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.

Schedule volatility → fatigue & income unpredictability → disengagement → exit

confidence · high

Business Risks

— The business value at risk if the reality holds.

Business Risk

Revenue Exposure

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.

$42.0M

revenue at risk (4 quarters)

Capacity shortfall × contribution margin on at-risk delivery volume

horizon · 4 quarters

Decisions

— A concrete, costed choice the intelligence supports.

Decision

Workforce Optimization

Stabilize driver schedules and rebalance routes to cut controllable attrition before adding headcount — protecting capacity at a fraction of the hiring cost.

Stabilize schedules (fixed shift patterns + 2-week visibility)
Raise pay across the board
Hire ahead of attrition

Scenarios

— A modeled future — assumptions run forward over reality.

Scenario

Human + AI Logistics

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.

Schedule stability uplift: +35%AI roster adoption: 70% of depotsAttrition elasticity: -1.3

Controllable attrition down ~8.5 pts; capacity restored to plan within 3 quarters.

Outcomes

— The business value created or protected.

Outcome

Revenue Protected

Under the Human + AI Logistics scenario, schedule stabilization protects the majority of at-risk last-mile revenue and avoids the cost of over-hiring.

$28.0M

revenue protected

Schedule stability→Attrition avoided→Capacity restored→Revenue protected

projected · next 4 quarters