Intelligent Planning & Route Optimization

Routing built around your operation.

Nash plans routes shaped to your operation, then keeps re-evaluating them as constraints, capacity, and conditions change through the day. The platform configures around how your network operates and adapts continuously as orders, traffic, and exceptions move.

Route Optimization

Every constraint, planned together.

Vehicle capacity across multiple dimension combined with driver shifts and break rules along with capability gates determine which jobs each vehicle and driver can take.

Delivery Windows

Promises encoded as constraints.

Hard windows act as feasibility constraints. Soft windows are weighted and traded against cost and service-level objectives. Nash builds a feasibility model across them all.

hard
14:00–16:00
soft
12:00–14:00 · weighted
excluded
11:30–12:00
recurring
every Tue + Thu

Continuous Re-optimization

Plan once, replan continuously.

Re-optimization runs on event triggers (new orders, cancellations, traffic shifts, weather alerts, route exceptions), or on demand. Routes can lock at departure, or stay open through the day, with lock policy configured per region or per fleet.

liveplanned 1× / adjusted 64×
staticplanned 1× / adjusted 0×

static · planned · adjusted  /  live · planned · adjusted 64×

Long-range planning

Simulate before you commit.

Territories can be drawn many ways. Scenario Modeling captures alternate options and replays them against the same demand to compare cost, on-time, utilization, and unassigned-stop deltas.

Territory Planning

manual + clustered
stops · 1,847 drive-time bound · 6.5h density · 31 stops/mi²

Simulations

replay against same demand
Scenario Modeling
A · baseline
cost otd
B · tighter windows
cost +4.2% otd +1.8%
C · mixed mix
cost −2.1% otd −0.4%
Fleet Simulators
What-if Analysis
Proactive Operational Planning

Operations control

Constraints change without an engineering ticket.

Planners drag-and-drop stops, swap drivers, and add or remove orders directly on the route, with the cost and service impact shown in real time. Service times, vehicle profiles, capability rules, and cost coefficients are all editable by ops, not engineers.

Change history

14:02 KM max drive time · 8h → 9h
13:47 LM EV toggled on
13:31 KM volume capacity adjusted

Planning Transparency

The optimizer shows its work.

Every route shows the constraint set it satisfied, the trade-offs it made, and what each alternate would have cost. Plan-vs-actual deviations are flagged against historical baselines. Financial impact lands at the local-team level. Every observation feeds back into the next plan.

Route detail 3 candidates considered
Chosen
Route A tightest window stack · lowest penalty
baseline
Alt 1
Route B shorter total drive · misses one soft window
+$11
Alt 2
Route C balanced fleet load · longer service time
+$24

Cost decomposition · chosen

fixed$X distance$Y time$Z penalty$0

Constraints satisfied

window 14:00–16:00 · met by 4 min
capacity 76% used · 24% headroom
capability EV cleared · hazmat n/a
shift 7h 12m of 8h max

P&L impact

this route cost $X margin $Y
vs. baseline +$Z
local team breakdown

Integrated end to end

From planning to dispatch to driver

What the planner configures, the dispatcher dispatches and the driver app shows. Fleet, constraints, and assignments propagate without translation between layers. One platform; one system of record.

Agentic Intelligence Layer
Module · you are here

Intelligent Planning

Routing, windows, simulation, scenario modeling.

Module

Intelligent Orchestration

Dispatch, assignment, exception handling, carrier mix.

Module

Workforce & Fleet Management

Drivers, shifts, pay, own-fleet operations.

Order rate
2,340/sec
Capacity
12,500drivers
Performance
98.1% OTIF

Results, Delivered

Nash delivers consistent performance improvements by coordinating logistics as a single system.

See a demo

>99%

Delivery success rate

20%

Reduction in delivery costs

+15

Net promoter score gain

50%

Reduction in manual work

Bring a week. We’ll route it.

Talk to Nash