This paper introduces an end-to-end framework using Liquid Neural Networks (LNNs) for adaptive supply chain control, addressing challenges like demand volatility and bullwhip effect. The framework includes a Liquid Time-Constant encoder, neural ODE policy network, CfC layer, and a meta-learning adaptation mechanism. It aims to provide stable, interpretable, and computationally efficient control policies that adapt in real-time to disruptions.
Key findings
LNNs offer superior expressivity, stability, and rapid adaptation to distributional shifts compared to traditional recurrent architectures.
The proposed framework integrates a Liquid Time-Constant encoder, neural ODE policy network, CfC layer, and meta-learning adaptation mechanism for supply chain control.
The architecture addresses limitations of existing methods by providing interpretable, stable, and computationally efficient control policies.
Potential reductions in total supply chain costs of 15–30% and significant mitigation of the bullwhip effect compared to existing deep learning approaches.
Limitations & open questions
The proposed framework's performance in real-world supply chain scenarios needs to be validated.
The generalization capabilities of the framework to various supply chain configurations require further investigation.