Monitor and control manufacturing processes with real-time Statistical Process Control (SPC). Create X-bar R charts, track Cp/Cpk process capability, detect variations before they cause defects, and ensure consistent product quality. iFactory's SQC module uses AI-powered analysis to predict quality issues 2-8 weeks before they occur — reducing defects by 37% and cutting scrap by 45%.
From data collection to continuous improvement, iFactory's SQC module implements the complete Shewhart cycle. Collect measurements, analyze with control charts, detect variations, and drive process improvements — all in real-time.
Collect measurements from gauges, sensors, IoT devices, and manual entry. Standardized data structures ensure traceability and audit compliance.
Auto-generate control charts with UCL/LCL at ±3σ. Detect common cause vs. special cause variation with visual alerts and run rules.
Calculate capability indices to measure how well your process meets specifications. Target Cpk ≥ 1.33 for Six Sigma quality levels.
Use SPC data to identify root causes, implement corrective actions, and verify improvements. Drive toward zero defects with data-driven decisions.
Control charts are the foundation of Statistical Process Control. iFactory automatically generates X̄-R charts for variables, p-charts for defect rates, c-charts for defect counts, and more. Real-time visualization helps you distinguish between common cause variation (normal) and special cause variation (requires action) — before defects occur.
X̄-R, X̄-S, Individual-MR, Median-Range for measurement data.
p, np, c, u, DPMO charts for defect and defective tracking.
UCL/LCL calculated at ±3σ with 1σ and 2σ warning zones.
WECO, Nelson, Juran rules detect patterns before out-of-control.
Variable Control Charts
Attribute Control Charts
Run Rules Available
Process capability indices (Cp, Cpk, Pp, Ppk) quantify your ability to produce within specification limits. A Cpk of 1.33 means 99.99% of products are within spec — the Six Sigma standard. iFactory calculates capability in real-time, showing whether your process is capable, centered, and stable enough for customer requirements.
Compares specification width to process spread (6σ).
Adjusts Cp for process centering — the key metric.
Long-term capability considering all variation sources.
Defects per million opportunities for sigma level.
Capability Interpretation
Move from reactive to predictive quality control. iFactory's AI analyzes SPC data patterns, equipment performance, and environmental conditions to predict failures before they occur. Machine learning detects subtle shifts that human analysis misses — enabling proactive maintenance and preventing defects before a single bad part is produced.
AI detects subtle patterns invisible to standard SPC charts.
Correlate multiple parameters to find hidden relationships.
Get alerts 2-8 weeks before issues impact production.
ML identifies which variables drive quality deviations.
Active Predictions
Eliminate manual data entry errors with automated collection from CNCs, PLCs, CMMs, digital gauges, and IoT sensors. iFactory connects to your equipment via OPC-UA, MQTT, Modbus, and direct database integration — streaming measurements directly into SPC charts in real-time. Mobile apps enable shop floor entry when automation isn't possible.
Connect to CNCs, PLCs, CMMs via OPC-UA, MQTT, Modbus.
Digital calipers, micrometers, height gauges auto-capture.
iOS/Android apps for manual entry with barcode scanning.
Bi-directional sync with SAP, Oracle, and other ERP systems.
Connected Equipment
Industry-specific control charts, capability targets, and compliance reporting — pre-configured for automotive, aerospace, medical devices, electronics, and more.
IATF 16949, PPAP, Cpk ≥1.67 requirements
AS9100, NADCAP, first article inspection
FDA 21 CFR Part 820, ISO 13485, CAPA
IPC standards, solder quality, AOI integration
FDA cGMP, 21 CFR Part 11, batch records
Tight tolerances, tool wear monitoring, CMM
FSMA, HACCP, weight/fill control, GFSI
Injection molding, extrusion, dimension control
"Within 6 months of implementing iFactory SPC, we reduced defect rates by 37%. The real-time control charts caught a tool wear issue that would have produced 2,000 bad parts. ROI was immediate."
"The variance reduction of 39.5% speaks for itself. DataNet integrated easily into our processes and helped improve product quality and reliability. Customer complaints dropped 45%."
"The AI predictions are game-changing. We caught a spindle bearing issue 3 weeks before failure. That single alert saved us $180,000 in scrap and 2 days of downtime."
Statistical Quality Control (SQC), also called Statistical Process Control (SPC), is a method of using statistical techniques to monitor and control manufacturing processes. It helps you distinguish between normal variation (common cause) and abnormal variation (special cause) that requires corrective action. By collecting and analyzing measurement data on control charts, you can detect process drift before it produces defective products — enabling prevention rather than detection.
iFactory supports all major control chart types: Variable charts (X̄-R, X̄-S, Individual-MR, Median-Range, EWMA, CUSUM) for measurement data, and Attribute charts (p, np, c, u, DPMO) for defect/defective data. Control limits are automatically calculated at ±3σ, with optional 1σ and 2σ warning zones. Multiple run rule sets (WECO, Nelson, Juran, Duncan, Westgard) can detect patterns before out-of-control conditions.
Cpk (Process Capability Index) measures how well your process produces within specification limits, adjusted for centering. A Cpk of 1.00 means 99.73% of products are within spec (3σ). A Cpk of 1.33 means 99.99% within spec (4σ) — the minimum for most Six Sigma programs. A Cpk of 1.67 means 5σ capability, required by many automotive suppliers (IATF 16949). iFactory calculates Cpk in real-time and alerts when capability falls below targets.
Traditional SPC is reactive — it detects problems after they occur. AI-powered SPC is predictive — it detects patterns that precede problems. Machine learning analyzes historical SPC data alongside equipment parameters, material properties, and environmental conditions to predict failures 2-8 weeks before they happen. This enables proactive maintenance and process adjustments, preventing defects rather than just detecting them. AI also performs automated pattern recognition and multivariate analysis that humans would miss.
iFactory connects to virtually any equipment: CNCs and machining centers via OPC-UA, PLCs via Modbus/TCP, CMMs via direct API (Zeiss, Hexagon, Mitutoyo), digital gauges via Bluetooth or USB, and IoT sensors via MQTT. We also integrate with MES and ERP systems (SAP, Oracle) for work order and part traceability. For manual inspection, iOS and Android apps support barcode scanning and guided data entry.
Yes, iFactory's SQC module is designed for ISO 9001, IATF 16949 (automotive), AS9100 (aerospace), FDA 21 CFR Part 820 (medical devices), and other quality management standards. Features include complete audit trails, electronic signatures, controlled document management, CAPA integration, and automated compliance reporting. Control charts and capability studies provide the documented evidence auditors require.
Documented results from SPC implementations show: 37% average defect rate reduction within 6 months, 22% increase in throughput, 45% reduction in customer complaints, and $1.2M+ annual cost savings. ROI comes from reduced scrap, less rework, fewer warranty claims, lower inspection costs, and prevented downtime. Most manufacturers see positive ROI within 3-6 months of implementation.
Absolutely. iFactory is designed for shop floor use, not just quality engineers. Simplified operator interfaces show clear pass/fail indicators and color-coded control charts. Real-time alerts notify operators immediately when action is needed. Mobile apps work on tablets at workstations. The goal is making statistical methods accessible to everyone — from machine operators to Six Sigma Black Belts — without requiring statistics expertise.
From control charts to AI predictions, iFactory's Statistical Quality Control module gives you complete visibility into process variation. Detect problems before they cause defects, improve capability, and drive continuous improvement with data-driven decisions.