In 2020 Rafi founded Stargazr, a Machine Learning Software designed for manufacturing companies. The software uses prescriptive analytics to send ML-based recommendations to C-levels, allowing cost reduction and efficient resource allocations in operational processes.
He received a Ph.D. in Digitization and Controlling and wrote my thesis (2017-2022) about the “Impact of Industry 4.0 on Controlling” at the TU Chemnitz and UCLA. he has gained industry experience (+8 yrs) through his positions as financial controller at Beiersdorf in Germany and Lufthansa Technik in Los Angeles, where he shaped the BI and Data analysis platforms for those companies. Rafi also lectured at USC (ISI Viterbi), Nordakademie Elmshorn, and Northern Business School for courses around digitization.
Using Machine Learning as Enterprise Performance Management System: How to receive recommendations to execute
This presentation will explore the benefits of using machine learning as an enterprise performance management system. By leveraging the power of artificial intelligence, organizations can receive valuable recommendations to execute their business strategies more efficiently and effectively. Attendees will learn about the key components of a successful machine learning system, including data preparation, model training, and deployment. We will also discuss practical use cases and real-world examples of how machine learning can help companies make better decisions and improve their bottom line. By the end of the session, attendees will have a better understanding of how to implement a machine learning-based enterprise performance management system and begin realizing its benefits.