A single undetected defect on a production line can multiply into a recall, a warranty claim, or a lost contract by the time it reaches the customer. Manufacturing quality management exists to catch that defect long before it gets there — through statistical control, structured process improvement, and a culture that treats consistency as non-negotiable. This course builds that capability specifically for a manufacturing environment.

Participants work through the core frameworks (ISO 9001, Six Sigma, Lean, TQM), the statistical tools used to monitor production in real time, and the structured problem-solving methods that turn a recurring defect into a solved one. The goal is practical: fewer defects, more consistent output, and production that runs at its intended efficiency.

Who Should Attend?

• Manufacturing managers and supervisors.
• Quality assurance and quality control personnel.
• Process engineers and continuous improvement professionals.
• Production staff and operators.
• Supply chain and procurement managers.

Knowledge and Benefits
After completing the program, participants will be able to:

• Apply core frameworks — ISO 9001, Six Sigma, Lean Manufacturing, and TQM — inside a real manufacturing environment.
• Use Statistical Process Control (SPC), control charts, and acceptance sampling to monitor and improve product quality.
• Apply Lean principles to eliminate waste, and use Six Sigma's DMAIC methodology to analyze and improve production processes.
• Identify the root cause of quality issues using statistical tools and structured methods such as Fishbone diagrams, 5 Whys, and FMEA.
• Comply with industry regulations (ISO, FDA), manage risk effectively, and maintain traceability throughout production.

Module 1: Quality's Place in Manufacturing

• Why quality matters in manufacturing, and how the discipline evolved historically.
• The real cost of poor quality to an organization.
• Quality standards and frameworks, including Total Quality Management (TQM).

Module 2: Controlling and Assuring Quality on the Line

• Quality Control (QC) versus Quality Assurance (QA), and the tools and techniques each relies on.
• Statistical Process Control (SPC), control charts, and process capability analysis.
• Acceptance sampling, inspection and testing procedures, including visual inspection, non-destructive testing (NDT), and automated inspection systems.

Module 3: Six Sigma and Lean in Practice

• The DMAIC methodology (Define, Measure, Analyze, Improve, Control) and root cause tools like Fishbone diagrams and Pareto charts.
• Statistical tools that support this work: hypothesis testing and regression analysis.
• Lean manufacturing principles — value stream mapping, waste elimination — and tools such as 5S, Kaizen, and Kanban for reducing cycle time.

Module 4: Statistics Behind Quality Decisions

• Mean, median, mode, and standard deviation as the building blocks of quality data.
• Probability distributions, hypothesis testing, and confidence intervals.
• Regression analysis, correlation, and the metrics used to track quality improvement.

Module 5: Solving Problems and Managing Risk

• Structured problem-solving methodologies and Failure Mode and Effect Analysis (FMEA).
• The role of leadership in building a quality culture, and how to involve and empower employees in it.
• Identifying manufacturing risks through qualitative and quantitative analysis, and documenting them for audits and traceability.

Module 6: Learning from Real Manufacturing Cases

• Success stories of quality improvement across different manufacturing sectors.
• Lessons drawn from cases where quality management fell short.