In a world of uncertainty, logistics firms must predict how many resources will be needed to achieve service objectives. According to the recent development of a statistical uncertainty model by Maryland Smith’s Ilya Ryzhov, predicting such costs is within reach.

Maryland Smith’s Ilya Ryzhov is leveraging a three-year grant awarded by the National Science Foundation to continue research on predictive and prescriptive methods for humanitarian logistics and disaster mitigation.

The Smith School is happy to welcome the following new professors to the school: Accounting & Information Assurance Hanna Lee Derek Johnson Decisions, Operations & Information Technologies Tunay Tunca Inbal Yahav Ilya O. Ryzhov Pamela Armstrong Ilchul Yoon Rui Zhao

For B2B sellers, knowing how much the buyer is willing to pay is difficult. New research from Maryland Smith is helping to figure it out.

How do you make good decisions when you don’t have all the information in front of you? New research lays out a framework to learn from indirect or incomplete information to make better decisions.

Managers who rely on computer models to help with decision-making bump into a dilemma when it comes to allocation of scarce resources in complex environments with many moving parts.

Political candidates, manufacturers and even online game designers can hit their performance targets with increased regularity using a new algorithm developed at Maryland Smith.