Our client is the maintenance planning department of a leading operator in the North American railroad industry with a fleet of 200,000+ railcars and an annual planning budget of about USD 1 B.

Wheel failure is a major maintenance activity for the client and estimating wheel replacement costs are crucial for accurate budget planning. Accurate forecasting is needed for optimum wheel inventory planning. The client wanted to improve budget planning and wheel inventory planning by forecasting wheel failures by the wheel size and the fleet type.

• Large data volume with up to 30 years of historical wheel failure data
• Difficulty in training model using historical data, which includes data of wheels that have not failed yet
• Difficulty in accounting for weather changes for more accurate failure forecasting – Weather is different over the years (e.g. cold versus warm winters)