Capstone – Project

Predictive Workload and Operations Scheduling

A Collaboration Between

Engagement Synopsis

A few potential opportunities that vary in technical and political complexity. I need to line up support internally before being able to commit to any of the following:

A) Predicting work content
We operate a number of heavy repair facilities for performing maintenance and upgrades on our railcar fleet. When a car is scheduled for maintenance and arrives at a shop, the extent of labor required is not known until the car is thoroughly inspected and an estimate is written. Miles traveled, # loads/unloads, commodity properties, customer handling, etc. all can influence the amount of repair work required. Being able to better predict maintenance would improve scheduling of rail cars and allocation at different shops.

We have a rudimentary system in place based on random forests built in Python (pandas, scikit-learn, Orange) / Excel, but it needs refinement.

Blue sky objective would be to build this insight into a tool that helps direct shop loading decisions (e.g., can we justify sending this car 500 miles further away to a shop with more available capacity?).

B) Predictive Maintenance
Our engineering group is dipping their toes in this space. They recently completed a project to predict the need to replace worn wheels in advance of exceeding a threshold which allows the railroad to perform the replacement at a high cost to us. Not sure what else our engineers might be pondering, but I’ll look into it.

C) Inventory Optimization
We are in very high-mix / low-volume space with many of our externally sourced components and also experience variable demand and lead times. There is an opportunity for analysis of current material management costs (carrying costs, stockout cost, inventory space) and optimum management strategy.

Company Information

StageLarge Enterprise
Hiring PotentialN/A

Company Overview

GATX Rail North America operates an extensive network of maintenance facilities in the United States and Canada dedicated to performing safe, timely, efficient, and high quality railcar maintenance services for customers.

Industry Mentors

Company Admin


[email protected]

Course Info & Engagement Details

SchoolMasters in Business Analytics (MSBA)
Engagement Format -
CourseMSBA Capstone (SPRING 2020)
Students Enrolled5 Students per Group (61 Enrolled in Program)
Meeting Day & TimeMonday OR Wednesday (3:00 - 4:50 PM ET)
Student Time Commitment8-15 Hours Per Week
Company Time Commitment2 Hours
Duration13.29 Weeks

Project Topics

Academic Mentors

There are currently no supervisors assigned.

Assigned Students

There are currently no students assigned.

Program Timeline

Touchpoints & Assignments Due Date Submission

Key Milestones & Project Process

  • February 7, 2020 - Define

    Introduction to GATX’s business with a 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.

    Suggested Deliverable:

    • Define data scope (time period, data items)
    • Finalize milestones and project charter
  • February 28, 2020 - Measure/Analyze

    The data provided will be from production databases and will be shared in its raw, unclean state. It will need to 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.

    Suggested Deliverable:

    • Feature selection
    • Regression model selection
  • March 27, 2020 - Improve

    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.

    Suggested Deliverable:

    • Specification of the training input file (time scope, file format, column names, data types, etc.)
    • Specification of the prediction input file
    • Specification of the output file
    • Training program/script
    • Final prediction program / script
    • Evaluation of model (cross-validation scores [R², RMSE, MAE, etc.] and known issues)
  • April 17, 2020 - Control

    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.

    Suggested Deliverable:

    • Mockups/suggestions for the front-end user interface for use by the operations scheduler and shop management
    • The final report out (slide deck, white paper, or similar) targeted toward management
      –Audience for the report should be assumed to be technical (familiar with stats) but not experts in ML or predictive analytics
      –Target ~15-minute presentation or ~5-minute read

Project Resources

There are no resources currently available