Your quality records already reveal where defects come from.
Odoo Quality captures inspection results, non-conformances, scrap reasons, and lot traceability across every production order. We build AI models on top of that data to surface defect patterns, predict quality risk, and trigger alerts before scrap accumulates — no cameras, no hardware.
Quality Data That Sits in Odoo but Never Gets Analyzed
Service Scope
ML models trained on Odoo Quality inspection history to identify recurring defect patterns by product, work center, operator, supplier, and material lot. Find the root cause before the next batch starts.
Predict first-pass yield for in-progress production orders based on upstream quality signals — incoming material lots, work center performance history, and process parameter patterns in Odoo.
Analyze scrap reason codes, non-conformance records, and lot traceability in Odoo to identify the highest-cost defect sources and their upstream drivers across materials, routing steps, and operators.
Automated alerts when production conditions match historical patterns that predict quality failures. Triggered in Odoo as quality alerts before the lot completes, not after scrap is counted.
Connect incoming inspection results in Odoo to downstream defect patterns. Identify which suppliers and material lots drive the highest scrap rates — before the next PO is placed.
Automated quality performance reports generated from Odoo data — first-pass yield, defect Pareto, cost of quality, and trend analysis by product line, work center, and time period.
Engagement Process
Review your Odoo Quality module configuration, inspection point coverage, non-conformance records, and scrap reason taxonomy. Establish data quality baseline before modeling.
Build models on Odoo quality history to identify recurring patterns, high-risk conditions, and upstream drivers. Validate against known defect events before deploying alerts.
Connect lot traceability, BOM components, and supplier receipts in Odoo to quality outcomes. Build the data model that links defects back to their source.
Configure quality alerts, escalation routing, and automated work order holds in Odoo based on AI risk signals. Tune thresholds to balance sensitivity against false positives.
Deploy quality dashboards and automated reports. Track model accuracy against actual defect outcomes and refine as your Odoo quality data grows.
Frequently Asked Questions
Every engagement starts with an assessment.
Not a proposal. Not a sales call. We tell you what we find, not what you want to hear. The Launchpad assessment maps your operation before any software work begins.