Date of Graduation

5-2026

Document Type

Thesis

Degree Name

Bachelor of Science in Data Science

Degree Level

Undergraduate

Department

Data Science

Advisor/Mentor

Karl Schubert

Committee Member

Adam Varga

Second Committee Member

Jeff Ervin

Abstract

Visibility of inbound freight is critical for managing operational efficiency, yet many organizations lack standardized compliance metrics for third-party carriers to uphold, preventing them from utilizing tracking data to make data-driven decisions. During a summer internship with the Transportation Department at O’Reilly Automotive, data inconsistencies were addressed in the Transportation Management System (TMS), and that data was utilized to create tracking compliance standards for third-party carriers. Data populated from various sources within O’Reilly’s TMS was cleaned, validated, and utilized to create a Tracking Scorecard that evaluates message transmission rates, timeliness, and errors. This tool provides actionable insights to improve tracking data reliability that could prove useful to multiple departments. The scorecard includes key performance indicators (KPIs) such as weighted compliance metrics, shipment-level completeness classifications, and visualizations so the report can be interpreted briefly. Additionally, a TMS Health Report was developed to monitor daily processing of messages into the TMS to enable early detection of data integration issues. The implementation of these tools improves the detection of integration issues and the overall quality of O’Reilly’s data that lives in the TMS system. This internship demonstrates that establishing clear standards for third-party carriers, providing practical ways to meet them, and proactively monitoring data quality are critical steps to enable informed decision-making in transportation logistics.

Keywords

tracking; transportation analytics; tracking compliance; O'Reilly Automotive

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