Statistical Process Control System

A comprehensive web-based statistical process control (SPC) application that provides real-time monitoring, anomaly detection, and advanced statistical analysis for process optimization. This project demonstrates expertise in statistical computing, real-time data processing, and sophisticated database design for time-series applications.

Project Overview

Statistical Process Control is critical for maintaining quality in manufacturing and operational processes. My implementation provides a complete SPC solution with real-time data ingestion, multiple chart types, statistical testing, and interactive visualization. The system emphasizes statistical accuracy, performance optimization, and user-friendly interfaces for complex analytical workflows.

Key Features

Real-time Statistical Monitoring

The system continuously collects and analyzes data points through multiple channels including CLI interfaces and HTTP APIs. It implements proper SPC methodology with dynamic control limit calculation, setup period management, and automatic anomaly detection using established statistical control theory.

Advanced Data Transformations

To handle non-normal data distributions common in real-world processes, I implemented multiple transformation algorithms including logarithmic, Anscombe, and Freeman-Tukey transformations. These ensure accurate control limit calculations and statistical test validity across diverse data types.

Interactive Visualization System

Built with Chart.js, the frontend provides responsive, interactive control charts with zoom, pan, and annotation capabilities. Users can navigate historical data using date range selection, view statistical test results, and add contextual annotations to data points for process documentation.

Statistical Test Integration

The system incorporates rigorous statistical testing including Kolmogorov-Smirnov normality tests and runs tests for randomness detection. These tests use the @stdlib/stats library for mathematical accuracy and provide p-values and test statistics to guide process analysis decisions.

Technical Implementation

The architecture emphasizes performance, statistical accuracy, and maintainability through careful design decisions:

Database Design

I designed a SQLite schema optimized for time-series data with WAL mode for concurrent access. The database includes sophisticated aggregation queries for count charts, efficient indexing for time-based lookups, and a flexible annotation system for process documentation.

Statistical Engine

The core statistical algorithms implement proper SPC theory including C4 constant calculations for unbiased estimators, Poisson distribution modeling for count data, and custom runs test implementation for randomness detection. All calculations maintain statistical rigor while optimizing for real-time performance.

Monorepo Architecture

The project uses npm workspaces to manage a clean separation between backend and frontend packages. TypeScript provides type safety across the entire stack, while Fastify delivers high-performance API endpoints for data ingestion and retrieval.

Development Insights

Challenges & Solutions

Statistical Accuracy vs Performance

Balancing rigorous statistical calculations with real-time performance requirements was challenging. I solved this by implementing efficient algorithms, using SQLite views for complex aggregations, and caching control limit calculations during setup periods.

Real-time Data Processing

Managing continuous data streams while maintaining database consistency required careful transaction design. I implemented proper error handling, used prepared statements for performance, and designed the schema to handle high-frequency data ingestion without blocking read operations.

Data Transformation Pipeline

Supporting multiple transformation methods while maintaining statistical validity required deep understanding of both the mathematical theory and practical implementation concerns. I created a flexible transformation system that preserves statistical properties while providing the necessary data normalization for accurate control charts.

The system successfully demonstrates the intersection of statistical theory, software engineering, and user experience design in creating tools for data-driven process improvement.