Optimizing Product Provenance Verification using Data Valuation Methods
This work addresses the problem of fraudulent trade practices in global supply chains, such as misrepresenting the origin of commodities like timber or agriculture, with an incremental improvement in data selection methods.
The paper tackles the challenge of verifying product provenance in supply chains by introducing a data valuation framework to improve training data selection for Stable Isotope Ratio Analysis models, resulting in enhanced model robustness and predictive accuracy across diverse datasets and geographies.
Determining and verifying product provenance remains a critical challenge in global supply chains, particularly as geopolitical conflicts and shifting borders create new incentives for misrepresentation of commodities, such as hiding the origin of illegally harvested timber or agriculture grown on illegally cleared land. Stable Isotope Ratio Analysis (SIRA), combined with Gaussian process regression-based isoscapes, has emerged as a powerful tool for geographic origin verification. However, the effectiveness of these models is often constrained by data scarcity and suboptimal dataset selection. In this work, we introduce a novel data valuation framework designed to enhance the selection and utilization of training data for machine learning models applied in SIRA. By prioritizing high-informative samples, our approach improves model robustness and predictive accuracy across diverse datasets and geographies. We validate our methodology with extensive experiments, demonstrating its potential to significantly enhance provenance verification, mitigate fraudulent trade practices, and strengthen regulatory enforcement of global supply chains.