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Future Edelman Impact Award

Congratulations to this year’s award winner!

Select MSBAIM industry practicum students have a new analytics contest to compete in, one that will help prepare them for a top competition in their field. 

The Krannert School's Future Edelman Impact Award and contest aims to inspire students to produce their best data work and to strive to be a finalist or winner of a future INFORMS Edelman Award.

The Franz Edelman Award for Achievement in Advanced Analytics, Operations Research, and Management Science is administered by the Institute for Operations Research and the Management Sciences, or INFORMS. 

Krannert's competition, open only to teams of MSBAIM students, emulates the INFORMS competition.

"Our goal in creating this Future Edelman Impact Award is to give our students practice via their Industry Practicum course project in delivering empirically-supported work that can be understood by the layman and that has measurable impact," says Matthew Lanham, academic director of the MSBAIM program and a clinical assistant professor of management. 

"We want the experience and the way of thinking, designing, doing, and delivering analytics to be a catalyst for the students as they develop into leaders who will one day compete for the INFORMS award," he says. 

INFORMS is a collection of academic and industry experts whose mission is to "advance and promote the science and technology of decision making to save lives, save money, and solve problems." The Edelman Award is one of several INFORMS awards and is named to honor one of the fathers of operations research and management science, more commonly known as the field of analytics. Top entities in the world compete for the Edelman Award, and recent winners include the United Nations World Food Programme, INTEL Corporation, and UPS. 

Three teams of Krannert students collected votes via social media for the 2022 Future Edelman Impact Award. Voting closed June 21, 2022. 

The votes have been tallied

Congratulations to the 2022 Future Edelman Impact Award winners! This team of six rising business analytics professionals received more than 3k views and 1k "Likes" to secure the top position and earn the $1,000 prize.

Thank you to everyone who participated and voted.

A Hierarchical Approach and Analysis of Assortment Optimization

Assortment planning is one of the most important & challenging applications of analytics in retail. Often retailers use a two-stage approach where in the first stage they run thousands of prediction experiments to identify what best captures expected demand. In the second stage, they decide which combination of products will lead to the best sales for a particular store - a classic knapsack-type problem. This work in collaboration with a national retailer focuses specifically on combinatorial assortment optimization & how the hierarchical nature of the decisions & analysis can lead to drastically different outcomes with respect to in-store profitability.

IP Detective - Patent Infringement Detection Using BERT

Patents play a significant part in innovation and help individuals and companies safeguard and retain ownership of their ideas. However, patent infringement is common, and more than 2,500 patent infringement suits are filed each year. Currently, patent infringement detection is largely done manually, and companies spend approximately $600 to identify each case of infringement. Our work provides an approach to automate this process through machine learning. Our model first vectorizes patent text using a BERT model trained on patent text, and then calculates similarity scores between competing patent claims. The overall score is then calculated by taking a weighted average of the subsection similarities, where the weights were calculated by training a logistic regression model based on historical cases of infringement. Looking at subsection scores along with the overall score, we can identify potential infringement of two competing patent claims rather accurately.

An Automated A / B Testing and Measurement Framework

Organizations that run a multitude of A/B tests can gain immense value from having an automated framework. In collaboration with a data science consulting company, we developed a parametrized accelerator to significantly reduce time to market for data scientists while performing A/B and A/A testing under varying circumstances using Python programming.