Deep Topological Intelligence for Sustainable Supply Chain Management

The objective of this chapter is to develop Deep Topological Intelligence (DTI) as a structural framework for sustainable supply chain management, optimizing logistics while preserving viability, redundancy, and resilience. “Structural sustainability” is implemented by building a sea–air–rail–road logistics graph with cost, time, capacity, and emissions; simulating disruptions and targeted attacks; learning latent representations with autoencoders; and testing structural conservation with persistent homology, Betti curves, and Wasserstein distances. Contributions include a topology-informed digital-twin view (sustainability as persistence of form under perturbation), a reproducible workflow linking KPIs to homological indicators, a protocol that flags “efficient but fragile” AI designs, and a study of model capacity versus topological fidelity. Higher-capacity autoencoders preserve loop structure with minimal H_1 deformation (W≈0.054), while H_0 connectivity is more sensitive, revealing fragility missed by KPIs and supporting carbon-aware redesign without eroding redundancy.