Features-SPC Reports

Full-Factor Quality Health Audit. Integrating Control Charts, CPK, ANOVA, and AI Insights to end the era of inefficient manual reporting.

With all inspection data stored in the SPC system, why rely on manual Excel reporting? NEXSPC redefines quality reporting standards. With one click, the system processes massive historical data to generate a professional report featuring 18 core statistical elements. From fundamental Xbar-R charts to advanced ANOVA and LLM-powered intelligent insights, this report doesn't just show 'what happened'—it explains 'why' and predicts future trends. Supports multi-dimensional slicing and Excel downloads, making reporting effortless and professional.

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1Panoramic Visualization Charts

Individual Charts: Automatically generate Individuals (I) and Moving Range (MR) charts.
Subgroup Charts: For mass production, providing full Xbar-R, Xbar-S, and Xbar-MR charts to precisely capture within-subgroup and between-subgroup variations.
Advanced Distribution Visualization:
Capability Analysis Histogram: Fully aligned with Minitab, including various standard deviations, PPK, CPK, PPM, etc.
Subgroup Distribution Plot: Clearly displays data dispersion at each sampling point, ensuring no outliers are missed.

2Advanced Statistics & Process Capability

Full-Dimensional Capability Assessment: One-click calculation of Pp/Ppk (long-term), Cp/Cpk (short-term), Ca (accuracy), and PPM. Includes Capability Comparison Charts to visualize specification limits (USL/LSL) against actual distribution.
Rigorous Statistical Testing:
Normality Test: Features 4 mainstream methods (including Anderson-Darling) to verify normal distribution, ensuring valid analysis prerequisites.
Distribution Fitting: Intelligently identifies the best distribution type (e.g., Weibull, Lognormal).
ANOVA: Automatically determines significant differences in subgroup means to help identify inter-batch fluctuations.
Statistical Summary: Summarizes Mean, Median, Skewness, Kurtosis, and other metrics for instant data profiling.

3AI-Powered Diagnostics & Out-of-Control Detection

Machine Learning Detection (ML Detection): Introduces advanced ML algorithms to supplement traditional rules, identifying complex non-linear anomaly patterns and reducing false alarm rates.
LLM Interpretation: An industry-first feature. The built-in AI large model automatically analyzes statistical data and generates a natural language narrative summary. Example: 'This week's CPK is 1.3, an improvement over last week; however, a significant mean shift occurred on Wednesday. We recommend investigating...' This enables non-experts to grasp quality status instantly.
Anomaly Root Cause Summary: The system automatically categorizes and summarizes all outliers by chart type and detection rules, listing operator-entered causes and corrective actions to form a closed-loop diagnosis.

4Flexible Interaction & Data Traceability

Multi-dimensional Data Slicing: Reports are dynamic. Filter by shift, machine, mold ID, supplier, and more to instantly generate dimension-specific reports.
Data Detail & Traceability: Reports include complete data tables. Any anomaly-triggering data is highlighted in red, with direct labeling of the violated detection rule and associated cause records.

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