About the Course

Due to competition, customer expectations regarding quality keep increasing. Organisations strive to keep meeting customer expectations to achieve their marketing objectives. The quality of provided products and services acquires much attention from organisations. There is a wide variety of statistical quality control tools. These tools require understanding their fundamental background and the situations where they are suitable tools to be used. Organisations need a guide to the appropriate set of quality tools as well as on how to use them. Additionally, organisations can be in need of a quality improvement methodology along which quality tools can be used. Six sigma has become a widely used improvement framework due to its effectiveness and project structure.

This Quality Assurance and Quality Control training course provides guidance on various quality tools and the underlying fundamental background. The course discusses how and when to use each of these tools. It illustrates some of the statistical process control tools described as magnificent tools and explains the topic of process capability analyses. The course represents a practical approach to statistical quality control and quality assurance. It explains six sigma along with the DMAIC improvement methodology and illustrates the allocation of the tools at the different phases of a six sigma quality improvement project.

Core Objectives

The delegates will achieve the following objectives:

• Define quality and important quality-related terms
• Identify the phases of DMAIC methodology in a Six sigma project
• Identify the tools to be used in each of the phases of the DMAIC methodology
• Use statistical process control tools
• Interpret basic control charts
• Develop an understanding of acceptance sampling and basic experimental design

Training Approach

This training course focuses on structured content, real-life implication, and interactive feedback. The topics and subtopics of the course are selected and ordered to support the sequential buildup of knowledge. It focuses on case studies, readings, and discussions to connect to practical implementation. Additionally, the delegates' feedback is sought regarding their needs, experiences, and acquired knowledge throughout the course. 

The Attendees

This training course addresses professionals assuming roles related to quality control and quality assurance. This includes professionals in quality teams directly conducting the quality activities as well as quality policy makers. Professionals working on six sigma projects or in the manufacturing/operations and are contributing to the quality of the product/service are also expected participants in the course.

Likewise, it will be valuable to the professionals but not limited to the following:

• Quality Engineers
• Quality Supervisors
• Quality Team Members
• Quality Directors and Managers
• Professionals working on Six Sigma Projects
• Manufacturing and Operations Professionals contributing to the quality of the product and service provided
• Roles in other areas which require a depth of quality knowledge

Daily Discussion

DAY ONE: INTRODUCTION TO QUALITY ASSURANCE AND CONTROL

• Quality Improvement
• Defining Quality
• Quality Terminology
• Six Sigma
• The DMAIC Methodology

DAY TWO: STATISTICAL PROCESS CONTROL

• Fundamental Statistical Models
• Introduction to Statistical Process Control (SPC)
• Histogram and Check Sheet
• Pareto Chart and Cause-and-effect diagram
• Defect Concentration Diagram and Scatter Diagram

DAY THREE: CONTROL CHARTS

• Variation
• Statistical Basis of Control Charts
• Control Charts for Variables
• Control Charts for Attributes
• Selection of Control Charts 

DAY FOUR: PROCESS CAPABILITY AND ACCEPTANCE SAMPLING

• Process Capability Analysis
• Introduction to Acceptance Sampling
• Sampling Plans
• Operating Characteristic Curve
• Average Outgoing Quality (AOQ)

DAY FIVE: BASIC EXPERIMENTAL DESIGN

• Introduction to Basic Experimental Design
• Guidelines for designing an experiment
• One-Factor-At-a-Time (OFAT) experiments
• Factorial experiments
• ANOVA