Industrie 4.0

Data analytics for the fourth industrial revolution, such as proactive service and maintenance of production resources or finding anomalies in production processes.



Data-driven aspects of medicine are explored, such as the need-driven care of patients or IT controlled medical technology.


Smart Infrastructure

Untersuchung datengetriebener Aspekter städtischen Lebens, bspw. der Verkehrssteuerung, der Müllentsorgung oder der Katastrophenbewältigung, bedarfsgesteuerte Optimierung von Verbrauchsmodellen, basierend auf Daten intelligenter Stromzähler.

Featured Projects

  • SDSC-BW: Smart prediction of shipping volumes with AI-models

    Predicting shipping volumes with artificial intelligence instead of intuitive prediction was the goal of the smart data experts at SDSC-BW together with the logistics and transport company LGI. Various algorithms were implemented, for daily, weekly and monthly prediction, and evaluated in order to find the best model. The complex models of SDSC-BW could significantly outperform the prediction models in the comparative analysis.

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  • Predictive maintenance at risk: A churner warning system

    The churn prediction is an important method to predict customer churn through machine learning and data mining. The challenge is to enable companies with a precise and real-time prediction, giving them enough time to keep their customers. Previous research in the B2B-context, as well as in the B2C-context is missing the dynamic aspect of macroeconomic variables in the time elapsed. It is the goal of this work to create a churn prediction model with the use of machine learning algorithms like Random Forest and neural networks and to research if an inclusion of dynamic aspects will lead to improvement.

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  • SDSC BW: sales forecasts of shape and colour

    A better planning for the use of materials in the production of furniture was the aim of the potential analysis of the furniture manufacturer Vitra and the SDSC-BW. The challenge for the company’s product forecast was the wide range of colours and materials. The SDSC-BW experts developed a predictive model based on the sales figures of the previous year to find hidden information and patterns in the data.

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  • SDSC-BW: Presciently increasing the energy efficiency

    Air needs high expenditure of energy for its compression – to improve the energy efficiency of the necessary compressed air systems is a big issue for the company Mader, manufacturer of compressed air systems. With the support of SDSC-BW, the company has started smart data analysis of its data to explore previously undiscovered patterns.

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  • STEP

    Smart Technician Mission Planning (STEP)

    The research project “Smart Technician Mission Planning ” (STEP) aims to simultaneously increase the efficiency of technician assignments and the availability of machinery. Several project partners will work on the simulation model that allows to evaluate individual measures quantitatively based on real dispatching operation data.

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