Generative AI and Mathematical Optimization in Supply Chain Decision-Making
AI is everywhere in supply chain conversations right now. But there’s still a gap between what AI promises and what real-world decision-making demands.
This whitepaper lays out AIMMS’ perspective on AI.
What's inside:
– AIMMS’ view on the role of generative AI in supply chain decision-making
– The limitations of using AI as a standalone decision engine
– Why optimization is critical for feasibility, accuracy, and scale
– Where generative AI fits across the optimization lifecycle
– How to design decision systems that balance speed, transparency, and control
Who this is for:
– Supply chain and operations leaders evaluating AI investments
– Analytics and CoE teams building decision models
– Organizations looking to scale decision-making without increasing risk
The core insight:
Generative AI and optimization are not competing approaches.
They solve different parts of the problem.
– Generative AI helps translate intent, accelerate model building, and explain outcomes
– Mathematical optimization ensures decisions are feasible, optimal, and defensible
Rely on one without the other, and you either slow down or take on unnecessary risk.
This whitepaper shows how to get the balance right.
See how AI and optimization work better together
Download the whitepaper to understand how AIMMS sees the future of AI-powered decision-making.