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Using Machine Learning as Enterprise Performance Management System: How to receive recommendations to execute

The digitization creates new requirements on innovative topics such as predictive analytics, machine learning and robotic process automation (RPA). Due to increasing competition in data-driven markets, firms are adopting state-of-the-art information technologies for competitive advantage (Verma and Bhattacharyya, 2017). With these technological advances, management receives faster and more insightful access to decision-relevant information. Brynjolfsson et al. (2011) examined a nearly 6% higher productivity improvement when firms make decisions based on data. Several surveys have shown the importance of analytics for now and in the future (Accenture, 2018; McKinsey, 2011; SAS Institute, 2014). At the same time, digitization offers MAs new opportunities and tools for a more efficient performance. Especially tasks with a highly repetitive character have a high probability of automation (Langmann, 2019).

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.


Rafi Wadan



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