When a salesperson quotes "oil filter for a Hilux 2.8 diesel," they're trusting that their catalog has the right reference. If the catalog has incomplete data — no year, no engine, no OEM number — the quote goes out with incorrect information. The customer receives the wrong part. The commercial relationship suffers.
The ACES and PIES standards exist precisely to solve this. They're not just technical acronyms for large distributors — they're the logic any parts store can apply to improve the accuracy of every quote.
The problem: data gaps that cause quoting errors
Each type of incomplete data produces a different type of quoting error:
No specific vehicle fitment data
The salesperson quotes the first reference that appears for "Corolla oil filter" without verifying year or engine. The part that arrives doesn't fit the customer's 2018 1.8L — they have to absorb return shipping and lose a day of work.
No OEM number or incorrect OEM number
Two different brands under the same generic name: "spark plugs for Jetta 2.0." Without the OEM number, the salesperson picks the cheapest option. The customer expected the Bosch equivalent — they receive a generic set and call to complain.
No product specs (dimensions, weight, brand)
A catalog without complete PIES data forces the salesperson to call the supplier to confirm measurements. Each quote becomes two additional phone calls — a process that should take 3 minutes takes 20.
What ACES and PIES are — and why they matter
These two standards were developed by the North American aftermarket industry (SEMA Data Co-op / Auto Care Association) to structure catalog data in a universal format. They're the common vocabulary connecting manufacturers, distributors, and parts stores.
Defines which vehicles are compatible with each part. Structures the part-to-vehicle relationship (Year/Make/Model/Engine) in coded form.
- · Eliminates quotes for incompatible parts
- · Enables search by VIN or vehicle data
- · Foundation of electronic parts catalogs (eCat)
Defines product attributes: part number, description, dimensions, weight, UPC, images, and price.
- · Eliminates supplier lookup calls
- · Enables automatic quotes with complete data
- · Reduces returns due to incorrect specs
What it looks like in practice: good data vs. bad data
The difference between a catalog with quality data and one without:
Name: "Oil filter"
Brand: Toyota
Price: $12.50
Fitment: —
Name: OEM oil filter
OEM: 90915-YZZD3
Brand: Toyota / Denso equiv.
Fits: 2015–2023 Corolla 1.8L, Camry 2.5L
Dimensions: 65mm × 78mm
Weight: 180g
The right-side record generates a correct automatic quote. The left-side record requires the salesperson to call, research, and manually verify — multiplying the time per request.
The real cost of incomplete data
Extra time per quote
Each manual verification adds 5–15 minutes. With 30 quotes per day, that's up to 7.5 hours of extra work — per week.
Returns from wrong parts
35–45% of auto parts returns are due to incorrect fitment — most of which are preventable with proper vehicle data in the catalog.
Loss of customer trust
A workshop that receives two wrong parts switches suppliers. Acquiring a new customer costs 5–7× more than retaining an existing one.
Blocked from e-commerce platforms
Auto parts marketplaces (Amazon, eBay Motors, Autodoc) require structured fitment data to list. Without ACES/PIES-compatible data, you can't access these channels.
How to improve catalog data quality
You don't need formal ACES/PIES implementation to improve. You can apply the same logic to your current catalog:
Add OEM number to every part
The original manufacturer's part number is the universal identifier. With it, any salesperson can verify the correct part without relying on a generic description.
Record fitment as Year/Make/Model/Engine
Minimum four fields. If a part fits multiple vehicles, list each combination separately. Ambiguity costs more than the time it takes to enter the data correctly.
Include brand and quality tier
OEM, OEM-equivalent, premium aftermarket, or economy. This classification gives the customer the information to decide — and eliminates post-sale conflict from "this isn't what I ordered."
Audit your catalog by return rate
The parts with the most returns are the ones with the worst data. Identify the 20 part numbers with the highest return rate and improve their data first — the impact is immediate.
Validate with the supplier before adding to catalog
An unvalidated record is a future return. Require fitment data and specs from the supplier before adding a new part — the initial validation time is less than the cost of correcting a wrong sale.
Salvador: the agent that validates every quote against the catalog
At Suplifai, Salvador is the agent responsible for catalog integrity. Before Victoria sends a quote to the customer, Salvador verifies that the reference matches the requested vehicle, that actual stock exists, and that product data is complete.
This cross-referencing process eliminates incorrect quotes before they reach the customer — not after.
Quotes built on reliable data
Suplifai connects your catalog with automatic validation logic. Every quote that goes out has been verified against vehicle fitment and real stock — without manual intervention.
See how it works →