Leveraging Generative AI for Supply Chain Optimization and Simulation

EasyChair Preprint no. 12930

Abstract

Supply chain optimization and simulation stand at the forefront of enhancing operational efficiency and resilience in today's complex business environment. This abstract explores the application of generative AI in optimizing supply chain networks, encompassing facility location planning, transportation routing, inventory allocation, and scheduling. Additionally, it delves into how simulation models powered by generative AI can simulate diverse scenarios to identify optimal solutions and mitigate risks.

Generative AI, with its ability to generate synthetic data and simulate complex scenarios, offers transformative potential in supply chain optimization. In facility location planning, generative AI algorithms analyze demographic data, market trends, and transportation costs to identify optimal locations for warehouses, distribution centers, and production facilities. By synthesizing diverse scenarios, generative AI facilitates robust decision-making, enabling organizations to minimize costs and maximize service levels.

Transportation routing, a critical component of supply chain management, benefits significantly from generative AI-driven optimization. Advanced routing algorithms powered by generative AI consider factors such as traffic patterns, delivery priorities, and vehicle capacities to optimize delivery routes and schedules. Moreover, generative AI enables real-time adaptation to dynamic conditions, ensuring efficient and cost-effective transportation operations.

Keyphrases: Facility location planning, Generative AI, Inventory allocation, simulation, Supply Chain Optimization, Transportation routing

@Booklet{EasyChair:12930,
  author = {Dylan Stilinski and Lucas Doris and Louis Frank},
  title = {Leveraging Generative AI for Supply Chain Optimization and Simulation},
  howpublished = {EasyChair Preprint no. 12930},

  year = {EasyChair, 2024}}