Wild variability in lane by lane shipment schedule
Sending less than full loads or more loads than necessary across all lanes
Understanding the dynamics between existing contracts and the spot market
Experiencing detention and demurrage fees across multiple sites
Facilities hitting capacity issues around both transportation and inventory space
Replanning takes too long when an issue occurs, and doesn’t consider the right variables
LevelLoad takes in all of your network demand, order, forecast, and transportation data along with the full constraints of the network, runs a daily optimization, and outputs a globally optimized replenishment transportation schedule that can be tendered by lane across the whole network.
LevelLoad’s supply chain analytics solution uses a revolutionary combination of linear programming and reinforcement learning to create replenishment transportation schedules across a network for the intermediate future.
Linear programming solves the network to identify the most optimal way to smooth shipments given the network constraints.
Reinforcement learning optimizes each shipment, ensuring that all containers are loaded as optimally as possible.
Our founding team is made of supply chain veterans and AI research experts. Together, we have combined our ex-pertise to bring a solution to market that is both practically and technologi-cally unlike anything the supply chain world has ever seen.
Lower Transportation Costs
Send Less Shipments
Improve Order Fill
Better Utilize Space
Integrate Easily
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