Industry 4.0 and Data Analytics are buzzwords everyone is talking about, but many manufacturing companies cannot fully leverage their potential because their machine, process, and other business data are not efficiently consolidated. A platform that enables rapid data exchange is essential for conducting analyses across all production steps and data systems. In our case study, we show how Gerolsteiner, together with Device Insight and NTT DATA Business Solutions, implemented a Data Intelligence Platform that paves the way for the market leader to become a data-driven factory.
With an annual sales volume of 8.2 million hectoliters (2023), Gerolsteiner Brunnen GmbH & Co. KG is the clear market leader in branded mineral waters in Germany. The various systems and equipment within Gerolsteiner’s production generate large amounts of data that can provide valuable insights and serve as a foundation for optimizations. However, these data had previously been insufficiently synchronized and analyzed. A transparent and comprehensive overview of the process data from production systems alongside the corresponding business data has been lacking. This resulted in high efforts for analyses and quality controls. A data-driven planning and improvement of production processes was not possible.
Gerolsteiner set the goal of establishing real-time transparency across the entire production process. The implementation of a Data Intelligence Platform was intended to form the basis for fast and flexible data analysis, enabling the planning and optimization of production processes across all steps and data systems.
Together with its partners Device Insight and NTT Data Business Solutions, Gerolsteiner implemented a platform that integrates all machine and equipment data along with business data. This enables the realization of numerous use cases in the areas of connected and data-driven factory – from predictive production planning to automated empty-bottle management and efficient cleaning processes using data analytics.
Determining the turnover rate for smart empty-bottle planning
The turnover rate is a key metric for empty-bottle planning at Gerolsteiner. However, calculating this metric used to be a time-consuming task. Now, the entire process is fully automated within the Data Intelligence Platform. By integrating data from various sources, including ERP systems and machine data, Gerolsteiner gains an accurate overview of the bottle pool and can precisely calculate the turnover rate.
Automated empty-bottle management
Another key objective for Gerolsteiner is the automated monitoring of parameters and processes across all empty-bottle systems. Data such as the number of returning bottles, the share of damaged bottles and foreign bottles are automatically captured and aggregated in the data platform. This allows precise planning of the required quantity of empty bottlesand optimizes empty-bottle management.
The Data Intelligence Platform serves as a robust foundation for implementing a wide range of use cases in the areas of connected and data-driven factory.
Transparent quality control
Regular bottle inspections, such as burst pressure tests for PET single-use bottles, are essential in Gerolsteiner’s production process. To optimize this inspection process, a custom application, integrated into CENTERSIGHT scale, was developed in a short amount of time, allowing for fast and easy documentation. The documented data is seamlessly available for analysis, alongside order and process data.
Efficient cleaning processes
The cleaning of empty bottles is also digitized. Analyses in Azure Databricks identify cost-saving opportunities: Can the same cleaning quality be achieved at a lower temperature? Or can the amount of cleaning agents be reduced? Energy and consumption data are linked with order data, allowing Gerolsteiner to more easily investigate complaints and improve overall process and product quality.
For the technological implementation of the solution, Gerolsteiner relied on an intelligent combination of Microsoft Azure, SAP Datasphere & SAP Analytics Cloud, Databricks, and the IoT framework CENTERSIGHT scale from Device Insight.
Using the OPC UA standard, data from the production systems is captured and transferred to the cloud via Azure IoT Edge. The Azure Cloud serves as the central platform for storing, aggregating, and making machine data accessible, supported by Azure Data Explorer and Azure Data Lake. CENTERSIGHT scale enables real-time visualization of this data. For corporate reporting, pre-aggregated process data is linked with ERP data from SAP S/4 HANA and made available in the SAP Analytics Cloud. Additionally, Gerolsteiner uses Azure Databricks for analyzing large volumes of data and developing AI models.
In a „Best-of-Breed“ architecture, the various systems were deployed in a way that maximizes their technological strengths while ensuring a cost-effective implementation of the Data Intelligence Platform.
The Data Intelligence Platform consistently ensures greater efficiency and speed. Production data is now collected and analyzed in real time through the Data Intelligence Platform, eliminating the need for painstaking manual processes. This saves time and money, allowing for a rapid response to changes in demand or challenges in production.
Thanks to the Data Intelligence Platform, we’ve already made significant improvements in managing
our production processes – with a clear focus on more transparency, flexibility, and predictive planning. Initial use cases were already implemented successfully as part of the proof of concept. At the same time, the platform is flexibly expandable, providing the foundation for our continued journey toward becoming a data-driven factory.
With its user-friendly interface and transparent presentation, even non-data analysts can easily work with the Data Intelligence Platform, gaining a deeper understanding of the condition and processes within and beyond production. The platform is designed to be flexibly expanded with new
use cases and serves as a central component in Gerolsteiner’s comprehensive digital transformation.
Learn in our free case study how you too can bring real-time transparency to your production and use data analytics for predictive process planning.
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