The ultimate goal of every manufacturing company is to achieve total customer satisfaction. One of the key initiatives in achieving this goal is through the introduction of Statistical Process Control (SPC).   The understanding of variation, its source and how to control the variation is the fundamental in making sure that the manufacturing process is stable. Through the introduction of statistical process control (SPC), it will prompt the user when there is out of control situation.  This course will introduce the participants to basic understanding of statistic, perform hand –on measurement system analysis, conduct process capability study and establish an effective Statistical Process Control (SPC) program. 

On successful completion of the course, the participants will be able to:

• Describe the overall concepts of variation to the process, fundamental statistics and measurement system.
• Describe the key elements and importance of an effective Statistical Process Control (SPC) program.
• Construct control charts for variables data (X-bar-R, and X-mR) and attribute charts (p, np, c, and u). 
• Outline the steps in implementing and maintaining the SPC program.

TARGET GROUP

Technicians, Supervisors, Executives, Quality & Manufacturing Engineers, Maintenance Engineers, and Management Personnel who are involved in continuous improvement activities  

TRAINING METHODOLOGY

Lecture, Group Discussion, Group Presentation, Discoveries, Hand On exercise, Case Study  

TRAINING PROGRAM OUTLINE

Day 1: 

Session 1: Introduction to continuous process improvement

Objectives:  

• Define SPC and its benefits

• Identify the 10-steps of SPC implementation

Session 2: Fundamental statistics

Objectives: 

• Describe the difference between a population and a sample 

• Name the 3 main characteristics of any distribution and measure central tendency and variability

• Analyze the spread using Histogram

• Use the Empirical Rule to characterize the normal distribution 

• Check for normality using graphical method

• Identify outliers using Box-Plot

• Calculate Z-Scores and obtain associated probability

• Explain the effect of averaging data due to the Central Limit Theorem

Session 3: Nature of variation

Objective: 

• Identify sources of variation in a process as common or special cause variation

• Differentiate variation by within and among subgroups

Session 4: Measurement System Analysis: repeatability and reproducibility study

Objectives:

•  List importance of the measurement system analysis

•  Conduct a measurement system study using the Gage R&R cross worksheet  

•  Determine gauge repeatability and reproducibility

•  Judge the adequacy of the measurement system

Day 2: 

Session 5: Process Capability study

Objectives:

• Define process capability

• Calculate process capability indices (Cp and Cpk).

• Perform process capability study

Session 6: Process Control for Variable and Attribute data

Objectives:

• Check the on-going variations of a particular parameter or the entire process

• Determine whether a process is stable

• Prevent abnormal factors to affect the process

• Predict the expected range of outcomes from a process

• Establish the Out of Control parameter

• Construct the Control Chart for Variable data

• Construct the Control Chart for Attribute data

• Establish the control plan to sustain process control