Technology Forum with Dr. Michael Eiden and Greg Smith
How to Transform Decision Making with Machine Learning-Driven Predictive Tools
A key lesson learned from the COVID-19 crisis is that we need our business resilience systems to sense what is happening in real time, constantly update our predictions, and allow us to take early action before a major risk escalates. This requires a much more dynamic and adaptive approach than has been traditionally used in conventional static enterprise risk management (ERM) systems. In this session, you’ll learn about a new approach, powered by leading-edge machine learning (ML) technology, that can greatly enhance prediction and forecasting. By integrating data from hundreds of sources and then combining it with your company’s internal data, this tool can provide context-specific, customized forecasts and predictions to support decision making. This Prediction Dashboard, developed by Dr. Michael Eiden and his team at Arthur D. Little, has several key features that make it transformative for decision making, not just during a crisis such as the COVID-19 pandemic, but also as a key element of an ongoing business resilience approach. Through case study and demonstration, you’ll discover how the Prediction Dashboard has been used in several industries, and will consider how it could benefit other industries and your organization.
Dr. Michael Eiden has designed, implemented and productionized ML/AI solutions for applications in medical diagnostics, pharma, biodefense and consumer electronics.
Greg Smith is a Cutter Consortium Fellow, Partner of Arthur D. Little, and co-founder of ADL’s Digital Problem Solving practice. Mr. Smith’s work specializes in applying disruptive information technologies to solve intractable business problems in major enterprises.
Effective prediction capability will be essential for companies in the 'new normal' after the crisis — not just in pharma, but across all industries.