Smart Drop-Shipping and Stocking Decision Support System

Drop-shipping is an increasingly important order fulfillment strategy in modern supply chains, allowing firms to reduce inventory holding costs by shipping products directly from suppliers to customers. However, because inventory is not directly controlled by the firm, drop-shipping can introduce uncertainty in product availability, delivery lead times, and service reliability. To compensate, firms often rely on expedited transportation, which increases costs and may negatively affect safety and efficiency in freight operations. These trade-offs create a challenging decision problem: determining which products should be stocked internally, fulfilled through drop-shipping, or managed under a mixed fulfillment strategy. Industry interviews with a major U.S. wholesaler indicate that firms tend to rely on drop-shipping for slow-moving products due to limited warehouse space and capital constraints, yet lack systematic, data-driven methods to guide these decisions Existing research largely focuses on single-product settings or coordination issues between retailers and suppliers and does not address multi-product decisions under warehouse capacity constraints. This project aims to fill this gap by developing an optimization-based decision support framework for drop-shipping and inventory planning across multiple stock-keeping units (SKUs). The proposed approach integrates mixed-integer programming with meta-heuristic methods to support large-scale, real-world applications. The model incorporates demand patterns, inventory holding costs, transportation costs, service level requirements, and cash flow constraints. A complementary simulation framework will be developed to evaluate system performance under uncertainty in demand, supplier inventory availability, and delivery times. The project supports Mid-America Transportation Center (MATC) themes of Safety and Transportation Systems of the Future by enabling more predictable and efficient freight movements, reducing reliance on expedited shipping, and promoting data-driven planning in distributed fulfillment networks. Expected outcomes include an implementable decision support tool, analytical insights for industry stakeholders, and dissemination through publications and conference presentations.

Language

• English

Project

• Status: Active
• Funding: $148,856.96
• Contract Numbers:

  69A3552348307

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Mid-America Transportation Center

  University of Nebraska-Lincoln
  2200 Vine Street, PO Box 830851
  Lincoln, NE  United States  68583-0851
• Project Managers:

  Bruner, Britain

• Performing Organizations:

  University of Missouri, St. Louis

  1 University Boulevard
  St. Louis, MO  United States  63121-4400
• Principal Investigators:

  Guo, Siqiang

  Li, Haitao

• Start Date: 20260601
• Expected Completion Date: 20270531
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Costs; Decision support systems; Delivery service; Freight service; Inventory control; Optimization; Shipping; Suppliers; Supply chain management; System design; Uncertainty; Warehousing
• Subject Areas: Economics; Freight Transportation; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01989399
• Record Type: Research project
• Source Agency: Mid-America Transportation Center
• Contract Numbers: 69A3552348307
• Files: UTC, RIP
• Created Date: May 16 2026 11:49AM