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DQ-Step – Improvement of data quality at a reference enterprise in plant engineering

DQ-Step - Improvement of data quality at a reference enterprise in plant engineering

(Drittmittelfinanzierte Einzelförderung)

Titel des Gesamtprojektes:
Projektleitung:
Projektbeteiligte:
Projektstart: 01/15/09
Projektende: 02/15/12
Akronym: DQ-Step
Mittelgeber: Industrie
URL:

Abstract

The application of IT-supported processes in plant engineering - especially in the areas of engineering, procurement, and construction (EPC) - is steadily increasing. Thus, the data quality in the information management systems becomes more and more important. The information management systems have the following challenges: The amounts of data increase, the integration of the tools is not optimized and due to the system heterogeneous. The quality of the deliverables (documents, parts lists, drawings etc.) is often manually assured, whereas corrective actions and extensions are partly not immediately returned to the sources. Instead of that, they are just carried out in the corresponding document. Data sources are partly redundant and data flows are not uniquely defined and applied. Additionally, the inadequate support of the existing Concurrent Engineering often lead to time- and cost-intensive feedback loops in the design process. The research project should lead to techniques and approaches that contribute to an improvement of data quality. More precisely, in the context of the engineering activities, relevant features of the data quality as well as corresponding measurable quality indicators should be determined. To achieve an enduring optimization of the data quality, based on the above mentioned quality indicators, a system architecture should be developed that makes a control of the data quality possible. Generally, quality-assuring techniques should be established in the engineering process as soon and automated as possible. Besides the specific approaches for the existent situation at the reference enterprise, the research project should provide findings that have relevance beyond that and are transferable to other application areas. After comprehensive analysis at the reference enterprise, a problem classification could be reached that separates three superordinate problem classes: missing engineering-relevant information, data errors, and missing projectstatus-relevant information. Based on that, corresponding requirements for the data quality tool to develop were stated. In addition to the functional requirements, the attention was especially paid to the development of a solution that is neutral regarding the existing information system landscape and adaptable to projects in the future. The goal of the data quality tool to develop was the support of Concurrent Engineering and the long-term improvement of data quality. The prototypically realized data quality tool was finally evaluated at the reference enterprise with representatives of the target group; this confirmed that the intended goals were reached. The prototype was installed on a testing plattform at the reference enterprise at the end of the project to be further refined. In the future, the concepts shall be integrated into the information system landscape of the reference enterprise.

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