Logistics Fleet & Vehicle Analytics

Vehicle utilization analytics fails when you divide loaded km by calendar days while assets sit in yard maintenance or work only night linehaul windows. Leaders see high fleet lists but low revenue per truck day.

FireAI aligns each asset to scheduled trips, available hours, and hold reasons such as breakdown, compliance document gap, or no demand in lane. Vehicle utilization analytics becomes comparable across depots when you normalize for contract type and shift pattern.

How FireAI solves the problem: It joins telematics motion to TMS assignment and revenue recognition rules you configure, so utilization reflects earnable versus lost hours. Drill-down ties low utilization to lane mix, hub dwell, or commercial gaps.

What FireAI tracks:

• Revenue or trip hours per available asset day by region and fleet type
• Yard and non-productive hours with reason tags
• Dedicated versus spot mix effect on rolling utilization
• Week-over-week trajectory after network or roster changes

Fleet controllers and network planning use vehicle utilization analytics to right-size fleet, challenge cross-hire, and prioritize assets for disposal or replacement.

Idle time tracking and dead km stay invisible when drivers leave engines running at tolls or when empty return legs post to a generic cost center. Sustainability and finance teams argue about the same trip file.

FireAI segments GPS idle buckets: customer dock, traffic, regulatory, and unexplained. Idle time tracking links to trip ID and customer so you can price detention or coach drivers. Dead km tracking compares empty reposition to network design so you see systemic backhaul gaps versus one-off exceptions.

How FireAI solves the problem: It applies geofence and ignition rules you approve, then flags idle minutes above threshold with optional photo or note capture. Dead km tracking rolls up by lane pair so planners see recurring imbalance.

What FireAI tracks:

• Idle minutes per 100 trip km by driver cohort and lane
• Dead km ratio and cost per ton where available
• Night versus day idle patterns for roster design
• Trend after coaching or incentive changes

Operations excellence and sustainability leads use idle time tracking and dead km tracking to cut fuel waste, refine pricing, and support green KPI reporting.

Fuel efficiency by driver and route becomes a blame game when payload, terrain, and speed are ignored. Top drivers on paper may run light loads on flat routes.

FireAI normalizes liters per ton-km or per revenue km where weight tickets exist, and applies route difficulty tags from GPS elevation and historical speed bands. Fuel efficiency by driver and route supports fair leaderboards and targeted coaching.

How FireAI solves the problem: It ingests fuel transactions, odometer, and trip manifests so efficiency is tied to comparable work. Fuel efficiency by driver and route highlights anomalies such as theft risk, harsh braking clusters, or wrong fuel grade.

What FireAI tracks:

• Normalized fuel metrics by driver, lane, and asset age band
• Variance from fleet median with confidence flags on small samples
• Idling and overspeed contribution to fuel loss
• Trend after training or incentive pilots

Fleet managers and HSE use fuel efficiency by driver and route to cut cost, support safety programs, and validate sustainability claims.

Maintenance cost analysis fragments when OEM schedules, warranty claims, and third-party invoices use different part codes and labor rates. Finance sees spikes without operational context.

FireAI unifies work orders, odometer at service, downtime hours, and cost lines by asset. Maintenance cost per vehicle supports preventive versus breakdown splits, repeat failure tracking, and make-model comparison.

How FireAI solves the problem: It tags jobs as preventive, corrective, or accident, then rolls cost per km and per available day. Maintenance cost analysis highlights assets that breach replacement thresholds or need vendor negotiation.

What FireAI tracks:

• Cost per vehicle per month and per 1000 km by age band
• Downtime hours linked to lost revenue where trips attach
• Repeat failure codes on same systems
• Warranty recovery rate and aging claims

Asset management and procurement use maintenance cost per vehicle to plan replacement cycles, standardize contracts, and reduce emergency breakdowns.