Sap Se

Ramana Mohanbabu

Oliver Kieselbach

Harshavardhan Jegadeesan

Swarnava Chatterjee

Himanshu Goyal

Stefan Feickert

Nisheeth Agarwal

Intelligent mapping from created item information to sustainability reference content from a variety of sources can be implemented to facilitate created item footprint management and other sustainability applications. The difficult task of finding appropriate emission factors across a portfolio can be automated. Assisted search can be implemented using enhanced search techniques. Fallback mappings can be implemented to accommodate different levels of granularity during search. A machine learning model can be trained based on a variety of input data, including confirmed mappings, mapping history, and rules. The process of mapping to emission datasets can thus be simplified, enabling footprint calculations to proceed.

Intelligent machine learning-based mapping service for footprint

The present disclosure involves systems, software, and computer-implemented methods for providing process intelligence by correlating events from multiple business process systems to a single business scenario using configurable correlation strategies. An example method includes identifying a raw event associated with a sending business process and a receiving business process, identifying a sending business process attribute associated with the sending business process and a receiving business process attribute associated with the receiving business process, determining a correlation strategy for associating the raw event with a business scenario instance, the determination based at least in part on the sending business process attribute and the receiving business process attribute, and generating a visibility scenario event from the raw event according to the correlation strategy, the visibility scenario event associated with the business scenario instance.

Correlating data from multiple business processes to a business process scenario

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Bengaluru, India

Ramana Mohanbabu