2020, 200 S., 21 cm, Softcover
In the course of Industry 4.0, companies are collecting more and more data to increase their productivity. Studies expect benefits by the implementation of a digitalized manufacturing resulting in increased output, decreased material and energy consumption which are estimated to amount to 18%. Thus, digitalized companies generate 110 billion additional revenue annually across the EU. The growth of data is one of the drivers for finding ways of an easy and systematic data use. This amount of data is decreasing the datas degree of efficiency since it gets harder to handle. This leads to the necessity of further activities in the field of digitalization to cope with the fast evolving data acquisition and storage. Thus, the thesis strives at developing a model that interconnects and aggregates manufacturing data to enable an automated KPI generation with regard to energy and resource consumption for the purpose of monitoring. Therefore, the thesis answers the following research issue: Can vertical integration of data in production systems be used to acquire product-specific performance indicators for manufacturing processes on shop floor level? To answer the research issue, a model fulfilling the needs for an industrial implementation and application in SME has been developed. The rough model displays the approach how to acquire, preprocess and use data in terms of information flow as well as how to integrate the user regarding information provision. Subsequently for specifying the models content, a ratio system with relevant energy and resource consumption KPI with regard to monitoring purposes has been developed based on established sustainability indexes and enhanced by the according mathematical equation. Taken these into account, the required information for every individual KPI could be derived as well as its assigned data source in terms of a production system. Additionally, the database structure reproducing the models specifications and requirements with regard to data interconnection has been derived. After the final setting of aggregation levels and the considered parameters, algorithms for the data selection dependent on the users parameter setting have been integrated into the model. The model has been transferred into a software environment which provides the main functionalities. This environment has been used to validate the models logic and applicability based on real yet anonymized production data provided by two industrial companies.