2024 Inorganic Ventures Team

The Challenge

MSBA-BA graduate students Alex Tellis, Jayme Bailey, Axel Gomez, and Scott Braatz partnered with Dr. Brian Alexander to assess the feasibility of integrating machine learning software into Inorganic Ventures’ workflow. Their goal was to determine if and how machine learning could enhance time efficiency and decision-making processes, particularly for the company’s chemists. The project focused on identifying both economic and qualitative benefits of the software, aiming to boost competitiveness and operational efficiency.

The Solution

The team developed a machine learning model and accompanying framework to integrate the software into Inorganic Ventures’ daily operations. They found that using machine learning for model predictions could significantly reduce the time required for chemists to complete their tasks, leading to increased efficiency and employee satisfaction. The economic analysis revealed substantial time savings, which translated into positive value for the company. Additionally, the software’s ability to handle small, noisy, or sporadic data sets ensured its robustness across various applications. The team recommended expanding the use of this machine learning software across different departments to maximize benefits, improve workflow, and enhance the company’s competitive advantage.