Predictive Workload and Operations Scheduling

Introduction to GATX’s business with focus on rail car repair operations. The current process and preliminary data will be shared with students for initial exploration.The goal of the project is to predict expected labor hours required in several categories of repair (cleaning / commodity flare, mechanical, interior blast, interior lining, exterior paint) using known characteristics of the car. Accurate labor predictions will inform future loads on labor and can predict expected cycle times of repair events.

The data provided will be from production databases and will be shared in its raw, unclean state. It will need be cleaned/wrangled into something that is suitable for input into a machine learning algorithm.Once in a manageable state, the team will need to decide which data is relevant to the problem at hand. Feature selection can be justified with statistics or by knowing the relationship between the feature and the target.

The team will develop a method for applying predictive analytics and evaluate the effectiveness of their method.This method will be used by the business user(s) on an on-going basis to evaluate current loads and inform daily operational loading decisions. As such, the method must be well-defined and programmatic. Required inputs and generated outputs must be documented for repeatability.

Define how the technical output can be utilized by the business for sustained effectiveness.Identify any known gaps in the final model and suggest solutions for these gaps.