Transportation Planning: Who Can Benefit from the Potential of Decision-Making AI?

Facing growing challenges in performance, sustainability, and competitiveness, decision-making AI is a valuable ally for optimizing transportation planning. Regardless of your transportation management structure, optimization solvers offer significant potential to enhance your operational performance.
 The power of optimization solvers, for who?

Owned Fleet / Rented Fleet / Contracted Transport

Owned Fleet

Managing an internal fleet can be complex: balancing capacity with workload curves, planning routes, and assigning drivers to vehicles are all critical decisions. Mathematical optimization solvers help you maximize the use of resources (vehicles/drivers) while balancing workload, reducing kilometers, travel time, and fuel costs — all while respecting business constraints such as driving time regulations and weekly schedules.

In case of last-minute disruptions (breakdowns, driver absence, urgent orders), such tools enable quick and optimal resource reallocation to ensure deadlines are met.

Externalized Fleet

If you outsource your fleet management to one or more transport service providers, decision-making AI remains essential. It enables to reduce overall transportation costs while maintaining control over your environmental impact through improved decision-making on key challenges such as:

  • Shipment consolidation: Leveraging carrier tariff grids (volume thresholds, flexibility on quantities and shipping dates) while ensuring an optimal vehicle use through 3D loading plan optimization.
  • Optimal route planning: Minimizing empty miles and reducing the number of resources required, which is often more cost-effective than quoting individual transport orders from point A to B.
  • Transport tenders: Preparing carrier RFPs with a tool that calculates the optimal transportation plan and minimum cost, enabling you to challenge carrier offers.
  • 3D Loading optimization: Minimizing linear meters or the number of trucks needed.

Less Than Truckload (LTL) or Full Truckload (FTL)

Less Than Truckload (LTL)

For LTL operations, where shipments from multiple clients are consolidated into a single vehicle, optimization allows grouping goods, planning drop-off points, prioritizing clients, and avoiding unnecessary routes — all while respecting time windows and specific delivery constraints.

Full Truckload (FTL)

For FTL operations, the objective of optimization solvers is to maximize vehicle filling rate for each shipment. The tool determines optimal transportation plans while accounting for all your pickup and delivery constraints (delivery time windows, driver skills and preferences, operation duration, driving time…).

Additionally, you gain a comprehensive view of your transportation plan, enabling automation of FTL leg sequences with a multi-day planning, and flow consolidation through optimized delivery schedules and frequencies.

Recurring vs. Dynamic Routing

Recurring Routing

If you serve multiple clients with regular delivery schedules, optimization can model the most profitable routes over the long term. It supports strategic and tactical planning by suggesting adjustments to visit frequencies, delivery time slots, fleet sizing, depot positioning, and zone allocation to agencies.

Dynamic Routing

For dynamic, often unpredictable routing, optimization solvers enable continuous adaptation to new requests and contingencies. They prescribe the best daily routes with visibility into vehicle and driver availability, capacity tracking, and traffic conditions (congestion).



Whether you operate an owned or outsourced fleet, manage LTL or FTL operations, or handle recurring or dynamic routing, decision-making AI tools adapt to your business specificities to significantly reduce transport costs and associated CO2 emissions, enhance operational efficiency, and deliver improved quality of service to your clients..