Quantum-Inspired Optimization for Supply Chain Logistics and Inventory Management in E-commerce

Abstract

E-commerce platforms face ever more complex supply-chain logistics and inventory-management challenges: multi-tier warehouses, delivery-fleet routing, high-dimensional SKU portfolios, demand volatility, returns, seasonality and cost pressures. Traditional optimisation approaches (integer programming, heuristics, meta-heuristics) increasingly strain under scale, real-time demands and combinatorial explosion. Meanwhile, quantum-inspired optimisation leveraging methods such as quantum annealing, coherent Ising machines, variational circuits and QUBO (Quadratic Unconstrained Binary Optimisation) mapping offer near-term practical approaches to combinatorial logistics and inventory problems. This paper proposes a comprehensive framework for applying quantum-inspired optimisation to e-commerce supply-chain logistics and inventory management: we provide an extended literature review of classical and quantum-inspired methods; we derive full mathematical formulations for logistics/inventory problems and map them into QUBO/quantum-inspired frameworks; we design a benchmarking/implementation methodology; we discuss industry architecture and deployment issues (data pipelines, latency, hybrid classical/quantum-inspired workflows, interpretability, cost-benefit); we present use-cases and discuss practical adoption in e-commerce. Our findings suggest that while fully fault-tolerant quantum computing remains some years away, quantum-inspired optimisation can deliver meaningful improvements in logistics and inventory decisions today, particularly for large-SKU e-commerce, multi-warehouse networks and time-sensitive delivery.