Research Paper

Core Innovation

This paper introduces the Simulation-Propose-then-OR-Dispose (SPORD) method that decouples simulation and optimization to handle complex supply chain planning. It uses accelerated matrix-vectorized simulation to generate candidate paths incorporating business logic, then applies integer programming to select optimal subsets, overcoming scale and trust barriers in prior approaches.

Why It Matters

Supply chain planning often suffers from isolated models, slow computation, and lack of executive trust, leading to inefficiencies and poor implementation. SPORD streamlines planning by generating operationally valid options and selecting optimal solutions quickly, improving fulfillment rates and reducing carbon emissions. This approach scales across thousands of suppliers and complex networks, enabling continuous, trusted optimization.

Potential Customers & Pain Points

• E-commerce firms – Fragmented planning and slow model building
• Supply chain analysts – Computational intractability and lack of trust in outputs
• Logistics managers – Need for scalable transparent decision tools
• Executives – Difficulty verifying and implementing optimization results

Business Model

Enterprise SaaS platform licensed to e-commerce and logistics companies, with tiered pricing based on scale and features including simulation acceleration, optimization modules, and analytics dashboards.

Competitive Landscape

• Llamasoft (Coupa)
• Blue Yonder
• Kinaxis
• Manhattan Associates

Implementation Challenges

• Integration with diverse legacy systems and data sources
• Executive adoption requiring trust in automated recommendations
• Scaling integer programming solvers for extremely large networks

Validation Strategy

• Pilot deployments with major e-commerce suppliers to measure fulfillment and carbon reduction
• User feedback loops to improve trust and transparency features
• Benchmarking against existing planning tools on speed and solution quality