Run the Diagnostic. See the Breakpoints. Take Control.

Your Network Is Physically Leaving Truck Capacity on the Table

Estimate how much truck capacity your network is leaving unused and what physical constraints are limiting fill rates — giving you a quick sense of whether your load planning could be more efficient.

Avg trucks per week 120

Typical stops per load

Freight profile

Trailer type

Cube utilization 80%

80%

Weight utilization 85%

85%

Axle limits bind before cube is full?

Which axle is usually limiting?

How often do you consolidate freight?

Results

Confidence: —

Avoidable Trucks

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30-day trucks: — Headroom: —

Unused Capacity

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Unused cube: — Unused weight: —

Axle-Limited Loads

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Likely axle: — Signal: —

This diagnostic provides directional estimates using common utilization assumptions. Exact counts require load-level data.

Why Your Plans Look Good on Monday and Break by Thursday?

A snapshot view that helps you see where your replenishment flow is volatile — identifying patterns like plan churn, carrier rejection, and execution gaps that suggest your network could benefit from smoothing and capacity-aware scheduling.

Avg loads per week 250

Used to translate rates into 30-day counts.

% loads changed inside 72 hours 20%

Includes date/time changes, carrier changes, and reassignments.

20%

% loads changed inside 24 hours 10%

This is where churn becomes expensive (expedites, manual work, service misses).

10%

Tender acceptance (last 30 days)

Typical tender lead time (best guess)

How far ahead are tenders typically sent?

When rejections spike, do late changes spike too?

How often do loads fall out of the plan due to warehouse readiness?

Examples: not picked, not staged, short/late, appointment moves driven by the DC.

On-time ship trend (last 30 days)

Results

Confidence: —

Plan Churn

—

72h: — 24h: —

Plan Fallout

—

Signal: — 30 days: —

Tender Fallout

—

Acceptance: — Lead time: —

DC Execution Gaps

—

On-time ship: — Impact: —

Primary instability driver: —

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This diagnostic provides directional estimates from self-reported signals. For precise diagnosis, use event-level planning + execution timestamps.