Logistics Finance Analytics

Most logistics finance teams see revenue and costs in separate systems: billing in the ERP, trips in the TMS, and fuel and tolls in card or vendor statements. Lane-wise P&L only appears after analysts allocate costs to corridors in spreadsheets, often weeks late.

FireAI joins completed trips, rated charges, and actual variable costs to produce lane-wise P&L that updates as trips close. You see contribution by origin-destination pair, hub, and client mix, with drill-down to trips that dragged margin on a lane.

How FireAI solves the problem: It maps every cost line to trips and lanes using your rules (per km, per ton, per stop, or contract-specific), then rolls up revenue from freight invoices and accruals so lane profitability matches what operations actually ran.

What FireAI tracks:

• Contribution margin by lane, week, and vehicle class
• Lane ranking by EBIT contribution and by cost overrun vs plan
• Empty or reposition legs allocated to the lane that triggered them
• Mix shift alerts when high-volume lanes dilute overall margin

This is the core of logistics finance analytics for leaders who need to decide which lanes to grow, reprice, or exit before the quarter ends.

Cost per km is the clearest unit economics signal for road logistics, but it rarely lives in one place. Fuel cards, maintenance, tyre amortization, and driver pay attach to assets and periods, not always to the routes and vehicle types that consumed them.

FireAI builds cost per km analytics by allocating asset and trip-level costs to each completed km, split by vehicle type (rigid, trailer, refrigerated) and by route or corridor. You compare realized cost per km to contract benchmarks, budget, and prior periods without manual fleet spreadsheets.

How FireAI solves the problem: GPS and odometer-corrected distance from trips is matched to cost postings by vehicle, period, and allocation rules you control, so cost per km reflects mixed loading, idling, and terrain differences across routes.

What FireAI tracks:

• Blended and loaded cost per km by vehicle type and route family
• Variance vs budget and vs customer tariff bands
• Driver and maintenance outliers that inflate km cost on specific routes
• Seasonal fuel and toll impact on cost per km with pass-through visibility

Teams use this to tune pricing, swap vehicle class on a corridor, or challenge carriers when cost per km analytics shows sustained drift.

Carrier and subcontractor invoices rarely match internal trip records line for line. Rate tables, detention, multi-drop, fuel clauses, and GST rounding create exceptions that finance clears in email threads while accruals drift.

FireAI runs freight invoice reconciliation by matching each invoice line to trips, PODs, and contract rates. Exceptions sort by value and age: duplicate charges, km or weight mismatches, missing PODs, and off-contract accessorials.

How FireAI solves the problem: It ingests carrier invoices, TMS trips, and rate cards in one workflow so approved payables align with what operations confirms moved, and accruals tie to open disputes.

What FireAI tracks:

• Match rate and exception value by carrier and hub
• Aging of unresolved freight invoice reconciliation items
• Estimated leakage from rate drift vs agreed tariffs
• Audit trail from dispute to settlement for SOX-friendly close

This tightens freight invoice reconciliation so month-end does not depend on heroic spreadsheet work.

Customer contracts set rates, minimums, fuel indexation, and SLA credits. Margin erodes quietly when actual trip mix, detention, or fuel recovery diverges from what sales modeled.

FireAI tracks client contract margin by rolling revenue, pass-throughs, and fully loaded cost for each account’s trips. You see margin vs target, trend by month, and which clauses (volume tiers, dedicated fleet, surge lanes) help or hurt.

How FireAI solves the problem: Contract terms are encoded once and applied to actual trip and billing data, so margin reporting matches what legal agreed, not a static budget spreadsheet.

What FireAI tracks:

• Realized margin vs contract floor by client and business unit
• SLA credit cost as a percent of revenue when OTIF or POD KPIs breach
• Volume tier progress and risk of missing rebate or penalty thresholds
• Spot overflow mixed into contract lanes for margin dilution visibility

Finance and commercial share one client contract margin view for renewals, indexation, and account plans.