For many supply chains, transportation costs are one, if not the largest, of all operational costs. Transportation networks have often developed over time, instead of through intentional design. This lack of intelligent design costs organizations millions of dollars annually as planners struggle with making transportation decisions related to balancing the size of a private fleet with a mix of common carriers, determining a robust strategy for mode selection, and designing optimal routes that minimize transportation costs and empty miles. With shrinking capacity, rising costs, and rapidly changing customer demands, making these decisions is becoming even more complex.
The llama.ai platform gives you a wide range of tools to analyze and improve your transportation decision-making process and establish optimal policies. Continuously improve your end-to-end transportation network and quickly adapt to business changes to stay ahead of the competitive curve.
- Test and understand trade-offs for ocean vs. air, truckload vs. LTL, rail vs. road, or parcel to pick the most cost-effective mode given your global supply chain network flows and costs
- Identify shipment consolidation opportunities to reduce transportation costs within multi-stop routes
- Determine optimal footprint for new or existing sourcing, production and distribution sites, as well as optimal DC-to-customer locations to achieve the lowest cost solution that meets service level targets
- Determine optimal shipment schedules for multi-stop vehicle routes to minimize costs while adhering to shipment frequency requirements and load balancing objectives
- Identify the most efficient use of your vehicles, containers, or warehouse capacity by modeling and optimizing the transportation network as it relates to customer service
- Determine optimal private fleet size and asset mix
- Simulate lead-time variabilities for each mode in the decision-making process to ensure service levels can be achieved and true inventory levels and investments are considered
- Determine the optimal sourcing assignments for both inbound and outbound locations