Before using AI to find suppliers or match items, an organisation often needs something simpler: records that can be distinguished reliably. If the same company appears under three names, a fast answer does not prove a correct match. Procurement data quality is a working discipline to maintain throughout automation, not a task completed once before it begins.
Connect each defect to the decision it affects
Duplicate supplier records split invitation and quotation history. An incorrect category sends invitations to unsuitable companies. A missing unit can lead to an invalid price comparison. Examine these problems in relation to their consequences rather than grouping everything under a general cleanup exercise.
The GS1 Data Quality Framework considers the processes producing data as well as the records themselves. GS1 — Data Quality Framework In practice, assign responsibility for corrections and decide how recurrence will be prevented. Cleaning one spreadsheet does not necessarily change how the next one will be prepared.
Create a small data dictionary
For suppliers, define legal name, internal identifier, category and contact. For items, define description, unit, quantity and material reference where available. State when a field may legitimately be blank. Not every empty cell is an error, but users should understand which omissions prevent processing.
“kg” and “kilogram” may refer to the same unit. “Pack” and “piece” cannot be treated as equivalent without conversion information. Send uncertain matches for review instead of merging similar text automatically. Name similarity is not evidence of commercial identity.
Keep AI suggestions reviewable
Show the source value and proposed system record together. When a user corrects a match, make clear whether the correction applies to this file only or to a general mapping. Otherwise, a single exception may be applied incorrectly to future records.
Start testing with frequently used records, then add incomplete and misspelled examples. Assess the commercial effect of incorrect matches alongside the overall accuracy count. Inviting the wrong supplier has different consequences from a minor wording error in a description.
How this affects Tenflex workflows
Company records, contacts and invitation lists are foundational data in Tenflex. Custom registration fields and registration approval can help collect the necessary information at the beginning. Checking valid units and supplier records also matters when preparing operations through AI Connect.
Begin with the most frequently used suppliers and items. Assign their owners and mark uncertain records for review. Better data helps ordinary RFQs and reporting as well as AI projects, giving the team a useful improvement even before a wider automation rollout is complete.
