SPORD: A Simulation-Propose-then-OR-Dispose Approach for Supply Chain Planning

2026-07-23

AI Summary

This study addresses three critical challenges in e-commerce supply chain planning—model fragmentation, computational non-scalability, and low decision credibility—by proposing the SPORD framework. SPORD introduces a novel decoupled simulation-and-optimization architecture that transforms simulation from a monitoring tool into a planning engine: feasible logistics paths are first generated via simulation and then an integer programming model selects the globally optimal subset, enabling end-to-end collaborative planning. The method integrates matrix-vectorized CPU/GPU-accelerated simulation, list scheduling, and integer programming to efficiently solve large-scale instances with numerous SKUs and network nodes. Furthermore, a closed-loop intelligent diagnostic mechanism enhances solution interpretability and reusability. Since its deployment in 2025, SPORD has served over 20,000 suppliers, reducing cross-regional fulfillment rates from 6.1% to 4.9% and achieving an average monthly carbon reduction of approximately 5,745 metric tons of CO₂ equivalent.

Abstract

For years, supply chain planning at e-commerce firms has operated as a collection of isolated projects. Each planning task from static network planning to dynamic warehouse assortment planning requires analysts to spend weeks building models from scratch, calibrating and persuading executives to act on outputs they cannot verify. Three barriers drive this: bespoke models proliferate because standardization is difficult (operational fragmentation); once unified, the combinatorial scale of millions of SKUs, thousands of nodes, and intricate routing logic exceeds what solvers can handle within a tight window (computational intractability); and a mathematically optimal solution still fails to be implemented if the executives do not trust it (implementation hurdle). To bridge this gap, we propose and implement the Simulation-Propose-then-OR-Dispose method, deployed as JD.com's NetSim platform. The central insight is decoupling: simulation proposes by generating and evaluating the full set of operationally valid candidate paths-absorbing all idiosyncratic business logic, while an integer program disposes by selecting the globally optimal subset. Computationally, matrix-vectorized CPU/GPU accelerated simulation achieves a 10-100 times speedup over serial methods, and a list scheduling algorithm reduces coupled-order processing from hours to minutes. Operationally, we establish a closed loop via an intelligent diagnosis engine. Since 2025, NetSim has optimized end to-end services for over 20,000 suppliers, the cross-regional fulfillment rate dropped from 6.1% to 4.9%, and the average monthly carbon reduction is approximately 5,745 tCO2e. SPORD moves simulation from monitoring to active planning. The transparent outputs turn skeptical executives into engaged collaborators, and the modular architecture ensures that the next planning requires just configuration, not reconstruction.

Problem

• operational fragmentation
• computational intractability
• implementation hurdle
• supply chain planning
• trust gap

Innovation

• SPORD
• simulation-optimization decoupling
• GPU-accelerated simulation
• integer programming
• supply chain planning