Anforderungen für Zeitreihendatenbanken im industriellen IoT

Petrik, Dimitri; Mormul, Mathias; Reimann, Peter; Gröger, Christoph

doi:10.1007/978-3-658-32439-1_19

Cited by 1 publication

(1 citation statement)

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“…A relational database can be used if the focus is on regular analysis of static data in the context of long-term value stream planning. In contrast, if the focus is on continuous monitoring of the value stream for short-term planning, a time series database is a more suitable solution [61]. To overcome this conflict, a hybrid database management system TimescaleDB is available, which is an extension of the relational database management system PostgreSQL optimized for time series data [62].…”

Section: Physical Data Modelmentioning

confidence: 99%

Design Model for the Digital Shadow of a Value Stream

Frick,

Terwolbeck,

Seibel

et al. 2024

Systems

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The value stream method, a key tool in industry to analyze and visualize value streams in production, aims to holistically optimize process steps, reduce waste, and achieve continuous material flow. However, this method primarily relies on data from a single on-site inspection, which is subjective and represents just a snapshot of the process. This limitation can lead to uncertainty and potentially incorrect decisions, especially in industries producing customer-specific products. The increasing digitization in production offers a solution to this limitation by supporting the method through data provision. The concept of the digital shadow emerges as a key tool that systematically captures, processes, and integrates necessary data into a model to enhance traditional value stream mapping. This addresses the method’s shortcomings, especially in heterogeneous IT landscapes and complex value streams. To effectively implement the digital shadow this study identifies concepts of digital shadows and their key components and evaluates them for their relevance in industrial environments using an expert study. Based on the results, a design model is defined. This model entails guidelines to support companies with the practical implementation of the digital shadow of a value stream. Lastly, the model is evaluated on a realistic value stream in a learning factory.

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Section: Physical Data Modelmentioning

confidence: 99%