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Atoptima Leverage Decision-Making AI at the core of your Supply Chain

What is Atoptima?

Atoptima offers powerful optimization solvers designed to enhance operational performance and promote sustainability within supply chains. By integrating advanced decision-making AI, the platform addresses complex constraints in transportation, packing, warehouse operations, and production scheduling. This approach moves beyond simple digitalization, providing sophisticated decision support to improve resource allocation, increase efficiency, and reduce environmental impact.

The technology leverages over 25 years of expertise in mathematical optimization, delivering significant improvements in logistics costs, greenhouse gas emissions reduction, and operations planning time. It empowers teams with reliable automation, leading to better resource assignment, substantial cost savings, and enhanced quality of service by enabling businesses to focus on their core activities and meet customer expectations more effectively.

Features

  • RouteSolver: Optimizes multi-trip, multi-echelon, FTL, LTL, reverse logistics, and network design.
  • PackSolver: Optimizes parceling, palletization, and 3D loading for trucks and cargo.
  • PickSolver: Manages order orchestration, batching, picking optimization, and slotting.
  • PlanSolver: Handles production planning, lot-sizing, and task scheduling.
  • Assembled Solvers: Allows building custom solutions by combining different software bricks.
  • Decision-Making AI: Automates complex decision processes for operational efficiency.
  • Mathematical Optimization Engine: Leverages 25+ years of research for superior performance.
  • Decarbonization Focus: Reduces carbon footprint by minimizing resource use through optimized planning.

Use Cases

  • Optimizing complex transportation routes for logistics providers.
  • Improving packing efficiency for warehouses and shipping companies.
  • Streamlining order picking processes in distribution centers.
  • Scheduling production tasks efficiently in manufacturing plants.
  • Reducing logistics costs and GHG emissions for supply chain managers.
  • Enhancing decision support systems for software editors.
  • Developing tailored optimization tools for domain experts.

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