Generative Simulation Benchmarking for Circular Manufacturing Supply Chains

This article explores the use of generative simulation and zero-trust governance to optimize circular manufacturing supply chains, which traditional optimization methods struggle to handle.

Details

The author's research journey began with attempting to optimize a linear supply chain using reinforcement learning, but found that the models were blind to circularity principles. Exploring circular economy concepts, the author realized that traditional supply chain optimization conflicts with circularity, as linear chains optimize for throughput velocity while circular systems must optimize for material retention and value preservation. The key technical components explored in this work are: 1) Circular manufacturing supply chains, which introduce complex feedback loops and material degradation that traditional optimization struggles with; 2) Zero-trust governance architectures, which can cryptographically verify supply chain transactions without exposing proprietary data; and 3) Generative simulation benchmarking, which creates probabilistic scenario spaces that can be systematically tested to evaluate different governance policies. The author combined these three paradigms to create a framework that simulates circular supply chains with mathematically provable governance guarantees, enabling systematic exploration and optimization of circular economy strategies.