ARTIFICIAL INTELLIGENCE

Classical Optimization to Agentic AI

AI in Supply Chain: The adago Approach

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At adago, we build agentic AI that reasons, plans, and adapts like a human expert. By combining the rigor of classical optimization with the intuition of modern large language models, our systems continuously learn and self-improve. The result: supply chains that anticipate change, make decisions autonomously, and drive measurable performance across every level of operation.

Strategic Partnership

Transparency, Accessibility, and Continuous Learning

AI Reinforcement Learning
Every AI recommendation is auditable, scenarios can be defined effortlessly via chat, and the system constantly improves with new data. Implementing adago enables predictive intelligence, agile decision-making, and sustainable optimization that aligns profit with planetary responsibility.
AI Challenges

The Fundamental AI Challenge in Supply Chains

Supply chains are living, unpredictable ecosystems characterized by:

Extreme variability

Extreme variability

Demand spikes, geopolitical shocks, and machine failures.

Interconnected networks

Interconnected networks

Thousands of nodes spanning suppliers, CDMOs, and logistics partners.

Millisecond-level decisions

Millisecond-level decisions

Real-time adjustments for production schedules, inventory, and transport.

Competing objectives

Competing objectives

Cost efficiency vs. sustainability, service levels vs. risk mitigation.

Traditional optimization tools like Excel or legacy ERP modules—crumble under this complexity. They rely on static assumptions, while the real world is stochastic. Our AI doesn't just analyze data, it anticipates chaos, learns from disruptions, and proactively manages trade-offs.
Three Pillars

Three Pillars of Intelligent Supply Chain Management

Beispielbild

Deterministic Precision: Classical Optimization

  • What it solves: Structured problems with clear constraints (e.g., production scheduling, EOQ).
  • Technical edge: Mixed-integer linear programming (MILP) for fleet routing, capacity planning.
  • Industry impact: Reduces transport costs by 12-18% while respecting warehouse limits.
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Adaptive Intelligence: Reinforcement Learning (RL)

  • What it solves: Dynamic, chaotic environments (e.g., demand volatility, supplier bankruptcies).
  • Technical edge: Trains AI agents in a digital twin to learn policies through 10,000+ simulated scenarios.
  • Industry impact: Cuts safety stock costs by 25% while maintaining 99% service levels.
Beispielbild

Conversational Interface: Large Language Models (LLMs)

  • What it solves: Democratizing access to AI insights.
  • Technical edge: Natural language queries (e.g., “Find Q3 bottlenecks”) auto-translate into simulation parameters.
  • Industry impact: Reduces scenario setup time from days to minutes for non-technical teams.
Key Components

Key Components of AI-driven Optimization

  • Architecture: Apache Kafka for real-time data streaming, Monte Carlo methods for probabilistic scenarios.
  • Technical USP: Simulates multi-echelon networks (raw materials → finished goods) with 95% accuracy vs. real-world outcomes.
  • Industry value: Stress-tests CapEx decisions (e.g., new bioreactors) before investment.
Stochastic Simulation Engine
Moritz Kern, CEO

Moritz Kern

CEO

moritz.kern@adago.de
+49 1578 1521428

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