Multi-Stage Inventory Optimization and Machine Learning Analytics

 BorgWarner is transferring several friction plate product lines from Heidelberg to a new manufacturing facility in Rzeszów, Poland, and must have standard operating procedures set in place to ensure a smooth transition. On this project, the Tauber team was tasked with two main objectives:

  1. Create and define a cross-functional standard operating procedure for the pretest process to minimize inventory levels and optimize saturation batch sizes while maintaining service levels
  2. Create a prediction tool to systematically evaluate process parameters for friction plate production in order to maximize first pass yields for pre-production testing

The team created a combination of communication, scheduling, inventory, and Machine Learning tools to improve output and resolve current problems. Combined use of the deliverables will allow the Heidelberg facility to free up capital through inventory reductions and service level improvements by integrating dynamic lead times, scheduling optimization, and streamlined processes.

View team project summary
2016 Spotlight! BorgWarner

Student Team:

Kyle Gilbert – EGL (BSE Electrical Engineering/MSE Industrial and Operations Engineering)

Ryan Kennedy – EGL (BSE/MSE Industrial and Operations Engineering)


Project Sponsors:

Christian Bauer – Manager of Quality & Manufacturing Engineering

Volker Reiners – Supervisor of Friction Core Engineering

Stefan Ueberle – Director Operations – Product Strategy


Faculty Advisors:

Prakash Sathe – College of Engineering

Joseph Walls – Ross School of Business