Generative Probabilistic Planning for Optimizing Supply Chain Networks
Hyung-il Ahn Santiago Olivar Hershel Mehta Young Chol Song
Supply chain networks in enterprises are typically composed of complex
topological graphs involving various types of nodes and edges, accommodating
numerous products with considerable demand and supply variability. However, as
supply chain networks expand in size and complexity, traditional supply chain
planning methods (e.g., those found in heuristic rule-based and operations
research-based systems) tend to become locally optimal or lack computational
scalability, resulting in substantial imbalances between supply and demand
across nodes in the network. This paper introduces a novel Generative AI
technique, which we call Generative Probabilistic Planning (GPP). GPP generates
dynamic supply action plans that are globally optimized across all network
nodes over the time horizon for changing objectives like maximizing profits or
service levels, factoring in time-varying probabilistic demand, lead time, and
production conditions. GPP leverages attention-based graph neural networks
(GNN), offline deep reinforcement learning (Offline RL), and policy simulations
to train generative policy models and create optimal plans through
probabilistic simulations, effectively accounting for various uncertainties.
Our experiments using historical data from a global consumer goods company with
complex supply chain networks demonstrate that GPP accomplishes
objective-adaptable, probabilistically resilient, and dynamic planning for
supply chain networks, leading to significant improvements in performance and
profitability for enterprises. Our work plays a pivotal role in shaping the
trajectory of AI adoption within the supply chain domain.