SAP
SAP is the leading enterprise application and business AI company. SAP stands at the intersection of business and technology, where innovations are designed to directly address real business challenges and produce real-world impacts. SAP’s integrated portfolio unites the elements of modern organizations — from workforce and financials to customers and supply chains — into a unified ecosystem that drives progress. For over 50 years, organizations have trusted SAP to bring out their best by uniting business-critical operations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit www.sap.com.
SAP and AI
SAP focuses on Business AI by embedding intelligent capabilities directly into enterprise applications and processes from the start. These solutions meet SAP’s high standards for product quality, ethics, privacy, and security.
Using advanced machine learning and deep learning technologies, SAP enhances its cloud solutions to deliver practical business value. For example, this technology/AI helps automatically match invoices to payments in CloudERP SAP Cash applications, streamline invoice processing in SAP Concur within Spend Management.
SAP & MDSI
Project: Synthetic Business Data Generation with Prof. Stephan Günnemann
Conducting research does not only require well-defined problem statements but particularly in AI also relevant data. However, SAP as a leading ERP provider, cannot just share confidential customer data with third parties and researchers. While public dataset may be used it is often not representative of real business data. To this end, this project aims to synthetically generate business data driven by SAP data models and metadata, allowing for both scientific reproducibility and business relevance.
Project: New Approaches to Tour Planning and Vehicle Routing with Prof. Stefan Minner
This is a joint research project regarding the use of data-driven approaches and decomposition algorithms in the context of the Vehicle Routing Problem (VRP) to enable the solution of larger and more complex real-world problems. In order to advance the state of knowledge in AI and advanced optimization, several more specific questions need to be addressed. For example, how are the clusters rated when aggregating and breaking down into sub-clusters, and how to improve the user acceptance of data-driven and AI-based approaches.