This AI-generated podcast episode explains mathematical optimization models, which provide a structured and efficient approach to decision-making in business and industry. The podcast outlines, initially, how these models are built upon insights derived from correlation and regression analysis.

By identifying relationships between variables and quantifying their impact, predictive analytics allows organizations to make data-driven decisions before defining the optimization framework. Once these predictive relationships are established, decision-makers can implement optimization models to determine the best possible actions while considering business constraints and goals.

This structured approach—leveraging predictive analytics and optimization—enables organizations to enhance productivity, reduce costs, and improve overall strategic decision-making.


Mathematical Modeling for Decision-Making: Understanding Optimization Models based on Correlation and Regression Analysis Insights

Marco Serrato serves as Vice President of Arizona State University‘s Learning Enterprise, which serves learners across their entire lifespan. Under this role, Marco collaborates with academic units and faculty across the university to identify and oversee opportunities to serve such learners.

Marco has a global understanding of higher education, including professional, executive and continuing education programs across different industries, geographies and emerging technology platforms. He has experience developing initiatives with private, governmental, and nongovernmental organizations in the United States, Latin America, Europe, Asia, Africa and the MENA region, including serving as emeritus board member and former chair of the International Consortium for University-based Executive Education (UNICON). He has also served as representative to the University Professionals for Continuing Education Association (UPCEA) and the Latin American and European Continuing Education Network (RECLA).

Prior to joining Arizona State University (ASU), Marco served as associate provost at The University of Chicago and as vice rector for continuing education at Tecnológico de Monterrey in Mexico. He also serves as professor of global strategy and business analytics at Thunderbird School of Global Management at Arizona State University and has experience as adjunct faculty at the PhD, master’s, executive education and undergraduate levels. Marco was a visiting scholar at the Massachusetts Institute of Technology (MIT) and has published more than thirty research papers on international peer-reviewed and conference journals, four books, and three book chapters. His contributions in the higher education landscape have been featured by international media including the World Economic Forum and the United Nations, among others.

Marco serves in several corporate and academic boards, including the American University in Cairo’s executive education council. He has also co-founded two start-ups that provide consulting and learning solutions in public and private sectors.

Originally from Mexico, Marco is a first-generation college graduate that holds a double-degree PhD in Industrial Engineering – Operations Research from Iowa State University and Industrial Engineering – Operations Management from Tecnológico de Monterrey.

Mathematical Modeling for Decision-Making: Understanding Optimization Models based on Correlation and Regression Analysis Insights

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