iBidLogistics measures the freight it procures. Because the platform runs the plan, the auction, the trip and the invoice, the analytics are built from first-party records rather than a spreadsheet someone maintains after the fact — what you were quoted, what you paid, who bid, who won, who arrived on time.
Filtered by branch and date range, so a plant manager and a group head are reading the same definitions on different slices.
Each step is a real state change in the system, so the drop between any two is a queue you can go and clear — not an estimate.
Normalised by distance and weight, so a change in route mix doesn't read as a change in rate.
The figures on this page are illustrative. They show the shape of each view and how it is calculated — not any customer's results.
The mock-up above shows the shape of it; this is the live Ease of Doing Business view for an active account — real spend, real sourcing mix, real freight lifecycle, computed the same way.
They sit on the tab you're already working in, so the numbers are next to the thing they describe.
Spend and savings over any date range, split by branch and by material, with the same period a year or a quarter earlier alongside it. Savings are measured against the price you expected to pay when the freight was raised, so the number means something specific rather than being a headline.
Bids per freight, how far the price moved between the opening and the winning bid, and how often a lane attracts only one bidder. A lane that never draws a second bid is not a market, and this is where you find it.
Plan-to-freight conversion, what is sitting in backlog, and schedule fidelity — whether loads actually moved on the date they were planned for. Demand against coverage shows where you are planning volume you have no carrier for.
On-time delivery against the promised date, turnaround at each stop, and where in the lifecycle trips are stalling — reported at gate, loading, in transit, waiting on proof of delivery.
A scorecard per carrier covering how often they are invited, how often they bid, how often they win, and how they perform once they have the load. Drill into any one of them for the freight-by-freight ledger behind the score.
Four registers behind the charts — plans, freights, trips and invoices — each exportable to Excel or PDF from the same definition that drew the chart, so finance and operations are never reconciling two different numbers.
Ranking carriers on price alone rewards whoever bids lowest and never turns up. This puts participation, price and performance in one row.
Response rate — how often an invited carrier actually bids. A carrier at 9% is taking up an invitation slot without competing for it.
| Carrier | Won | On time | Near miss |
|---|---|---|---|
| Carrier A | 96 | 94% | ₹340 |
| Carrier B | 71 | 91% | ₹610 |
| Carrier C | 52 | 78% | ₹1,180 |
| Carrier D | 18 | 89% | ₹2,050 |
Near miss is the gap between a carrier's final bid and the winning one. A carrier losing by a few hundred rupees every time is a rate conversation waiting to happen; one losing by thousands is not really competing on that lane.
Analytics are only worth acting on if you can explain where each figure came from.
Every figure traces to a row the platform wrote as the work happened — a bid, an allotment, a gate-in, a POD. Nothing is keyed in twice.
The chart, the register and the export are generated from the same definition, so a total on screen matches the spreadsheet it exports to.
Where the data to compute a metric doesn't exist yet, the view says so rather than showing a confident zero.
Tell us the lanes you run today and we'll walk the dashboards against them.
