# From weekly scheduling to real time: a 53.51% cost reduction

> A model with 30 million variables and 2,000 points analyzed at once, with the gain growing each time decisions were made more frequently.

Sector: Logistics and transportation. Client: Large-scale distribution operation (anonymous, per Orion policy).

## Results

- **19.32%** — cost reduction with weekly planning
- **37.19%** — cost reduction with daily planning
- **53.51%** — reduction with the model running in real time

## The challenge

A distribution network with 2,000 points cannot be planned one leg at a time: origin, destination, service window and capacity all constrain one another, and the total cost only becomes visible when the network is evaluated as a whole.

In practice, planning happened less often than the operation required. Between one cycle and the next, reality changed, with orders, availability and constraints in motion, and the plan lost its fit with operations precisely where cost is decided.

## The approach

Orion built a mathematical model with about 30 million variables, capable of analyzing 2,000 points simultaneously instead of optimizing isolated legs.

The gain was measured in stages, and the progression is the central finding of the case: a 19.32% cost reduction with weekly planning, 37.19% with daily planning and 53.51% with the mathematical model running in real time. The closer to the operation the decision is made, the more value is captured.

## Project objectives

- Reduce total logistics cost by evaluating the entire network, not leg by leg.
- Increase planning frequency from weekly to daily and then to continuous.
- Keep the model aligned with the real operation over time.

## About the company

- ~30 million variables in the model
- 2,000 points analyzed simultaneously
- Gain measured at 3 frequency stages

## Solution applied

[Logistics and Fleet Optimization](https://orionotimizacao.com/en/solutions/logistics-and-fleet-optimization) — Routing, fleet sizing and employee transport: operations optimized for total cost.

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