented that enables the generation of the complete data set. This serves as a basis for the recognition and filtering out of erroneous events in contrast to standard and exception events.

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Author:Jokim JanßenGND, Tobias SchröerORCiDGND, Günther SchuhORCiDGND, Wolfgang BoosORCiDGND
Parent Title (English):Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures. IFIP WG 5.7 International Conference, APMS 2023, Trondheim, Norway, September 17–21, 2023, Proceedings, Part IV
Series (Serial Number):IFIP advances in information and communication technology (692)
Place of publication:Cham [u. a.]
Editor:Alfnes Erlend, Anita Romsdal, Jan Ola Strandhagen, Gregor von Cieminski, David Romero, David Romero
Document Type:Conference Proceeding
Year of Completion:2023
Date of first Publication:2023/09/14
Release Date:2023/09/21
EPCIS; anomaly detection; data set; deviation identification strategies; incorrect data; supply chain event management
First Page:685
Last Page:698
Institute / Department:FIR e. V. an der RWTH Aachen
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften