Quality Control and Quality Assurance Introduction Series v1.0
Complete Practical Guide from TQM, SPC, and Six Sigma Basics to Practice
Series Overview
This series is a comprehensive 5-chapter educational content designed for progressive learning from the basics to practice of quality control and quality assurance in process industries. It comprehensively covers fundamental concepts of TQM, Statistical Process Control (SPC), Six Sigma methodologies, ISO 9001 Quality Management Systems, and data-driven quality improvement.
Features:
- ✅ Practice-Oriented: 40 executable Python code examples
- ✅ Systematic Structure: 5-chapter structure for progressive learning from basics to applications
- ✅ Industrial Applications: Real-world quality management examples from chemical plants and manufacturing processes
- ✅ Latest Technologies: Python statistical analysis, machine learning-based quality prediction
Total Learning Time: 120-150 minutes (including code execution and exercises)
Learning Approach
Recommended Learning Sequence
For Beginners (Learning quality management for the first time):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
- Duration: 120-150 minutes
For Manufacturing/Quality Management Professionals (With foundational knowledge):
- Chapter 1 (Quick review) → Chapter 2 → Chapter 3 → Chapter 5
- Duration: 90-110 minutes
For Data Scientists (With statistical knowledge):
- Chapter 2 → Chapter 3 → Chapter 5
- Duration: 60-80 minutes
Chapter Details
Chapter 1: Quality Management Basics and TQM
Learning Content
- Basic Concepts of Quality Management
- Definition of quality and history of quality management
- Differences between QC, QA, and QMS
- Process capability indices (Cp, Cpk) calculation
- Quality cost classification and analysis
- TQM (Total Quality Management)
- Eight principles of TQM
- PDCA cycle implementation
- Pareto analysis (80-20 rule)
- Cause-and-effect diagram (fishbone diagram)
- Quality Improvement Methods
- 5 Whys analysis
- Defect rate calculation and confidence intervals
- Quality metrics dashboard
- Statistical quality evaluation basics
Learning Objectives
- ✅ Understand basic concepts of quality management
- ✅ Apply the eight principles of TQM
- ✅ Calculate process capability indices (Cp, Cpk)
- ✅ Identify critical quality issues using Pareto analysis
- ✅ Implement continuous improvement using PDCA cycle
Chapter 2: Statistical Process Control (SPC)
Learning Content
- Control Chart Basics
- X-bar and R control charts
- p-chart (defect rate control)
- EWMA control chart (exponentially weighted moving average)
- CUSUM control chart (cumulative sum control chart)
- Process Stability Evaluation
- Calculation of control limits (UCL, LCL)
- Detection of special cause variation
- Process capability analysis (Pp, Ppk)
- Western Electric rules
- Application to Chemical Processes
- Control charts for product purity
- Reaction temperature process control
- Quality control for batch processes
Learning Objectives
- ✅ Create and interpret various control charts
- ✅ Statistically evaluate process stability
- ✅ Calculate control limits correctly
- ✅ Detect special cause variations
- ✅ Apply SPC to chemical processes
Read Chapter 2 → (In preparation)
Chapter 3: Six Sigma Methodology and DMAIC
Learning Content
- Six Sigma Basics
- What is Six Sigma (3.4 DPMO)
- Sigma level calculation
- Relationship between DPMO and process capability
- Six Sigma belt system
- DMAIC Cycle
- Define: Project charter, VOC analysis
- Measure: Data collection plan, measurement system analysis
- Analyze: Root cause analysis, hypothesis testing
- Improve: Design of Experiments (DOE)
- Control: Standardization, control plan
- Utilizing Statistical Methods
- t-test, F-test, chi-square test
- Regression analysis and ANOVA
- Taguchi method
Learning Objectives
- ✅ Calculate and evaluate Six Sigma levels
- ✅ Practice DMAIC cycle
- ✅ Utilize statistical hypothesis testing
- ✅ Optimize processes using design of experiments
- ✅ Manage Six Sigma projects
Read Chapter 3 → (In preparation)
Chapter 4: ISO 9001 and Quality Management System
Learning Content
- ISO 9001 Basics
- Seven principles of ISO 9001:2015
- Process approach
- Risk-based thinking
- Document control and record management
- Internal Audit and Corrective Actions
- Internal audit plan development
- Checklist creation
- Nonconformance management
- Corrective and Preventive Actions (CAPA)
- QMS Operation and Performance Evaluation
- KPI setting and monitoring
- Management review
- Continuous improvement mechanisms
Learning Objectives
- ✅ Understand ISO 9001 requirements
- ✅ Plan and conduct internal audits
- ✅ Practice nonconformance management and CAPA
- ✅ Set and monitor QMS KPIs
- ✅ Implement process approach
Read Chapter 4 → (In preparation)
Chapter 5: Data-Driven Quality Improvement
Learning Content
- Quality Prediction Using Machine Learning
- Building quality prediction models
- Defect detection models (classification problems)
- Correlation analysis between process parameters and quality
- Feature importance analysis
- Anomaly Detection and Early Warning
- Anomaly detection using Isolation Forest
- Process anomaly detection using Autoencoder
- Time series anomaly detection (Prophet, STL decomposition)
- Alert system design
- Quality Big Data Analysis
- Defect pattern classification using clustering
- Quality characteristic summarization using principal component analysis (PCA)
- Causal inference and verification of quality improvement effects
- Dashboard construction and real-time monitoring
Learning Objectives
- ✅ Build quality prediction models using machine learning
- ✅ Implement anomaly detection algorithms
- ✅ Discover quality improvement opportunities through big data analysis
- ✅ Design real-time quality monitoring systems
- ✅ Practice data-driven quality improvement
Read Chapter 5 → (In preparation)
Overall Learning Outcomes
Upon completing this series, you will acquire the following skills and knowledge:
Knowledge Level (Understanding)
- ✅ Explain basic concepts of quality control and quality assurance
- ✅ Understand TQM, SPC, and Six Sigma theories
- ✅ Understand ISO 9001 Quality Management System
- ✅ Know statistical quality control methods
- ✅ Understand data-driven quality improvement approaches
Practical Skills (Doing)
- ✅ Create quality analysis and control charts using Python
- ✅ Calculate process capability indices (Cp, Cpk, Pp, Ppk)
- ✅ Detect anomalies using SPC control charts
- ✅ Implement Six Sigma DMAIC cycle
- ✅ Practice internal audits and CAPA management
- ✅ Implement quality prediction and anomaly detection using machine learning
Application Ability (Applying)
- ✅ Develop quality management plans for chemical processes
- ✅ Design and execute quality improvement projects
- ✅ Identify root causes of quality issues through data analysis
- ✅ Handle quality management tasks as a quality engineer
FAQ (Frequently Asked Questions)
Q1: What level of statistics prerequisite knowledge is required?
A: Basic statistics knowledge (mean, standard deviation, normal distribution) is sufficient. Necessary statistical methods are explained carefully in each chapter and can be learned practically with Python code examples.
Q2: What is the difference between this series and the process control series?
A: While the process control series focuses on "real-time control and automation," this series focuses on "quality evaluation, management, and improvement." Combining both enables optimal operation while ensuring quality.
Q3: Can this be applied to actual manufacturing sites?
A: Yes. All code examples are designed with practical application in mind. However, during implementation, please comply with on-site safety regulations, data security, and regulatory requirements.
Q4: Which Python libraries are required?
A: We primarily use NumPy, Pandas, Matplotlib, Seaborn, SciPy, and scikit-learn. For SPC, we use statistics, and for quality prediction, we utilize machine learning libraries.
Q5: What should I learn next?
A: We recommend the following topics:
- Design of Experiments (DOE): Efficient quality improvement experiments
- Reliability Engineering: Failure rate analysis, FMEA, FTA
- Manufacturing Data Analysis: Big data, IoT, digital twin
- Lean Manufacturing: Waste elimination and efficiency improvement
Next Steps
Recommended Actions After Completing the Series
Immediate (Within 1 week):
1. ✅ Conduct process capability analysis on your company's quality data
2. ✅ Identify critical quality issues using Pareto chart
3. ✅ Create control charts and evaluate process stability
Short-term (1-3 months):
1. ✅ Launch Six Sigma improvement project
2. ✅ Introduce SPC control charts to daily operations
3. ✅ Build quality prediction model
4. ✅ Organize ISO 9001 compliant quality manual
Long-term (6 months or more):
1. ✅ Build company-wide Total Quality Management (TQM) system
2. ✅ Foster data-driven quality improvement culture
3. ✅ Build career as quality engineering specialist
4. ✅ Present at quality management conferences or write papers
Feedback and Support
About This Series
This series was created as part of the PI Knowledge Hub project under Dr. Yusuke Hashimoto at Tohoku University.
Created: October 26, 2025
Version: 1.0
We Welcome Your Feedback
To improve this series, we look forward to your feedback:
- Typos, omissions, technical errors: Please report via GitHub repository Issues
- Improvement suggestions: New topics, additional code examples you'd like to see, etc.
- Questions: Sections that were difficult to understand, areas where additional explanation is needed
- Success stories: Projects using what you learned from this series
Contact: yusuke.hashimoto.b8@tohoku.ac.jp
License and Terms of Use
This series is published under CC BY 4.0 (Creative Commons Attribution 4.0 International) license.
What you can do:
- ✅ Free viewing and download
- ✅ Educational use (classes, study sessions, etc.)
- ✅ Modification and derivative works (translation, summarization, etc.)
Conditions:
- 📌 Author credit display required
- 📌 Modifications must be clearly indicated
- 📌 Please contact us in advance for commercial use
Details: CC BY 4.0 License Full Text
Let's Get Started!
Are you ready? Start from Chapter 1 and begin your journey into the world of quality control and quality assurance!
Chapter 1: Quality Management Basics and TQM →
Update History
- 2025-10-26: v1.0 Initial release
Your quality management learning journey begins here!