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Blog›Operations

Fleet Analytics: Stop Guessing Why Delivery Costs Are Rising

Connect your TMS or telematics database and ask AnalityQa AI which routes are bleeding fuel, which drivers are underperforming, and which vehicles are sitting idle while others are overloaded.

Try AnalityQa AI AI free →See live examples
Operations warehouse with KPI monitoring

The problem

  • →Fuel costs vary by 20 % across similar routes and nobody has pinned down whether the driver, the vehicle, or the route is responsible.
  • →On-time delivery rates are tracked in aggregate but never broken down by route, driver, or time of day — so there is nowhere to start fixing them.
  • →Vehicle utilisation data is buried in the TMS and only surfaces in monthly fleet reviews when it is too late to act.
  • →Driver performance comparisons are avoided because pulling individual-level data requires a custom report that takes a week to arrive.

Why the usual approach breaks down

TMS, telematics, and fuel card data are siloed

Delivery confirmation is in the TMS, GPS and fuel consumption are in a telematics platform, and fuel card transactions are in a separate finance system. Combining them to get cost per kilometre by driver requires integration work most logistics teams cannot do in-house.

Ops managers are not SQL fluent

Fleet managers understand routes, drivers, and vehicle specs — not database schemas. The gap between knowing what question to ask and being able to query the data means most fleet decisions are made with incomplete information.

Weekly reports miss mid-week anomalies

A vehicle with a fuel leak or a driver taking non-optimal routes will run up costs for days before a weekly report surfaces the problem. Fleet anomalies need to be caught as they happen, not in retrospect.

Utilisation imbalances are invisible until vehicles fail

Some vehicles accumulate mileage far faster than others because planners default to familiar assignments. Without a utilisation heatmap, the imbalance is only noticed when a high-mileage vehicle needs early servicing.

How AnalityQa AI AI solves it

Upload your data — or connect it live — and ask in plain English.

01

Connect your TMS or telematics database directly

AnalityQa AI connects to PostgreSQL or MySQL in read-only mode. If your TMS or telematics platform stores data in a relational database, connect it once and query it in plain English. CSV exports from fleet management platforms also work.

02

Ask questions in plain English

Type 'Which routes have the lowest on-time rate this month?' or 'Show me fuel cost per kilometre by driver for the last 30 days' and get a chart instantly — no SQL, no BI request.

03

Fuel anomaly detection across the fleet

Ask AnalityQa AI to flag any vehicle or driver whose fuel consumption per kilometre is more than 15 % above their 60-day baseline. It will return a ranked list of anomalies with dates and route context so you can investigate immediately.

04

Vehicle utilisation heatmaps to balance the fleet

AnalityQa AI maps active hours and kilometres per vehicle across the week. You can see at a glance which vehicles are being over-used, which are sitting idle, and whether the imbalance is consistent or route-specific.

05

Auto-refresh dashboards with threshold alerts

Set on-time rate and fuel cost dashboards to refresh hourly. Configure alerts so you are notified when a route's on-time rate drops below your SLA threshold — before the customer complaint arrives.

You askedGenerated in 4.2s

"Show on-time delivery rate by route for the past 30 days, ranked worst to best."

Total

12,840+9.2%

Average

324+4.1%

Top segment

38%+2pp

bar-chart

Last 12 mo

table

heatmap

A dashboard built in AnalityQa AI — from question to chart, no SQL.

Real examples

Paste your data. Ask. Ship.

You

Show on-time delivery rate by route for the past 30 days, ranked worst to best.

AI

Analysed 14 active routes. On-time rates range from 61 % to 98 %. Routes 7 and 12 are consistently below your 85 % SLA target. Both share the same afternoon departure window — likely a congestion factor.

bar-chart
You

Flag any vehicles with fuel consumption more than 15 % above their 60-day average.

AI

Identified 3 vehicles with anomalous fuel consumption. Vehicle 14 is 28 % above its baseline over the past 11 days. Vehicle 22 spiked 19 % last week and returned to normal — possible one-off load issue. Details below.

table
You

Give me a utilisation heatmap — active hours per vehicle per day of week.

AI

Mapped utilisation for 31 vehicles over 8 weeks. Vehicles 1–8 average 9.4 active hours on weekdays; vehicles 24–31 average 4.1 hours. Weekend utilisation is concentrated in just 6 vehicles. Heatmap below.

heatmap
You

Compare average fuel cost per kilometre by driver, top 10 highest vs lowest.

AI

Top 10 highest-cost drivers average $0.31/km; lowest 10 average $0.19/km — a 63 % gap. The variance correlates more with route type than individual driving behaviour, but 3 drivers show elevated cost on the same routes as peers.

bar-chart
You

Which vehicles are due for service within 5 000 km based on current mileage accumulation rate?

AI

Based on 30-day mileage accumulation rates, 6 vehicles will hit their next service threshold within 3 weeks. Vehicle 9 is the most urgent — at current pace it reaches the limit in 11 days.

table

What teams get out of it

✓Fuel anomalies surface within hours instead of appearing in a month-end cost variance report.
✓Route-level on-time rates replace aggregate delivery KPIs, giving dispatchers a specific problem to fix rather than a company-wide average to worry about.
✓Utilisation heatmaps let fleet managers balance vehicle assignments before maintenance costs reflect the imbalance.
✓Driver performance reviews are based on data, not impression, and take minutes to prepare instead of a week.

Frequently asked questions

Can AnalityQa AI connect to telematics platforms like Samsara, Verizon Connect, or Geotab?+

If these platforms expose data via a PostgreSQL or MySQL interface, or allow you to export to a connected database, yes. Many customers set up a nightly export from their telematics platform into a PostgreSQL schema and point AnalityQa AI at that.

Is it safe to connect AnalityQa AI to our live TMS database?+

AnalityQa AI uses a read-only credential and never writes to your database. Create a dedicated read-only user scoped to the relevant schemas. Your DBA can verify that only SELECT queries are issued.

Can we combine TMS data with fuel card transactions from a finance system?+

Yes. Connect both systems if they run on compatible databases, or upload fuel card exports as CSV. AnalityQa AI joins them on vehicle ID or date and lets you calculate cost per kilometre across both data sources.

How frequently can fleet dashboards refresh?+

Refresh intervals range from every 15 minutes to daily. For delivery operations with same-day SLAs, most teams use hourly refresh for on-time rate dashboards and daily refresh for cost and utilisation summaries.

Does AnalityQa AI store our fleet data?+

Query results are stored in your encrypted workspace for the retention period you configure. The underlying TMS or telematics database is never copied in full — only the rows matching your query are returned.

We operate across multiple depots. Can we compare them?+

Yes. Connect each depot's data source and ask cross-depot questions directly — 'Compare average fuel cost per km across our three depots this quarter.' AnalityQa AI handles the join and the comparison.

What does AnalityQa AI cost for a fleet operations team?+

Pricing is per workspace and scales with connected sources and users. A 14-day free trial is available with no credit card required. Enterprise plans with dedicated infrastructure and SLA are available for larger fleets.

Related guides

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Warehouse Efficiency: From WMS Data to Actionable Metrics

Operations

Your operations KPI dashboard, built by asking questions

Your data has answers. Start asking.

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