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Institute
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.
Systematisation Approach
(2023)
Current megatrends such as globalisation and digitalisation are increasing complexity, making systems for well-founded and short-term decision support indispensable. A necessary condition for reliable decision-making is high data quality. In practice, it is repeatedly shown that data quality is insufficient, especially in master and transaction data. Moreover, upcoming approaches for data-based decisions consistently raise the required level of data quality. Hence, the importance of handling insufficient data quality is currently and will remain elementary. Since the literature does not systematically consider the possibilities in the case of insufficient data quality, this paper presents a general model and systematic approach for handling those cases in real-world scenarios. The model developed here presents the various possibilities of handling insufficient data quality in a process-based approach as a framework for decision support. The individual aspects of the model are examined in more detail along the process chain from data acquisition to final data processing. Subsequently, the systematic approach is applied and contextualised for production planning and supply chain event management, respectively. Due to their general validity, the results enable companies to manage insufficient data quality systematically.