How Diageo uses Machine Learning to optimize Finance
FMCG companies are generally quite efficient at managing outstanding debt and collecting trade receivables, but there are further improvement opportunities that can be unlocked using Machine Learning. My talk will introduce a real-life case study on how a small international team built a predictive ML model in R to help improve cash collection at a large FMCG company.
Data & Analytics Transformation Manager, Diageo
Daniel Percze graduated from Budapest University of Technology & Economics in 2006. He started his career as a .NET developer at KFKI IQSYS, then continued in BAT, where he participated in CRM, ERP and BI implementation projects, including the SAP rollout in Hungary. He moved to MOL Group in 2015, where he worked on redefining the BI strategy to include predictive analytics and BW on HANA, piloted Azure Machine Learning tools in Danube Refinery and led the Core SAP development team. He is currently working as Data & Analytics Transformation Manager for Diageo, focusing on bringing new Cash-related insights to Diageo.