Inferring Inventory Dynamics from Supply Chain Networks: A Graph Learning Approach with Autonomous Validation

2026-07-12

AI Summary

This study addresses the challenge faced by small and medium-sized enterprises (SMEs) in measuring supply-demand imbalances due to the absence of systematic inventory records and severe scarcity of labeled data. To this end, the authors propose a multi-agent semi-supervised inference framework that integrates graph-based machine learning models incorporating supply chain network topology and production function constraints to infer enterprise inventory dynamics. The work innovatively reframes the label scarcity problem as a multi-agent collaborative task and introduces a novel unlabeled self-validation mechanism grounded in economic theoretical consistency. Leveraging five econometric models—capturing spatial spillovers, dynamic persistence, causal direction, shock propagation, and supply-demand forecasting—and a multi-agent consensus fusion technique, the approach yields predictions whose causal structures and network transmission mechanisms align closely with established economic theory, demonstrating both effectiveness and robustness in inventory change prediction without access to ground-truth labels.

Abstract

Supply-demand mismatch represents a fundamental challenge in supply chain management, yet its direct measurement remains particularly elusive for small and medium-sized enterprises (SMEs).These firms typically lack systematic inventory records, leaving labeled training data critically scarce. Conventional supervised learning methods rely heavily on labeled samples, rendering them ill-equipped to reliably validate firm-level predictions under such data-scarce conditions. To resolve this unlabeled-data dilemma, we develop a multi-agent semi-supervised inference framework that reframes the label-scarcity problem as a structured, collaborative task distributed across specialized agents. We first construct a production-function-constrained graph machine learning model that infers firm-level inventory changes directly from supply chain network topology. A dedicated econometric validation agent then concurrently loads five econometric models (spanning spatial spillovers, dynamic persistence, causal direction, shock transmission, and supply-demand forecasting) to generates structured economic evidence from complementary dimensions. An expert review agent synthesizes the structured econometric evidence and produces a unified consistency assessment by resolving cross-agent inconsistencies. Empirical results demonstrate stable predictive performance on inventory-change forecasting tasks. Multi-agent econometric validation further confirms that predicted inventory dynamics align closely with established economic theory in terms of causal structure and network transmission mechanisms. Critically, the proposed agent framework enables effective prediction verification even when ground-truth observations are unavailable.

Problem

• inventory dynamics
• supply chain networks
• label scarcity
• small and medium-sized enterprises
• supply-demand mismatch

Innovation

• graph learning
• semi-supervised inference
• multi-agent validation
• inventory dynamics
• supply chain networks