How do I decide which salvage cars to buy at auction?

Buy the loss types that yield your short parts, not the cheapest lots. The dashboard scores supply-versus-demand coverage for every part, flags which front-end parts stay chronically short, and produces a ranked auction buying list naming the specific loss types and models to target next. For example, source end-of-life Fiat Uno and Palio to close a 47-unit gap.

Does it connect to our ERP or database, or only work from spreadsheets?

In this build the three inputs (vehicles bought, parts recovered, customer orders) are CSV datasets. The architecture supports live database or ERP connections through the Workflows layer without changing any of the six views, so demand and intake data flow in automatically once connected.

How does it calculate whether a part is short or overstocked?

It divides listed supply units by completed-order demand units to get a coverage score for each part. Below 85% is a shortage; above 125% is surplus that ties up cash. A part at 60% coverage means only six in ten orders for it can currently be filled, and the rest wait for stock.

How does it know which cars to buy for a specific short part?

For each short part, the app traces every listed unit back to the vehicle it came from, reads that vehicle's loss type, and counts which loss types yield the part most often intact. The top loss types and models become the auction buying instruction, sized to the exact unit gap for that part.

Is this available to use now?

This parts-sourcing dashboard is a demonstration build shown as a walkthrough, marked coming soon rather than generally available. It runs across six views (Overview, Intake, Demand, Revenue, Mismatch and Recommendation) on CSV datasets, and is built to connect to live systems through the Workflows layer when deployed.

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Source auction cars from real parts demand, not gut feel

Parts sourcing

Source auction cars from real parts demand, not gut feel

Name: Source auction cars from real parts demand, not gut feel
Uploaded: 2026-06-29
Duration: 15 s
Description: Your best-selling parts are always short. Other stock never moves. The reason is what you buy at auction, not how much you spend. This sourcing dashboard scores supply-versus-demand coverage for every part, names the front-end parts that stay chronically short, and hands the buyer a ranked auction list: which loss types and models to target next to close the gap without spending more.

Your best-selling parts are always short. Other stock never moves. The reason is what you buy at auction, not how much you spend. This sourcing dashboard scores supply-versus-demand coverage for every part, names the front-end parts that stay chronically short, and hands the buyer a ranked auction list: which loss types and models to target next to close the gap without spending more.

Use case: Connect parts-demand data to auction buying so a salvage operation stops over-buying front-collision cars and starts sourcing the loss types that actually yield its short parts.

Published: 2026-06-29

Source auction cars from real parts demand, not gut feel. Your best-selling parts are always short. Other stock never moves. The reason is what you buy at auction, not how much you spend. This sourcing dashboard scores supply-versus-demand coverage for every part, names the front-end parts that stay chronically short, and hands the buyer a ranked auction list: which loss types and models to target next to close the gap without spending more.

What this is

A salvage operation buys written-off cars at auction, strips them for parts, and sells the parts on. Here is the blind spot: front-collision cars dominate the intake, and front-collision damage destroys exactly the parts customers order most (bumpers, hoods, headlamps, grilles, windshields, fenders). So the highest-demand parts stay short while rear and interior parts pile up unsold. This app reads three datasets (vehicles bought, parts recovered, customer orders) and works the problem across six views: the closed-loop overview, what is bought (Intake), what the market orders (Demand), realised versus projected P&L (Revenue), the supply/demand gap per part (Mismatch), and a concrete auction buying list (Recommendation) that names the loss types and models to target.

How it works

Step 1. Overview: see the loop that is missing. The dashboard opens on the closed-loop cycle (buy at auction, dismantle, recover, sell, market demand) with a dashed feedback arc marked "missing today". Five KPIs summarise the operation, and a red "The Gap" callout names the live share of front-collision vehicles in the intake mix.
Step 2. Intake: what you are buying. The vehicle mix at auction: total spend, average price per lot, top models, and the front-collision share in red. A loss-type chart and a sortable table of recent purchases show how much of the intake destroys front-end parts.
Step 3. Demand: what the market orders. Completed orders only. The most-ordered parts ranked, with front-end body parts flagged in red, plus revenue per part and which vehicle models drive the most demand.
Step 4. Revenue: realised vs projected. Six KPIs split between current and projected: realised revenue, total investment, current P&L, and the projected position at full sell-through. A P&L bridge bar shows whether selling the listed inventory clears the break-even line.
Step 5. Mismatch: the supply/demand gap. Coverage for every part: listed supply divided by completed demand. A diverging chart sorts parts worst to best, shortage below 85% in red, surplus above 125% in gray. The key finding names the structural cause.
Step 6. Recommendation: the auction buying list. One card per short part: the gap in units, the best donor loss types and models that yield that part intact, and a generated "auction buy priority" line. For example, source end-of-life Fiat Uno and Palio to close a 47-unit gap.

What you see in the video

The walkthrough moves through all six views: the closed-loop overview and the live front-collision gap, the auction intake mix, the parts the market actually orders, realised-versus-projected P&L against break-even, the per-part coverage chart that separates shortage from surplus, and the recommendation cards that translate the mismatch into a specific auction buying list, naming which loss types and models to target to close each shortage without spending more.

When to use this

You buy written-off vehicles at auction and resell the recovered parts.
Your highest-demand parts are always short while other stock sits unsold.
Auction buying decisions are made without demand data to back them.

Frequently asked questions

How do I decide which salvage cars to buy at auction?: Buy the loss types that yield your short parts, not the cheapest lots. The dashboard scores supply-versus-demand coverage for every part, flags which front-end parts stay chronically short, and produces a ranked auction buying list naming the specific loss types and models to target next. For example, source end-of-life Fiat Uno and Palio to close a 47-unit gap.
Why are my highest-demand parts always out of stock while other stock sits unsold?: Your auction intake is misaligned with demand. Front-collision cars dominate the buying, and front-collision damage destroys exactly the parts customers order most: bumpers, hoods, headlamps, grilles, windshields, fenders. Rear, side and interior parts then pile up unsold. The fix is buying different loss types, not spending more.
Does it connect to our ERP or database, or only work from spreadsheets?: In this build the three inputs (vehicles bought, parts recovered, customer orders) are CSV datasets. The architecture supports live database or ERP connections through the Workflows layer without changing any of the six views, so demand and intake data flow in automatically once connected.
How does it calculate whether a part is short or overstocked?: It divides listed supply units by completed-order demand units to get a coverage score for each part. Below 85% is a shortage; above 125% is surplus that ties up cash. A part at 60% coverage means only six in ten orders for it can currently be filled, and the rest wait for stock.
How does it know which cars to buy for a specific short part?: For each short part, the app traces every listed unit back to the vehicle it came from, reads that vehicle's loss type, and counts which loss types yield the part most often intact. The top loss types and models become the auction buying instruction, sized to the exact unit gap for that part.
Is this available to use now?: This parts-sourcing dashboard is a demonstration build shown as a walkthrough, marked coming soon rather than generally available. It runs across six views (Overview, Intake, Demand, Revenue, Mismatch and Recommendation) on CSV datasets, and is built to connect to live systems through the Workflows layer when deployed.

Tags: Auction sourcing, Parts demand, Coverage analysis, Circular economy, Salvage, Remanufacturing

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