Towards a maturity model to measure data consistency in the manufacturing industry
Authors: Maximilian Feike, Philipp Christel
Abstract: The deployment of Artificial Intelligence (AI) in the manufacturing context is said to provide significant benefits to organizations. However, many manufacturers struggle to meet the requirements necessary for the use of AI technologies within their company. A major challenge is linking and processing the existing data into a way in which it can be reliably processed by AI algorithms. This is especially relevant in established companies characterized by a historically grown bulky and decentralized IT infrastructure. Moreover, in many of these companies there is no common understanding of data consistency. Therefore, we investigate the diverse dimensions of data consistency and set the foundation for a maturity model to assess a company division’s status quo. Based on our literature review and four interdisciplinary and iterative workshops conducted with experts from an automotive OEM, we developed the concept of a maturity model for data consistency that provides situation-specific recommendations for further improvement.
Keywords: maturity model, data consistency, data continuity, legacy systems, manufacturing industry
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