AI Planning and Optimization Services

Machine learning learns patterns from historical data to predict outcomes, such as next week’s demand. Mathematical optimization searches all feasible decisions to find the best one for a stated objective, such as the cheapest set of routes that meets every delivery window. They work best together: predictions become inputs to the optimizer, which turns them into an actionable plan. Optimization does not need large training datasets, but it does need accurate rules and costs.

Not always. Open-source solvers such as HiGHS and Google OR-Tools handle many routing, scheduling and linear problems very well. Commercial solvers such as Gurobi, CPLEX or Xpress are often significantly faster on large mixed-integer problems and can justify their license cost when solve times or plan quality matter. We benchmark options on your data during prototyping and recommend the most cost-effective choice for your scale.

You need data describing the decisions and their constraints: orders or demand, resources and capacities, costs, time or distance information, and business rules such as labor regulations or service commitments. Much of this exists in ERP, WMS, TMS or HR systems; some lives only in planners’ heads and is captured during workshops. Historical plans are valuable for comparing the optimizer against current practice and demonstrating improvement.

A prototype that replays historical data and compares plans with current practice typically takes six to ten weeks. A production solution with system integration, a planner interface and change management usually needs another three to five months, depending on complexity and the number of sites. We often launch in one region or site first, prove the value and then roll out more widely.

No. In our experience the best results come from planners and optimizers working together. The optimizer handles the combinatorial heavy lifting, while planners provide context it cannot know, handle exceptions and negotiate with stakeholders. We design interfaces that let planners lock decisions, adjust priorities, compare scenarios and understand recommendations. Planners typically shift from building plans manually to improving them and managing disruptions.

We design for re-optimization from the start. The model can be re-solved with the latest information, such as a canceled order, a sick employee or a delayed truck, while keeping changes to the existing plan minimal so operations are not disrupted. Solve times are tuned to your planning rhythm, from overnight batch plans to re-planning every few seconds for real-time dispatch.