Statistical Process Control

Course details

Statistical process control (SPC) is used to monitor two types of manufacturing industry data; (a) measurements (variables) data, and (b) non-measurement data (attributes).

Statistical process control for measurements (variables) can be considered as essentially two separate but closely related subjects, namely (a) process capability analysis, and (b) control charts. Process capability is the ability of a manufacturing operation to

produce product within specification, and the calculation of the capability indices, Cp/Cpk and Pp/Ppk is widely practised in industry. However, control charts are not as widely used as they should be, and the role of the control chart is not well understood. The primary function of the control chart is to ensure, as far as is possible, that the process mean remains on target, in a stable condition, and it is important that personnel with responsibility for operating processes understand the important role that control charts play in assuring optimum capability.

Statistical process control for attributes is used to monitor the percentage of nonconforming product, or rate of occurrence of defects, in the manufacturing operation. The control chart for attributes provides personnel with responsibility for quality of product with crucially important information, which will assist them in controlling and reducing the incidence of defects.

Learning Outcomes

On successful completion of this training course, delegates should be able to:

• Undertake capability analysis, including analysis of non-normal data, and understand the meaning of the indices Cp/Cpk and Pp/Ppk
• Implement statistical process control methods in production
• Construct and interpret control charts for variables and attributes
• Demonstrate understanding of the important relationship between capability analysis and process stability as observed on control charts
• Use Minitab software for data analysis and identifying trends

Who Should Attend

• Product managers and team leaders
• Quality engineers, process engineers and technicians
• Staff concerned with controlling manufacturing processes

Entry Requirements

Participants don’t require a prior knowledge of statistics as the course will commence with a session on basic statistics. However, having knowledge of mathematics, for example Pass Leaving Certificate level, will be helpful in understanding the statistical concepts presented on the course.

Course Programme

Day 1

• The objectives and benefits of SPC – assessing process performance, distinguishing special from common causes
• Introduction to Statistics Underlying SPC
• Variation in manufacturing processes and its causes; Calculation of basic statistics including standard deviation
• The normal and standard normal distribution and use of the normal tables to calculate tail values
• Sampling distribution of the mean
• Process Capability Analysis
• Conducting process capability studies – identifying characteristics, specifications, and/or tolerances
• Distinguishing between natural process limits and specification limits, and calculating process performance metrics including percent defective and PPM
• Calculating process capability indices Cp, Cpk, capability ratio, and assessing process capability
• Calculating process performance indices Pp and Ppk and assessing process performance
• Process capability analysis involving nonnormal data:
• Using Box-Cox and Johnson transformations
• Fitting nonnormal distributions such as Weibull, Smallest Extreme Value and Largest Extreme Value

Day 2

• Variables Control Charts
• Identifying and selecting characteristics for monitoring by control chart
• Rational subgrouping
• Construction and interpretation of the X-bar and R chart. Distinguishing between common and special causes using the rules for determining statistical control
• Individual and moving range charts
• The role of control charts in optimising capability – explanation of how the differences between Cp/Cpk and Pp/Ppk arise.
• Attributes Control Charts
• The four attributes control charts; p, np, c, and u charts and when it is appropriate to use them
• Laney p’ and Laney u’ charts to be used when the sample size is very large
• The advantages/disadvantages of attributes control charts versus variables control charts.
• Interpreting the charts using the rules for determining statistical control