Applying ML and AI to Enhance Dealer Business Systems Data

This session presents the approach to enhance the analysis and management of dealer business systems data in the agriculture, construction, and forestry equipment sector. Our challenge was the accurate identification of primary failed parts after equipment maintenance. Typically, this process requires considerable resources and time, as engineers manually analyze failures in complex work orders involving multiple parts. We developed an innovative solution combining AI/ML and Gen AI with Databricks. Our AI-driven model, developed through an integration with DataRobot, successfully predicts the primary failed component with remarkable accuracy and significantly refined our predictive capabilities. We also applied this to our advanced service parts forecasting. By leveraging the integral capabilities of Databricks, we streamlined a historically labor-intensive process but also set a new standard in predictive maintenance and lifecycle management.