Helios Logistics is losing drivers faster than it can hire them — and the cause is fixable.
Follow one thread of intelligence from a certified reality all the way to protected revenue. Every step is a connected object you can open and audit.
Bottom line: $42.0M is exposed; a scheduling decision protects $28.0M of it. Here is the one thread that matters this quarter.
Highlighted for the Executive lens · exposure · decision · outcome
Driver Attrition
Last-mile driver attrition is running at 21.4% and accelerating. Each exit pulls capacity out of the network faster than recruiting can replace it, and the trend line has bent upward for five straight quarters. This is the certified ground truth — not a survey impression.
Open object →Schedule Instability Drives Attrition
The dominant cause is not pay — it is schedule instability. Drivers facing week-to-week shift volatility leave at roughly twice the rate of those with stable rosters. Volatility breeds fatigue and income unpredictability, and that drives the exit. Crucially, it is a controllable lever.
Open object →Week-to-week shift volatility is the dominant, controllable driver of driver attrition — ahead of pay. Drivers with unstable schedules leave at roughly twice the rate of those with stable ones.
Revenue Exposure
If attrition holds its trajectory, the capacity shortfall puts $42.0M of regional last-mile revenue at risk over four quarters — the contribution margin on delivery volume the network can no longer guarantee. The people problem is now a P&L problem.
Open object →If driver attrition holds at the current trajectory, unmet delivery capacity puts a material share of regional last-mile revenue at risk over the next four quarters.
Workforce Optimization
Three options sit on the table: stabilize schedules, raise pay across the board, or hire ahead of attrition. The intelligence favors stabilizing schedules — it attacks the largest controllable cause at a fraction of the cost of blanket pay rises or over-hiring.
Open object →Human + AI Logistics
Model it forward: an AI scheduling agent proposes stable, fatigue-aware rosters while human dispatchers approve and handle exceptions. Run over certified reality with a -1.3 attrition elasticity, controllable attrition falls about 8.5 points and capacity returns to plan within three quarters.
Open object →An AI scheduling agent proposes stable, fatigue-aware rosters; human dispatchers approve and handle exceptions. Modeled forward over certified reality to project the attrition and capacity effect.
Revenue Protected
The result is $28.0M of last-mile revenue protected over the next four quarters, plus the avoided cost of over-hiring — schedule stability converted, step by step, into business value.
Open object →