A Classification of Data Structures for Process Analysis in Internal Logistics

Data Science plays a crucial role in driving new approaches to process optimization. With the increasing complexity of internal logistics systems, data-oriented methods have become essential in addressing the challenges that arise. However, standardized process analytics frameworks are lacking due to the heterogeneity of the underlying processes and the resulting data. This article aims to address this complexity by presenting a categorization of internal logistics data, consolidating the current state of the art. The categorization takes into account both real-world and scientifically proposed data architectures, providing a comprehensive overview. It includes a classification of comparative data fields based on their importance, the associated internal logistics processes, and potential usage scenarios. This classification is designed to cater to different use cases, such as diagnostics or prescriptive analytics. By presenting this categorization, the article enables practitioners to effectively leverage generated process data in a more goal-oriented manner. It empowers them to conduct suitable analyses tailored to their specific needs and objectives, based on the provided data architectures. In summary, this article offers valuable insights into internal logistics data categorization, providing a framework for practitioners to make informed decisions and optimize processes using data-driven approaches.