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
Sam Jeffcoat
Second Committee Member
Donnie F. Williams Jr.
Abstract
This undergraduate thesis explores how data analytics and engineering judgment are used to support pricing decisions in the less-than-truckload (LTL) freight market. It’s based on an internship with ArcBest Corporation. It explains the company’s background, its role in the LTL market, and the responsibilities of a Pricing and Supply Chain Engineer within the Yield department.
Most of the internship was spent evaluating requests for proposals (RFPs), in which a negotiating third party provides a customer’s shipment data that must be cleaned, analyzed, and translated into a comprehensive pricing offer. Using the Data Science Analytics Process as a framework, this thesis explains how Pricing Engineers understand the request, evaluate the data, model projected profitability, interpret the results, and communicate the final offer. The analysis focuses on the major components of LTL pricing, including lane structure, base rates, discounts, minimum charges, fuel schedules, accessorials, shipment characteristics, and internal cost projections. The literature connects these responsibilities to broader industry topics such as freight classification, dimensional pricing, shipment-level costing, and the increasing use of data-driven pricing methods.
This thesis contributes to a practical understanding of how data analytics supports pricing strategy in LTL freight. It also considers how artificial intelligence could improve new business RFP processes through decision-support tools such as internal chatbots, automated data extraction, and account-specific models for recurring bid formats. However, because pricing decisions require context, judgment, relationship management, and accountability, the thesis argues that AI should support Pricing Engineers rather than replace them.
Keywords
Less-than-Truckload; Artificial Intelligence; Request-for-Proposal; Dynamic Pricing
Citation
Levin, L. C. (2026). Reimagining Less-than-Truckload Pricing Development in Competitive Bid Environments with Artificial Intelligence. Data Science Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/dtscuht/33
