Product · PartsProcure365
PartsProcure365 optimisation for parts retailers and distributors
You send us your catalogue’s search events, stock levels and sales history. We resolve every VIN vehicle-specifically from original catalogues, compare the car’s real need against what your catalogue showed, and return data ready for your purchasing system. Your own catalogue, your own interface and your own supplier relationships remain yours.
- 4
- input datasets, of which only search events are mandatory
- 3
- result tables, all bound to KType
- CSV / JSON
- straight into your catalogue and purchasing system
- KType
- TecDoc vehicle type
- OE number
- a need your catalogue did not show
- Part group
- brake pads, front
- Parc (Traficom)
- vehicle families in Finland
- Tier
- A · add to range
- Candidates
- aftermarket equivalents from the index
What the service delivers
Three outputs that go straight into the purchasing system
The key principle: every result row is bound to a KType (TecDoc vehicle type). This lets you import the results into your catalogue and purchasing system without separate vehicle interpretation.
Range recommendations per article
KEEP, DEEPEN, REDUCE or DROP with reasons and confidence levels, plus a fit rate: how often a shown article actually fitted the car and how often it was a sibling variant.
Coverage gaps
OE numbers your vehicle population needs but your catalogue did not show, weighted by the Finnish vehicle parc (Traficom) and search volumes. Tier A: add to range, B: consider, C: monitor.
KType variant map
Per KType and part group: which OE variants your search population really needs and in what shares, and which of your own articles cover them.
Regional view
Results by region too, where public data allows it
By default PartsProcure365 weights demand with the national vehicle parc. Where the vehicle authority publishes regional data on its vehicle stock, the same calculation can be run per region: every branch or distribution area gets its own range recommendations and coverage gaps based on the real vehicle parc of that area. In Finland this is possible with Traficom’s open data, which includes the home municipality of registered vehicles.
- Range recommendations and coverage gaps per branch or distribution area
- Weighted by the real vehicle parc of the area, not the national average
- Same data model and result tables, with a region identifier added
- Requires regional vehicle-stock data published by the authority (in Finland, Traficom open data)
- Region
- the branch’s distribution area
- KType
- TecDoc vehicle type
- Parc (region)
- vehicle families in the area
- Tier
- A · add to the area’s range
How it works
From data to a purchase list
You deliver the data
Search events (mandatory), articles and stock levels, sales, and your replenishment supplier’s range. Continuously via API or daily, weekly and monthly as files.
We resolve every VIN
Every vehicle is resolved vehicle-specifically from original catalogues. This tells us what the car really needed, not only what the catalogue showed.
We compare need and shown
Shown articles are mapped to OE numbers and compared with the car’s need. Over-coverage, sibling variants and gaps are measured; demand is weighted by the Finnish vehicle parc.
We return the results
Four tables in CSV or JSON plus a summary of the run’s coverage. All rows bound to KType, ready for your catalogue and purchasing system.
Input data
What you deliver
The service accepts four datasets. Events are mandatory; the others improve the accuracy of the recommendations. Quantity recommendations are produced only with stock and sales data.
| Dataset | Required | Contents | Frequency |
|---|---|---|---|
| events – search events | Mandatory | Every catalogue search: vehicle (VIN or registration number + KType), part group and all shown articles | Continuously (API) or daily/weekly (file) |
| articles – articles and stock | Recommended | Article list: brand, article number, stock on hand, cost, lead time | Weekly |
| sales – sales | Recommended | Units sold and returned per month, 12 months back | Monthly |
| supplier_range – replenishment supplier range | Optional | Which articles your replenishment supplier can deliver (brand + article) | As needed |
The most important integration requirement: record in the event all articles the catalogue showed to the searcher, not only the one picked or ordered. Without this, over-coverage (sibling variants) cannot be measured and fit rates cannot be calculated.
Delivery and results
Two delivery channels, one data model
API push
Your catalogue system sends rows as a JSON array to an HTTPS endpoint, up to 5,000 rows per call, authenticated with an API key. The response is the batch result: accepted rows, per-row errors and unmapped part groups. The same event is never stored twice.
Batch files
Alternatively you deliver files in an agreed way, for example over SFTP. Formats: JSON array or CSV (UTF-8, header row with field names). We ingest the files and deliver the batch result.
Results in CSV and JSON
Every run produces the tables reco_sku, reco_gap, reco_ktype and reco_sku_ktype plus a summary.json: number of events, share resolved VIN-exactly, unmapped part groups and articles per event. CSV opens directly in Excel.
PartsProcure365 or the full platform?
An honest comparison: data only, or the decision at the point of sale
PartsProcure365 gives the buyer a complete procurement picture against their own stock, and DropGear does not become a supplier. The full PartsFinder365 platform adds the value at the point of sale: preventing errors before the order and demand signals a catalogue cannot produce.
| PartsProcure365 | PartsFinder365 platform | |
|---|---|---|
| Purchase signals: gaps, over-coverage, parc weighting and KType map | yes | yes |
| Your own catalogue, interface and supplier relationships remain | yes | Your own white-label platform |
| Prevents the wrong variant at the point of sale using equipment data | Visible afterwards as a sibling variant | yes |
| Availability against your own stock at the moment of search | Reconstructed from stock files at monthly precision | Recorded at the moment of every search |
| OE numbers of the whole assembly and cross-selling signals | no | yes |
| Service-based demand: which parts vehicles need next | no | yes |
| Sales of OE parts when no aftermarket equivalent exists | no | yes |
| Conversion measured natively (cart, order, pair sales) | Only if you deliver picked/ordered fields | yes |
PartsProcure365 is a natural entry point: the purchase signals are of the same quality as on the platform, because every VIN is resolved by the same engine. The platform turns hindsight into a decision at the point of sale.
Play the game and see the difference
Invisible demand is a browser-based simulation: run your own parts wholesale business for six quarters and work out what the demand you never saw costs in your region. No login, about half an hour. In Finnish, Swedish and English.
FAQ
Questions about PartsProcure365
Which data is needed to start?
Why must all shown articles be recorded, not only the picked one?
How are our part groups mapped?
Does DropGear see our supplier prices or our customers?
Can we get the results per region or per branch?
What does the service not do yet?
How do we get started?
Let us show what your search data looks like once resolved
The first run is made with your existing data. You receive range recommendations, coverage gaps and the variant map with your own figures.