COURSE OVERVIEW:

The meaning of this course lies in the transition from reactive quality checking to proactive process steering through the application of advanced statistical methodologies. In high-volume industrial environments, relying on final inspections is inefficient and costly; true quality is achieved by understanding and controlling the inherent variation within the production process itself. This program provides the mathematical and analytical tools necessary to differentiate between common-cause and special-cause variation, enabling technical leaders to make data-driven decisions that stabilize output and reduce waste.

The scope of the training addresses the comprehensive suite of Statistical Process Control (SPC) tools, focusing on the selection, implementation, and interpretation of control charts for both attribute and variable data. It covers the assessment of process capability and performance indices, ensuring that the manufacturing or analytical process is physically capable of meeting customer specifications. Participants will explore the design of experiments (DOE) for process optimization and the use of regression analysis to model the relationships between process inputs and final product quality.

The coverage includes detailed instructions on the use of Shewhart charts, Cumulative Sum (CUSUM) charts, and Exponentially Weighted Moving Average (EWMA) techniques for detecting small process shifts. The course emphasizes the importance of data integrity, the calculation of measurement system capability through Gage R&D studies, and the implementation of Lean Six Sigma principles for continuous improvement. By mastering these statistical protocols, participants will be able to enhance process reliability, minimize the cost of poor quality, and ensure the long-term competitiveness of their industrial operations.

COURSE OBJECTIVES:

After completion of this course, the participants will be able to:

• Distinguish between common-cause and special-cause variation in a process.
• Select the appropriate control chart for different types of industrial data.
• Calculate and interpret process capability indices.
• Perform Gage R&R studies to validate measurement system reliability.
• Implement Shewhart control charts for variable and attribute data.
• Utilize CUSUM and EWMA charts to detect subtle process drifts.
• Conduct hypothesis testing to compare process means and variances.
• Apply regression analysis to identify key process drivers and outcomes.
• Design screening experiments to optimize industrial process parameters.
• Utilize Pareto analysis and Fishbone diagrams for root cause investigations.
• Integrate SPC data into the organizational Quality Management System.
• Communicate statistical findings to stakeholders for strategic decision-making.

TARGET AUDIENCE:

This course is intended for Process Engineers, Quality Assurance Managers, Production Supervisors, and Laboratory Technical Leads involved in industrial manufacturing.

TRAINING COURSE METHODOLOGY:

A highly interactive combination of lectures, discussion sessions, and case studies will be employed to maximize the transfer of information, knowledge, and experience. The course will be intensive, practical, and highly interactive. The sessions will start by raising the most relevant questions and motivating everybody to find the right answers. The attendants will also be encouraged to raise more of their questions and to share in developing the right answers using their analysis and experience. There will also be some indoor experiential activities to enhance the learning experience. Course material will be provided in PowerPoint, with necessary animations, learning videos, and general discussions.

The course participants shall be evaluated before, during, and at the end of the course.

COURSE CERTIFICATE:

National Consultant Centre for Training LLC (NCC) will issue an Attendance Certificate to all participants completing a minimum of 80% of the total attendance time requirement.