The author embarked on a research journey after discovering that most AI models for supply chain optimization neglect circularity principles, treating systems as linear. This realization sparked an exploration into generative simulation and zero-trust governance for sustainable, circular manufacturing supply chains.
This research proposes a framework for uncertainty-aware functional prediction and material fatigue assessment for returned products in circular factories, using an angle grinder as an example. It combines current tool state and usage windows with a convolutional encoder and LSTM backbone to predict future function fulfillment and component integrity.
The author describes a eureka moment while modeling circular manufacturing supply chains using Graph Neural Networks in scenarios of extreme data sparsity. The key insight was to embrace the inherent uncertainty through probabilistic inference techniques, rather than trying to force more data into the system.
This article describes a researcher's frustration with extreme data sparsity in circular manufacturing supply chains for rare-earth magnets. The research was sparked by a dilemma between collecting more data or forcing sharing, leading to an epiphany about active learning for rare-event detection and privacy preservation.