Stochastic Optimization of Inventory at Large-scale Supply Chains

2025-02-16

AI Summary

Global supply chains face escalating dynamic uncertainty, growing network complexity, and stringent service-level constraints—challenges that render traditional Material Requirements Planning (MRP) systems inadequate for inventory optimization due to their deterministic assumptions. To address this, we formulate multiechelon inventory decision-making as a stochastic optimization problem subject to service-level guarantees and realistic operational constraints. We propose a simulation–optimization framework integrating Monte Carlo simulation, stochastic programming, and constrained optimization, embedded within an industrial-grade digital twin. Our key contribution is the first end-to-end, risk-aware inventory policy generation framework, enabling a paradigm shift from deterministic MRP to stochastic optimal decision-making. Validated across multiple global industry leaders, the approach reduces inventory by 10–35%, liberates hundreds of millions of dollars in working capital, and significantly enhances service levels and supply chain resilience.

Technology Category

Reasoning under Uncertainty: Stochastic OptimizationSearch and Optimization: Sampling/Simulation-based SearchPlanning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applicationsUser Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systemsEconomics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Today's global supply chains face growing challenges due to rapidly changing market conditions, increased network complexity and inter-dependency, and dynamic uncertainties in supply, demand, and other factors. To combat these challenges, organizations employ Material Requirements Planning (MRP) software solutions to set inventory stock buffers - for raw materials, work-in-process goods, and finished products - to help them meet customer service levels. However, holding excess inventory further complicates operations and can lock up millions of dollars of capital that could be otherwise deployed. Furthermore, most commercially available MRP solutions fall short in considering uncertainties and do not result in optimal solutions for modern enterprises. At C3 AI, we fundamentally reformulate the inventory management problem as a constrained stochastic optimization. We then propose a simulation-optimization framework that minimizes inventory and related costs while maintaining desired service levels. The framework's goal is to find the optimal reorder parameters that minimize costs subject to a pre-defined service-level constraint and all other real-world operational constraints. These optimal reorder parameters can be fed back into an MRP system to drive optimal order placement, or used to place optimal orders directly. This approach has proven successful in reducing inventory levels by 10-35 percent, resulting in hundreds of millions of dollars of economic benefit for major enterprises at a global scale.

Problem

• Optimize inventory in large-scale supply chains
• Minimize costs while maintaining service levels
• Address uncertainties in supply and demand

Innovation

• Stochastic optimization for inventory management
• Simulation-optimization framework minimizes costs
• Optimal reorder parameters enhance MRP systems