Seminar Department of Industrial Engineering Revenue Management for Rent Driven Industries by Dr. Ahmet Kuyumcu (Founder and CEO)

Revenue Management (RM) is a new frontier for the rent driven industries such as multi-family housing (apartment), self-storage, and senior housing. These industries share some of the same challenges: They have meaningful supply constraints with highly seasonal demand and uncertain lengths of stay. They also practice many incentives and promotions, and existing customer rate increases must be optimized. This presentation highlights RM challenges and opportunities for the rent driven industries and discusses the similarities and differences among them. It then provides details about how self-storage industry is adapting to the solution framework, which has been used by dozens of large owners and operators around the world. This RM solution won the 2017 Franz Edelman Award, the world's most prestigious award for achievement in the practice of advanced analytics and operations research.

Short Bio: Ahmet founded Prorize with the vision of building cutting-edge dynamic pricing solutions that will help global companies manage their prices effectively and achieve sustained revenue growth. Ahmet spearheaded the pricing solution that won the 2017 Franz Edelman Award. He has built end-to-end RM systems across a wide range of industries including hotel, gaming resort, airline, self-storage, apartment, senior housing, broadcasting, automotive, distribution, telecommunication, high-tech manufacturing, and on-line retailing. Previously Chief Scientist at Zilliant, Ahmet pioneered price optimization algorithms for the Zilliant's product suite from scratch. He also taught graduate-level courses in pricing and RM at the University of Texas at Austin, and at the Indian School of Business. Ahmet has authored and published several articles in professional journals and presented dozens of technical articles at conferences and universities around the world. He holds M.S. and Ph.D. degrees in Operations Research from Texas A&M University.

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Meeting ID: 969 4557 2901 Passcode: 335897