Data analytics - Successful utilization of big data in production and logistics

In the age of Industry 4.0, enterprise data is increasingly viewed as part of the added value. The digitalization and automation of the last few years allows a comprehensive data collection, which is a major challenge for many companies. On the one hand, real-time data for the response to short-term changes have to be analyzed and processed. On the other hand, future events are to be projected from the collected data.

Through knowledge gained from research and development projects in different fields of production, logistics and big data management, RISC Software GmbH supports its partners and customers in the preparation and implementation of these new tasks.

Data Analytics

Smart data analysis and forecasting

The intelligent network of information and communication technologies guarantees strong efficiency and quality increases for production and logistics. Smart technologies can reduce energy and resource consumption as well as increasing the flexibility and agility of production and logistics processes.

The intelligent combination of analysis and forecasting tools allows data to be exploited in a resource-conserving manner. Big data and machine learning approaches are important components and their usage will decisively influence the further development of the company.

Data engineering allows smart handling of large amounts of data on standard hardware. In addition to the on-line time import of incidental sensor data from production and logistics systems, efficient storage and aggregation must also be guaranteed. Through modern methods from the area of machine learning, these data streams can be analyzed. In this context, correlations, and patterns are identified which are used for error and cause analysis as well as for continuous quality monitoring.

Using mathematical prediction algorithms, models can be developed in order to adjust the maintenance intervals and at the same time for early failure detection. This results in an increase of efficiency in the production and logistics processes and at the same time an improvement in product quality.

Data Analytics

Reference Projects

With data analytics, bottlenecks or overflows can be predicted, such as in the following projects RTM-O, IPPO and HOPL.

RTM-O – Rail Transport Mobility Optimization

In the RTM-O project, RISC Software GmbH, together with OMV, RCA, OnTec and IPH, develops and designs a collaborative optimization and control of the loading and unloading processes by rail for OMV, which enables an end-to-end railway supply chain optimization.

See also und

IPPO – Intelligent networking of forecast, planning and optimization for the design of sustainable transport chains

Together with Fraunhofer Austria and Hödlmayr International AG, RISC Software GmbH develops mathematical forecasting algorithms for supply chain planning, which can be used to forecast material flows and thus enables planning of reliable and sustainable transport cycles.

HOPL – Heuristic Optimization in Production and Logistics

Within the framework of the HOPL project, forecast models are being developed for more efficient use of resources in the transport sector. Gebrüder Weiss GmbH is able to plan the volume of orders and the associated resources better with the short-term forecasts.

Start with data analytics

RISC Software GmbH meets the latest requirements of modern data management and offers individual solutions in the fields of data analysis and forecasting, supply chain management as well as intelligent production, production and logistics processes.

Data Analytics
Data Analytics


Stefanie Kritzinger

Mag. Stefanie Kritzinger, PhD

Head of Unit Logistics Informatics
Phone: +43 7236 / 33 43 - 243