TY  - CONF
A1  - Janßen, Jokim
A1  - Schröer, Tobias
A1  - Schuh, Günther
A1  - Boos, Wolfgang
A2  - Erlend, Alfnes
A2  - Romsdal, Anita
A2  - Strandhagen, Jan Ola
A2  - Cieminski, Gregor von
A2  - Romero, David
A2  - Romero, David
T1  - Derivation of the Data Attributes for Identification of Incorrect Events in Supply Chain Event Management
T2  - 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
N2  - 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.
T3  - IFIP advances in information and communication technology - 692 
KW  - anomaly detection
KW  - data set
KW  - deviation identification strategies
KW  - incorrect data
KW  - EPCIS
KW  - supply chain event management
KW  - rev
Y1  - 2023
UR  - https://epub.fir.de/frontdoor/index/index/docId/2910
UR  - https://link.springer.com/chapter/10.1007/978-3-031-43688-8_47
SN  - 978-3-03143-687-1
SN  - 978-3-031-43688-8
SN  - 1868-4238
SP  - 685
EP  - 698
PB  - Springer
CY  - Cham [u. a.]
ER  -