The Challenge 
Management and periodic calculation of different CO2 product footprints (Scope 3 emissions) of various suppliers taking into account ERP movement data in a central solution. Until now, CO2 emission factors were managed without IT system support. Reducing the CO2 supplier footprints of purchased raw materials and packaging is a key lever for reducing the Corporate Carbon Footprint of Mann & Schröder Cosmetics.

The Solution: SAP Product Footprint Management
SAP Product Footprint Management (SUS-PFM) is a native cloud solution based on the SAP Business Technology With a live connection to the S/4 HANA system, existing ERP data (master data, transaction activity data) can be transferred at any time. The integration thus enables holistic consideration of emissions, including GHG Scope 3 emissions obtained either from life cycle analysis (LCA) databases or Mann & Schröder Cosmetics' own calculations. Once emission factors were imported into SAP Product Footprint Management and linked to SAP S/4 HANA product data, master data such as plants, product groups could be considered for the calculations. The subsequent analysis of CO2 product footprints provides a better understanding of the sources of CO2 emissions. The results can help to optimize further key business processes (e.g., procurement processes, supplier management), explains Dr. Carmen Matzke, Head of Regulatory at Mann & Schröder Cosmetics.

Optimized data transfer: tailored to Mann & Schröder's needs
With the help of developments based on the SAP standard, the data transfer from the S/4 HANA logistics system was customized so that the required data points were automatically transferred to SAP Product Footprint Management. Data points that were not required were excluded from the data transfer in order to be able to interpret the (cumulative) footprint for purchased products in a meaningful way. The automatic and selective transfer of data (master data and transaction data) from the S/4 HANA logistics system reduces manual activities and the susceptibility to errors, while improving data quality.