Author ORCID Identifier:
Date of Graduation
7-2026
Document Type
Thesis
Degree Name
Master of Science in Industrial Engineering (MSIE)
Degree Level
Graduate
Department
Industrial Engineering
Advisor/Mentor
Nachtmann, Heather
Committee Member
Hill, Bryan
Second Committee Member
Chimka, Justin
Keywords
Data Science and Analytics; Data-driven Supply chain; Literature Review; Next-generation Transportation and Logistics; Smart Logistics
Abstract
The increasing integration of data science and analytics into next-generation transportation and logistics planning and operations offers improved system performance but requires managing and interpreting vast and complex datasets. The Arkansas Smart Transportation Research, Infrastructure and Data-Driven Engineering Systems (AR-STRIDES) program (NSF Award No. 2445877) is working to develop data science solutions and data-driven supply chain technologies for next-generation transportation and logistics systems. This thesis supports AR-STRIDES by providing a comprehensive literature review of recent literature studying data science and analytics in transportation and logistics systems and conducting a regional analysis of Arkansas to create a database of companies, higher education institutions, and academic researchers working in related fields. The research findings highlight Arkansas’s strong foundation for innovating smart logistics and data-driven transportation solutions. The synergy between industrial capabilities from leading companies and academic research expertise strengthens Arkansas’s potential to advance operational efficiency, cost effectiveness, and resilience of next generation transportation and logistics systems.
Citation
Shams, K. (2026). Leveraging Data Science and Analytics for Next-Generation Transportation and Logistics: Literature Review and Regional Analysis. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/6346
