Data-Driven Hyperconnected Supply Chain Networks: Integrating Practical Constraints for Sustainable Development

Abstract

Present supply chain networks operate unsustainably economically, environmentally, and socially. Prior research in hyperconnected networks highlights solutions for many of these issues by creating a multi-tiered network of regional, local, and gateway hubs to connect sets of origin-destination pairs. These networks enhance efficiency, resilience, and sustainability by seamlessly linking transportation hubs, warehousing, and distribution centers, enabling faster, cost-effective, and low-emission goods movement across regions. While hyperconnected network research focuses on theoretical optimizations, it often overlooks spatial, infrastructural, and environmental constraints. Including the location of airports, train yards, ports, major highway intersections, and major metropolises can increase the model's fidelity. Networks based solely on flow may lose out on the practical implementation. There are often regulations surrounding types of facilities and truck movement throughout a city. This could change the design of the network to include two smaller hubs bordering the city vs. one larger hub within the city. Connecting the network to the real word by leveraging GIS and Costar data, this research integrates physical, environmental, and practical constraints encompassing hyperconnected networks. This network’s design will be linked to the United Nations Sustainable Development Goals for reducing emissions and an emissions reduction portfolio will be provided along with a projected timeline to net zero. Further, we assess this approach by applying it to multi-tier hyperconnected network for freight movement in Southeast USA. The results suggest that this approach can readily be extended to larger geographical regions, including nationwide applications or internationally.