Predict quality failures before they reach your customer
FyndEm Quality analyzes process parameters, sensor data, and supplier quality records to detect deviations, predict equipment failures, and identify process drift — from data your MES and ERP already collect.
Most manufacturing quality management catches defects after they happen. By the time SPC charts flag an excursion, defective product has already moved downstream.
⚠️ The Problem
Manufacturing quality teams rely on statistical process control, inspection sampling, and post-production testing — tools designed to detect problems after they occur. Process drift, equipment degradation, and incoming material variation create defects that traditional SPC charts catch too late. The result: scrap, rework, warranty claims, regulatory exposure, and customer trust erosion.
✓ The FyndEm Approach
FyndEm Quality applies behavioral pattern analysis to your existing process data — MES logs, sensor readings, ERP quality records, and supplier data — to predict quality deviations before they manifest as defects. The model identifies the subtle, multi-variable interactions that precede quality failures and alerts your team in time to intervene. No new sensors. No MES replacement. Insights from data you already collect.
Four predictive capabilities that transform quality management from reactive to anticipatory.
Quality Deviation Prediction
Detect process drift and parameter combinations that precede quality failures — hours or shifts before defects would be caught by traditional SPC or end-of-line inspection.
Equipment Failure Early Warning
Analyze sensor data, maintenance records, and production patterns to predict equipment degradation and failure — enabling predictive maintenance that prevents unplanned downtime and quality excursions.
Supplier Quality Scoring
Score incoming materials and supplier lots by predicted quality risk — allowing your receiving team to prioritize inspection and your quality team to address supplier issues before they impact production.
Root Cause Pattern Analysis
Identify multi-variable interaction patterns across production lines, shifts, and material lots that correlate with quality outcomes — surfacing root causes that single-variable analysis cannot detect.
Designed For
Manufacturing quality teams, plant operations leaders, continuous improvement groups, and supply chain quality managers in discrete manufacturing, process manufacturing, pharmaceutical production, food and beverage, automotive, and aerospace environments. Integrates with SAP, Oracle MES, OSIsoft PI, Wonderware, SCADA systems, and standard CSV/SFTP data exports.
FyndEm Quality is deployed wherever quality failures carry high cost — in materials, time, compliance, or customer trust.
🏭 Discrete Manufacturing
Predict assembly defects by analyzing upstream process parameters, tooling wear patterns, and material lot characteristics — enabling intervention before defective units reach final assembly.
⚗️ Process Manufacturing
Detect batch quality deviations early by monitoring multi-variable process parameter interactions — catching the subtle drift that single-parameter SPC charts miss.
💊 Pharmaceutical & GMP
Identify OOS (out-of-specification) risk before batch release testing — reducing the cost and regulatory exposure of failed batches and supporting proactive deviation management.
🚗 Automotive & Aerospace
Predict warranty-relevant quality issues at the point of production — before vehicles or components leave the plant — using the same process data already being collected for traceability.
🍽️ Food & Beverage
Monitor process conditions and ingredient lot characteristics to predict food safety and quality deviations — supporting HACCP compliance and reducing product holds.
📦 Supplier Quality Management
Score and rank suppliers by predicted quality performance — not just historical reject rates — enabling proactive supplier development and incoming material risk management.
No new sensors. No MES replacement. FyndEm Quality works from standard manufacturing data exports.
Process Parameters
MES logs, sensor readings, machine settings — temperature, pressure, speed, cycle time
Quality Records
Inspection results, SPC data, non-conformance reports, CAPA records
Maintenance Data
Work orders, PM schedules, equipment runtime hours, downtime logs
Supplier & Material Data
Incoming inspection results, COAs, lot traceability, supplier scorecards
Powered by CentroidAI’s behavioral intelligence engine
The same patent-pending AI architecture that predicts surgeon adoption, patient risk, and donor churn — applied to manufacturing quality.
See it applied to your production data
Every engagement begins with a retrospective diagnostic — a run on your own historical quality and process data that shows what FyndEm Quality would have predicted before it happened. No commitment required.