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Arixent

Logistics & Supply Chain

Representative engagement

Forecasting demand across a logistics network

A governed forecasting system that helps planners position capacity against demand at the level where operating decisions happen.

Container terminal with cranes and stacked containers at sunset

Context

The situation

A logistics operator plans capacity across regions and facilities using spreadsheets and planner judgment. Demand shifts, seasonality and customer changes lead to idle capacity in some places and overload in others.

The domain requirements are covered in Logistics & Supply Chain.

Data Science & Predictive Modeling: Forecasting, churn, risk and optimization models, delivered by embedded data science teams.

Data Engineering & Platform Modernization: Batch and streaming pipelines, lakehouses and migrations off legacy warehouses.

Analytics, BI & Customer Insights: Semantic layers, dashboards and customer analytics people actually use.

Problem definition

How we framed it

The decision was capacity allocation, so the forecast needed to be at the level planners act on, with uncertainty ranges they could plan against. We started by reconstructing the current planning baseline and agreeing how forecast quality would be judged before building models.

Delivery

What we built

  • A data platform joining orders, shipments, customer and external calendar data into a governed model
  • Hierarchical forecasting models at network, region and facility levels with reconciliation between them
  • Uncertainty ranges and scenario views so planners can prepare for peaks rather than single-point estimates
  • Planner tooling that shows forecasts alongside capacity and lets teams override with reasons captured
  • Monitoring of forecast accuracy by segment with automated retraining

Similar problem? We would love to hear it.

Operating impact

What changes for the business

Planning conversations shift from arguing about numbers to deciding on actions, capacity is positioned ahead of demand rather than after it, and the organization has a data foundation for further optimization such as routing and pricing.

Next step

Ready when you are.

Tell us what you are trying to build and we will come back with a point of view, not a pitch.