Supplier Onboarding Checklist for Distributors

A new supplier sends a spreadsheet, you load it, and three weeks later the same SKUs are still stuck in review. The columns do not match your schema, half the barcodes fail validation, two products already exist in your catalog under different part numbers, and nobody can say which attribute values are trustworthy. This pattern is not a supplier problem — it is a process problem. Claro is built to run the matching, enrichment, validation, and write-back steps that currently happen manually: it resolves incoming SKUs against your live catalog, fills attribute gaps with provenance-tracked enrichment, and writes clean records back into your PIM or ERP so every new supplier range lands correctly from day one.

This checklist is a concrete, repeatable sequence of intake, validation, matching, and enrichment gates that gets a supplier range live the first time — whether you sell MRO consumables, CPG, furniture, or industrial components. The goal is not a prettier intake form. It is a defined set of quality gates each batch must pass before it touches your live catalog, so that reliability is a property of the process rather than the heroics of whoever loaded the file.

The problem: what manual onboarding actually looks like

Before the checklist, here is the reality most distributor teams recognize:

Phase 1: define the contract before the file arrives

Most onboarding failures are decided before a single row is loaded, because nobody agreed on what a compliant file looks like. Settle these with the supplier before anything is sent:

• Required identifiers: GTIN/UPC, manufacturer part number (MPN), and the supplier’s own SKU. Decide which is authoritative for matching and document it in writing.
• Mandatory attributes per category, including units of measure, pack/case quantities, and dimensions. A furniture distributor that pins UOM and pack quantity here avoids loading “case of 6” as a single sellable unit.
• File format and encoding (UTF-8 CSV, BMEcat, or a structured feed), plus a column-to-schema mapping both parties sign off on.
• Asset expectations: image count, minimum resolution, and a naming convention that ties each asset to an identifier so assets can be matched programmatically.
• An update cadence and a named point of contact for corrections, so errors have a clear path to resolution rather than a shared inbox nobody owns.

If you are formalizing supplier expectations into a number you can track over time, a supplier data scorecard turns these contract points into a measurable vendor quality score.

Phase 2: intake — normalize, map, validate

Once the file lands, run it through the same three gates every time before it reaches the matching step. Skipping any one of them means errors compound downstream.

1. 1
   Normalize the file
   Fix encoding, delimiters, and stray quoting so the file parses cleanly. A mis-detected delimiter silently shifts every column one position and corrupts the whole batch. Use a CSV fixer on every intake, not just the ones that look broken.
2. 2
   Map supplier columns to your canonical schema
   Align incoming column names to your internal field names. Record the mapping in a supplier profile so the next drop from the same vendor is automatic, not a fresh guessing game. The playbook on mapping supplier attributes to your schema covers unit conversions and multi-value fields specifically.
3. 3
   Validate identifiers and required fields
   Check that GTINs carry valid check digits, MPNs are present, and all mandatory attributes for the category are populated. Use a GTIN validator to catch transposed digits and dropped leading zeros before they reach the match step.

Phase 3: match against your existing catalog

This is the step manual onboarding skips, and it is the primary reason catalogs fill with duplicates. Before creating any new records, ask whether each incoming product already exists — possibly under a different supplier part number, a slightly different description, or without a GTIN at all.

An industrial distributor onboarding a second bearing supplier will find heavy overlap with existing stock. Treating every incoming row as a new product doubles the catalog and breaks pricing, analytics, and procurement reporting. Set explicit confidence thresholds so clear matches auto-link, clear non-matches create records, and only the ambiguous middle reaches a human reviewer.

The playbook on matching supplier catalogs to your inventory covers how to tune confidence bands for your category mix. If you are weighing whether to build this matching logic in-house or use a purpose-built platform, scripts vs a matching platform lays out the practical tradeoffs.

Phase 4: enrich, verify, and write back with provenance

New supplier records typically arrive thin: a title, a price, maybe a single low-resolution image. Filling attribute gaps from manufacturer datasheets and trusted secondary sources is what makes a category sellable. The discipline that separates safe enrichment from risky enrichment is provenance — recording where each value came from so a reviewer can verify it and a buyer can trust it.

Keep AI-generated attributes gated behind a verification step rather than published blind. Once verified, Claro writes the enriched, validated record back into your existing PIM or ERP — no manual re-entry, no copy-paste from a staging spreadsheet. The guide on enrichment without hallucination covers the pattern in detail, and why supplier onboarding takes weeks explains the hidden bottlenecks this phase eliminates.

The complete gate sequence

1. 1
   Pre-load contract
   Identifiers, mandatory attributes, file format, asset standards, and update cadence agreed with supplier before first file.
2. 2
   File normalization
   Encoding, delimiters, and quoting fixed; file parses without errors.
3. 3
   Schema mapping
   Supplier columns mapped to canonical fields; mapping saved for reuse.
4. 4
   Identifier and field validation
   GTINs validated for check-digit correctness; MPNs present; required attributes populated per category.
5. 5
   Catalog match
   Each incoming record matched against existing catalog; confidence-banded routing to auto-link, auto-create, or human review.
6. 6
   Enrichment with provenance
   Attribute gaps filled from manufacturer sources; source attached per value; AI-assisted values gated behind verification.
7. 7
   Write-back to PIM/ERP
   Clean, validated, enriched records written directly into your live system; no manual re-entry.

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