Managing international assignments poses persistent cost and alignment challenges for global enterprises. This project applies predictive modeling and unsupervised learning to a proprietary cross-company dataset to enhance global mobility policy design. By forecasting assignment costs and segmenting employee profiles, we uncover patterns that link targeted benefits with improved return on investment and assignment success. Our results support the use of adaptive, data-driven mobility programs that reduce waste and increase strategic impact.
Watch the team present this project at 02:07:25 in the session recording here.
Keywords: global mobility, ROI, predictive modeling, clustering, cost forecasting, decision support, people analytics, international assignments
Faculty Advisor
Gizem Agar, PhD is an expert in data analytics, machine learning, and transformation with a passion for mentoring. With 15+ years of interdisciplinary academic and industry experience, her latest work is in manufacturing, pricing, logistics, and supply chain. She has received the CEO Award and Outstanding Achievement in Analytics awards for her contributions to Caterpillar.
Dr. Agar teaches Principles of Data Mining, Python for Analytics, Supply Chain Optimization, Capstone courses and advises students. She holds a PhD and MSc in Industrial Engineering from University of Oklahoma, and BSc in IE and a BSc in CE from Cankaya University, Turkiye. She was a visiting scholar at the Kuhne Logistics University (Hamburg, Germany) and at the Technical University of Vienna (Vienna, Austria).