Date of Graduation

5-2024

Document Type

Thesis

Degree Name

Bachelor of Science in Data Science

Degree Level

Undergraduate

Department

Data Science

Advisor/Mentor

Schubert, Karl

Committee Member/Reader

Wolf, Jared

Committee Member/Second Reader

Shipp, Justin

Abstract

In the context of intermodal transportation, understanding the dynamics of award compliance holds significant importance for operational efficiency and strategic decision-making. Award compliance refers to the percentage of awarded freight volume that is realized, indicating the extent to which contractual agreements are fulfilled. This analysis delves into the intricate relationship between customer characteristics and award compliance, aiming to provide valuable insights into the variability and predictability of compliance rates. By analyzing Request for Pricing (RFP) data and primary awarded freight volumes, the study seeks to address the need for more accurate volume estimations, crucial for sales planning, revenue projections, and network optimization within JB Hunt Intermodal's freight operations. Currently reliant on business acumen and PowerBI reporting, this research aims to enhance existing methodologies by uncovering underlying patterns and seasonality in compliance behaviors. Through cautious investigation and analysis, this thesis aims to offer actionable recommendations for optimizing future RFP decisions and rates, ultimately contributing to improved market balance and operational efficacy in the intermodal freight industry. By bridging the gap between data-driven analysis and industry expertise, this analysis not only advances academic understanding but also offers practical implications for enhancing customer satisfaction and profitability within the intermodal transportation sector.

Keywords

transportation logistics, predictive modeling, compliance behavior

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