Please use this identifier to cite or link to this item: http://dx.doi.org/10.18419/opus-3022
Authors: Vetlugin, Andrey
Title: A process insight repository supporting process optimization
Issue Date: 2012
metadata.ubs.publikation.typ: Abschlussarbeit (Master)
URI: http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-82590
http://elib.uni-stuttgart.de/handle/11682/3039
http://dx.doi.org/10.18419/opus-3022
Abstract: Existing solutions for analysis and optimization of manufacturing processes, such as online analysis processing or statistical calculations, have shortcomings that limit continuous process improvements. In particular, they lack means of storing and integrating the results of analysis. This makes the valuable information that can be used for process optimizations used only once and then disposed. The goal of the Advanced Manufacturing Analytics (AdMA) research project is to design an integrated platform for data-driven analysis and optimization of manufacturing processes using analytical techniques, especially data mining, in order to carry out continuous improvement of production. The achievement of this goal is based on the integration of the data related to the manufacturing processes, especially from Manufacturing Execution Systems (MES), with the other operating data, e.g. from Enterprise Resource Planning (ERP) systems. This work is based on AdMA platform described in [1] and Deep Business Process Optimization platform described in [2]. It is focused on the conceptual development of the Process Insight Repository, which is a part of the AdMA platform. The Process Insight Repository is aimed at storing the manufacturing process related data and the insights associated with it. Being part of the AdMA platform, the Process Insight Repository is oriented on storing the insights retrieved by application of data mining techniques to the data of manufacturing processes, so that the newly extracted knowledge can be stored along with the process data itself. Chapter 2 describes the conceptual schema of the Process Insight Repository. The conceptual schema defines what data must be stored in the Process Insight Repository and how different parts of this data are interconnected. Chapter 3 provides a review of technologies that can be used for the implementation of the Process Insight Repository. This includes technologies for storing manufacturing process data, free form knowledge and data mining related data. Chapter 4 describes the details of the prototype implementation of the Process Insight Repository. The result of this work is the created conceptual schema of the Process Insight Repository and a prototype implementation as a proof of concept.
Appears in Collections:05 Fakultät Informatik, Elektrotechnik und Informationstechnik

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