Challenges: Defining the Data Problem
Paul Hartmann AG sells products in 100 different countries, which generates massive amounts of data.
This data comes from a number of technological innovations. For example, there’s an infection database that uses artificial intelligence to help hospitals detect and prevent infections early – saving lives and reducing treatment costs. There are also IoT devices used in boxes to trigger automatic replenishment for hospitals of key medical supplies.
“We had too many data sources,” says Fibitz, “and no way to analyze them across applications to gain new insights about our products and better understand our customers’ needs.”
To access and ingest all the different data sources, Paul Hartmann AG used SAP Data Intelligence Cloud to create reusable pipelines. The centralized data management features helped give visibility into all the different sources and pipelines – bring much-needed structure and organization to all their data sources.
SAP HANA Cloud offers scalable data modeling and persistence, helping Paul Hartmann AG reliably manage all data. The SAP HANA Predictive Analysis Library (PAL) showed itself to be invaluable. This built-in function library provides algorithms for classic machine learning use cases, like classification, regression, time series forecasting, cluster analysis, and more. Furthermore, SAP HANA Cloud’s powerful transactional and analytical processing capabilities laid the groundwork to readily analyze data
The self-service and predictive analytics capabilities of SAP Analytics Cloud help with supply chain reporting and financial and budget planning.
Now, Paul Hartmann AG has one source of truth for all customer, supplier, and operational data. This benefits both the business and the consumers.
On the business end, users now have much simpler access to data through single sign-on and a unified, user-friendly interface. Greater operational insight has resulted in streamlined production processes as well as better inventory planning. Plus, with more information on what customers need, Paul Hartmann AG can increase revenue streams by introducing new innovations and services.
One of these innovations is the enhancement of its inventory sensor boxes. The plan is to incorporate external data on factors such as weather to help prepare in case of an emergency. “For example,” explains Fibitz, “if we know heavy snow is on the way, we know there will be more accidents on the road. Not only can we account for this in our own manufacturing processes, but we can also update the minimum stock level on the sensor boxes to make sure hospitals get timely deliveries and are prepared.”
With their new data landscape, Paul Hartmann AG is excited to continue innovating with more exciting use cases that leverage machine learning, artificial intelligence, and other exciting technologies.