Logistics HR & Driver Analytics

Driver performance scoring fails when you rank on km alone while ignoring load factor, customer complaints, or coaching history. Star ratings from apps rarely tie back to payroll or contract renewals. Safety and fuel metrics sit in telematics while productivity sits in TMS, so one driver looks great in one system and risky in another.

FireAI builds a governed scorecard: you choose weights for on-time delivery, incident-free km, fuel efficiency after normalization, POD quality, and customer feedback where available. Driver performance scoring becomes comparable across regions when shift patterns and equipment types are tagged.

How FireAI solves the problem: It resolves driver identity across HR, TMS, and telematics, then applies your policy library so driver performance scoring updates on a cadence you pick and exports to reviews and variable pay prep.

What FireAI tracks:

• Composite driver performance scoring with drill-down to component KPIs
• Trend versus peer cohort on similar lanes and asset types
• Coaching notes and training completion linked to score movement
• Concentration of risk when a small cohort drags network KPIs

HR business partners and transport heads use driver performance scoring to target training, adjust incentives, and support fair disciplinary or recognition cycles.

Driver attendance analytics often stops at clock-in while operations cares about trips started and completed. You see full attendance with half utilization when dispatch is late or demand is thin. Contractor and payroll driver mixes distort simple headcount views.

FireAI overlays roster and attendance timestamps with TMS trip assignment and actual start times. Attendance versus trip assignment analysis highlights same-day gaps: present but no trip, trip without a matching attendance window, or excessive wait between clock-in and first movement.

How FireAI solves the problem: Rules tie each driver day to expected shift pattern by hub, with exceptions routed for supervisor confirmation. Driver attendance analytics and trip facts share one timeline so disputes shrink.

What FireAI tracks:

• Attendance hours versus assigned trip hours by driver and week
• Gap rate between clock-in and first revenue trip by depot
• Share of days with attendance but zero trips for demand versus roster reasons
• Trend after roster or dispatch process changes

Workforce planners and ops controllers use attendance versus trip assignment analysis to fix roster granularity, challenge phantom productivity, and align bench strength with lane demand.

Driver incentive calculation stays brittle when schemes mix per km, per trip, SLA bonuses, and fuel savings in one pay cycle. Version control for policy PDFs does not match what payroll actually ran. Disputes spike after peak weeks when surge rules were unclear.

FireAI stores incentive rules as versioned logic with effective dates, maps trips and outcomes to those rules, and previews driver incentive calculation before payroll submission. Simulation shows what-if when you change a threshold or add a customer-specific kicker.

How FireAI solves the problem: Each pay line traces to trips, KPI results, and rule version so driver incentive calculation is explainable to drivers and auditors. Exceptions queue for HR approval with full context.

What FireAI tracks:

• Earned versus paid incentive by driver and pay run with variance reasons
• SLA bonus attainment versus contract definition by account
• Fuel or productivity uplifts net of safety gates you configure
• Cycle time from period close to locked driver incentive calculation file

Compensation analysts and transport finance use driver incentive calculation automation to shorten close, reduce grievances, and align pay with operational truth.

Overtime tracking logistics fragments when statutory caps, contract caps, and customer SLAs all push toward longer shifts. Night driving rules, weekly rest, and state-wise variations are hard to enforce from trip lists alone. Finance sees overtime cost spikes without knowing if they were avoidable or demand-driven.

FireAI rolls up hours from attendance and trip duration, applies your compliance matrix, and flags drivers approaching limits before assignment accepts another leg. Overtime tracking logistics connects to cost centers and customer accounts when you need recovery or pricing feedback.

How FireAI solves the problem: Alerts trigger at thresholds you set; dispatch integrations can soft-block assignments that would breach rules. Audit trails show who acknowledged exceptions.

What FireAI tracks:

• Regular versus overtime hours by driver, week, and hub
• Compliance risk heatmap for max hours, rest gaps, and consecutive night runs
• Overtime cost versus plan and versus prior year same period
• Drivers with repeat borderline patterns for coaching

HR compliance and transport operations use overtime and compliance hours tracking to reduce fines and fatigue risk while keeping service commitments realistic.