Uncovering Data Treasures With Strategy

A new Data Strategy and a Company-Wide Data Model Pave the way

Customer, order, production, and machine data - For a company like RENOLIT, where a variety of data types from numerous sources (e.g., MES, CRM, ERP, and others) meet a heterogeneous system landscape, the first step was a data strategy consultation – conducted by experts from Telekom MMS. This resulted in a standardized data model as the basis for the start of the transition phase.

To increase quality, data from the ERP, MES, and database management system were first harmonized and cleaned up. These could then be summarized into “data pots,” from which contexts could be derived and, ultimately, the first data products were created. For example, the data pots "material, location, and production order" were combined into one such data product.

To display the results of the analyses, two dashboards for production control were developed with selected data products as a minimum viable product (MVP). These were deployed at the first production sites, tested, and then continuously further developed.

Meanwhile, the cloud-based data platform built on Microsoft Azure has been expanded to include additional data sources and rolled out to multiple locations. Not only production, but also the finance department benefits from the comprehensive reporting and KPI dashboards for effective management of production management.

A Cloud-Based, Central Data Platform Promotes Transparency, Quality, and Future Viability

By developing a data strategy, a lot of knowledge that was previously hidden in silos could be brought together for the first time. Discussions about the production process, sharing of knowledge, and exchange of experiences took place between the individual locations, creating the foundation for a joint, coordinated approach. Even in the subsequent transition phase, the Telekom MMS RENOLIT team accompanied and followed the individual pace of the plastics manufacturer.

With the central data platform, both production managers and engineers now share a transparent and uniform view of meaningful and high-quality data. This enables collaborative work across location boundaries, e.g., plant-wide analyses, evaluations, and insights. The management benefits not only from high-quality reports for controlling production management and sales, but also from time savings in automated report generation.

Furthermore, the solution is highly scalable: regardless of whether new products or additional plants are to be integrated, the adaptation effort is significantly reduced by the centralization of the data model. RENOLIT can now scale better in terms of processing power and bandwidth, as it has moved to the cloud. This enables the continuous expansion of the data platform as a future-proof solution. The path for the use of AI & Machine Learning has also been smoothed, which opens up further application potentials for RENOLIT.