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Improving Forecast Accuracy for New Product Launches Using Machine Learning Models

The Tauber team, using business analytics, developed a machine learning solution to better forecast new product demand. First, the team developed an algorithm to select the most appropriate like products based on product characteristics. In addition, prediction algorithms were developed to directly forecast demand without using like product selection. To help ensure the realization of expected cost savings, the Tauber team made recommendations for wide-scale implementation of the new process throughout the company.

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View team project summary
2016 General Mills team at Spotlight!

Student Team

Tania Martinez Garcia - Master in Supply Chain Management

Erik Knapp - EGL (BSE/MSE Industrial and Operations Engineering)


Project Sponsors

Beth Blaylock - Supply Chain Initiative Leader

Gary Donahue - Supply Chain Initiative Leader

Christine England - Sr. Manager, Technology & Analytics

Dave Engler - Director, HMM & Supply Chain Strategy

Ethem Ucev - Analytics Consultant, Supply Chain

Carol German - Program Manager, HMM & SC Strategy


Faculty Advisors

Joline Uichanco - Ross School of Business

David Kaufman - College of Engineering

Project Photos

2016 General Mills team at Kickoff