Supply Chain Optimization driven by AI  – From fragmented reactiveness to proactive resilience 

Modern supply chains face increasing complexity from shifting customer demand, economic fluctuations, and competitive pressures. Many organizations rely on spreadsheets or basic tools that lack real-time insight and automated decision support. Optilon’s advanced solutions built around artificial intelligence and optimization address these challenges. 
  

Our solutions combine cloud analytics with advanced optimization to create a single source of truth for demand, supply, inventory, capacity, and transportation. By uniting probabilistic/ML‑based forecasting with optimization models (multi‑echelon, network, and routing), teams can quickly evaluate trade‑offs across cost, service, and sustainability. In an interactive interface, business users simulate scenarios across road, rail, air, and ocean, and instantly see the impact on service levels, lead time, cost, and CO₂ before execution. 
 

Our optimization and AI engines support extremely large problems using advanced computing acceleration, achieving significantly faster performance for complex models than traditional systems.   
 

AI plays a central role in several key areas:  

• Forecast accuracy: Machine learning evaluates multiple demand models in parallel and automatically selects the best fit per product, region, and horizon. 

• Optimization trade‑offs: Mathematical models (multi‑echelon, network, routing) quantify cost, service, risk, and environmental impact, enabling transparent, like‑for‑like scenario comparisons. 

• Solver acceleration: Modern optimization stacks pair proven algorithms with AI‑driven techniques and GPU acceleration; with NVIDIA CUDA‑X and first‑order methods (e.g., hybrid‑gradient/PDLP), very large models can see order‑of‑magnitude speedups, unlocking bigger scenarios and faster planning cycles. 
   

Decisioning platforms employ open standards and support human-in-the-loop workflows where teams can author decision logic, test scenarios, and deploy models without IT intervention, ensuring transparency and auditability. Beyond this, our platforms support enterprise-level deployment and collaboration across silos.