STATISTICAL QUALITY CONTROL TECHNIQUES
“Applying Data-Driven Methods to Improve Product Quality, Process Stability, and Operational Excellence”
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Course Schedule
| Date | Venue | Fees (Face-to-Face) |
|---|---|---|
| 17 – 21 Nov 2025 | Dubai – UAE | USD 3495 per delegate |
Course Introduction
In today’s competitive environment, quality excellence is a critical driver of customer satisfaction, regulatory compliance, and cost efficiency. Statistical Quality Control (SQC) offers a robust framework to monitor, control, and improve manufacturing and service processes using data-based decision-making.
This intensive 5-day course equips quality, operations, and engineering professionals with practical skills in the application of statistical techniques to real-world quality challenges. Participants will learn to use tools such as control charts, process capability analysis, sampling plans, and root cause analysis to ensure consistent output, identify variation, and drive continuous improvement across operations.
Course Objectives
By the end of this course, participants will be able to:
• Apply statistical methods to monitor and improve process quality
• Use control charts for variables and attributes to detect process shifts
• Perform capability studies to assess process performance
• Design effective sampling plans and measurement systems
• Utilize statistical tools for root cause analysis and problem-solving
Key Benefits of Attending
• Implement quality assurance methods that are data-driven and systematic
• Detect problems early and reduce process variation and waste
• Align quality control practices with ISO, Six Sigma, and Lean standards
• Strengthen internal audits and supplier quality management
• Support product compliance and customer satisfaction initiatives
Intended Audience
This program is designed for:
• Quality Control and Quality Assurance Engineers
• Process, Production, and Manufacturing Engineers
• Six Sigma Practitioners and Lean Leaders
• Quality Auditors and Compliance Officers
• Anyone responsible for maintaining or improving product/process quality
Individual Benefits
Key competencies that will be developed include:
• Statistical thinking for quality improvement
• Use of control charts (X̄-R, p, np, c, u) and interpretation
• Process capability indices (Cp, Cpk, Pp, Ppk)
• Measurement System Analysis (MSA) and Gauge R&R
• Corrective action based on statistical evidence
Organization Benefits
Upon completing the training course, participants will demonstrate:
• Improved quality assurance through proactive data monitoring
• Reduced rework, scrap, and customer complaints
• Stronger compliance with quality standards (ISO 9001, IATF 16949, etc.)
• Better supplier control and product traceability
• Enhanced analytical capability within quality teams
Instructional Methdology
The course follows a blended learning approach combining theory with practice:
• Strategy Briefings – Core SQC principles, tools, and quality frameworks
• Case Studies – Real industry examples of control charting and RCA
• Workshops – Chart creation, capability studies, sampling design
• Peer Exchange – Sharing sector-specific quality improvement strategies
• Tools – Excel-based templates, statistical tables, audit checklists
Course Outline
DETAILED 5-DAY COURSE OUTLINE
Training Hours: 07:30 AM – 03:30 PM
Daily Format: 3–4 Learning Modules | Coffee breaks: 09:30 & 11:15 | Lunch Buffet: 01:00 – 02:00
Day 1: Fundamentals of Statistical Quality Control
Module 1: Introduction to SQC and the Role of Variation (07:30 – 09:30)
• Types of variation, causes, and impact on quality
Module 2: Descriptive Statistics and Data Visualization (09:45 – 11:15)
• Measures of central tendency and dispersion, histograms, boxplots
Module 3: Workshop – Variation Analysis (11:30 – 01:00)
• Understanding data behavior and process spread
Module 4: Introduction to Control Charts (02:00 – 03:30)
• Principles, objectives, and chart selection
Day 2: Variable Control Charts and Process Monitoring
Module 5: X̄-R and X̄-S Charts (07:30 – 09:30)
• Control limits, subgrouping, and chart interpretation
Module 6: Individual-Moving Range Charts (09:45 – 11:15)
• For low-frequency or batch processes
Module 7: Process Stability and Special Cause Detection (11:30 – 01:00)
• Rules for out-of-control conditions and trends
Module 8: Workshop – Control Chart Construction (02:00 – 03:30)
• Hands-on plotting using real process data
Day 3: Attribute Control Charts and Process Capability
Module 9: p, np, c, and u Charts (07:30 – 09:30)
• Charts for defectives and defects, binomial and Poisson distributions
Module 10: Process Capability Analysis (09:45 – 11:15)
• Cp, Cpk, Pp, Ppk – interpretation and application
Module 11: Non-Normal Data and Capability for Special Distributions (11:30 – 01:00)
• Transformations and non-parametric methods
Module 12: Workshop – Capability Study (02:00 – 03:30)
• Analyzing process capability and improvement targets
Day 4: Sampling and Measurement Systems Analysis
Module 13: Acceptance Sampling Plans (07:30 – 09:30)
• OC curves, AQL, LTPD, and sampling standards (MIL-STD, ANSI/ASQ)
Module 14: Measurement System Analysis (MSA) and Gage R&R (09:45 – 11:15)
• Evaluating repeatability, reproducibility, bias, and resolution
Module 15: Workshop – Gage R&R Application (11:30 – 01:00)
• Hands-on use of MSA templates
Module 16: Risk-Based Thinking and Sampling Strategy (02:00 – 03:30)
• Linking sampling to process risk and quality impact
Day 5: Root Cause Analysis and Continuous Improvement
Module 17: Statistical Tools for Root Cause Analysis (07:30 – 09:30)
• 5 Whys, Pareto analysis, Fishbone diagrams, DOE
Module 18: Linking SQC to Quality Systems and Audits (09:45 – 11:15)
• Using SQC in ISO/QMS environments and internal audits
Module 19: Final Case Study and Data Presentation (11:30 – 01:00)
• Group exercise on solving a quality problem with SQC tools
Module 20: Review, Feedback, and Certification (02:00 – 03:30)
• Action planning and course close
Certification
Participants will receive a Certificate of Completion in Statistical Quality Control Techniques, validating their ability to apply statistical tools to monitor, control, and improve quality across processes.