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SPORD: A Simulation-Propose-then-OR-Dispose Approac... | AI Research

Key Takeaways

  • SPORD: A Simulation-Propose-then-OR-Dispose Approach for Supply Chain Planning introduces a new methodology to solve the complex, fragmented, and often slow...
  • For years, supply chain planning at e-commerce firms has operated as a collection of isolated projects.
  • To bridge this gap, we propose and implement the Simulation-Propose-then-OR-Dispose method, deployed as this http URL 's NetSim platform.
  • 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.
Paper AbstractExpand

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 this http URL '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.

SPORD: A Simulation-Propose-then-OR-Dispose Approach for Supply Chain Planning introduces a new methodology to solve the complex, fragmented, and often slow planning processes currently used in large-scale e-commerce supply chains. By decoupling the evaluation of business rules from the final optimization, the authors provide a unified framework that allows companies to move from static, manual planning to a dynamic, automated system that is both computationally efficient and transparent to stakeholders.

The Core Problem: Why Planning Fails

Supply chain planning at large firms has historically been plagued by three major barriers. First, "operational fragmentation" means that because every business unit has unique rules, analysts must build bespoke models from scratch for every task, which is time-consuming and difficult to maintain. Second, "computational intractability" arises because the sheer scale of modern supply chains—involving millions of products and complex routing—makes it impossible for traditional solvers to find an optimal solution quickly. Finally, there is an "implementation hurdle": executives often distrust "black-box" mathematical models, making it difficult to adopt the solutions even when they are technically optimal.

How SPORD Works

The SPORD method addresses these issues through a "Simulation-Propose-then-OR-Dispose" architecture. Instead of trying to force complex, non-linear business rules into a rigid mathematical formula, the system uses a two-step process: 1. Simulation-Propose: The system uses a massively parallel simulation engine to generate and evaluate all "operationally valid" candidate paths. This step absorbs all the idiosyncratic business logic—such as storage rules or routing constraints—that would normally break a standard optimization model. 2. OR-Dispose: Once the simulation has evaluated the costs and service impacts of these valid paths, an integer programming model (the "OR" part) selects the globally optimal subset.
By separating the "what is possible" (simulation) from the "what is best" (optimization), the system becomes modular and easier to configure for different business needs without requiring a total reconstruction of the model.

Computational and Operational Impact

To handle the massive scale of data, the authors implemented a matrix-vectorized simulation engine that runs on CPUs and GPUs, achieving speeds 10 to 100 times faster than traditional serial methods. They also utilized a list-scheduling algorithm to manage complex order dependencies in minutes rather than hours.
The method has been deployed as the "NetSim" platform at JD.com, where it has been used to optimize services for over 20,000 suppliers. Since 2025, the platform has demonstrated significant real-world results: the cross-regional fulfillment rate improved from 6.1% to 4.9%, and the system has achieved an average monthly carbon reduction of approximately 5,745 tCO2e.

Building Trust Through Transparency

A key advantage of the SPORD approach is its ability to turn skeptical executives into collaborators. Because the simulation engine provides transparent, verifiable outputs based on actual supply chain "physics" and business rules, stakeholders can see exactly why a specific path was chosen. This allows for "what-if" analysis, where executives can compare the proposed solution against alternatives, fostering the trust necessary to move from theoretical planning to active, real-world implementation.

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