Statistical process control (SPC) is essentially a combination of two different but directly
linked statistical tools; (a) capability analysis, (b) control charts. Capability analysis provides
information on the ability of the process to meet specification; while control charts must be
used to ensure process stability and that the goal of optimum capability, is achieved at all
SPC is used in all manufacturing industries, and SPC control chart are also used to identify
trending in laboratory data. In the pharmaceutical industry SPC is often referred to as
Continued Process Verification (CPV). The FDA has identified CPV as an essential third
phase of process validation, providing “continual assurance that the process remains in a
state of control (the validated state) during commercial manufacture” – FDA Guidance for
Industry, January 2011. This objective is achieved through the use of SPC control charts.
Pharmaceutical plants worldwide are urgently gearing up to implement control charts on the
production line in order to meet the goals of CPV set by the FDA.
Cp/Cpk and Pp/Ppk are two sets of indices commonly used as measures of capability. There
is a high degree of confusion across manufacturing industry as to the relationship between
these two set of indices, and the specific circumstances in which each of the two sets should
be used – they are frequently used incorrectly. Major emphasis will be placed during this
course on providing delegates with a thorough understanding as to why there are
differences between the two set of indices, and their appropriate usage.
Statistical process control is most usually associated with measured product characteristics;
the resulting data is usually known as variables data. However, SPC also has a major role as a
tool for the monitoring and control of manufacturing defects (commonly known as
attributes). 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. There are four types of attribute control charts and use of
these will be described during the training course.
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,
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
Calculating process performance indices Pp and Ppk and assessing process
Process capability analysis involving non-normal data:
Using Box-Cox and Johnson transformations
Fitting non-normal distributions such as Weibull, Smallest Extreme Value and
Largest Extreme Value
Variables Control Charts
Identifying and selecting characteristics for monitoring by control chart
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
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
Interpreting the charts using the rules for determining statistical control
Who Should Attend?
Product managers and team leaders
Quality engineers, process engineers and technicians
Staff concerned with controlling and monitoring manufacturing processes
What will I learn?
Participants achieve the following learning outcomes from the programme;
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
What are the 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.
For In-House courses, the tutor will contact you in advance to discuss the course programme
in more detail in order to tailor it specifically for your organisation.
Delegates will receive a very comprehensive course manual written by the course tutor,
which explains the statistics underlying SPC and worked examples of the calculation of
process capability analysis and the calculation of control chart limits. The manual will be
prepared using data collected in advance from the company, and the participants will
undertake exercises in the manual using their own workplace data.
What software do we use?
Minitab will be demonstrated as part of the training. Delegates are invited to bring a laptop
loaded with either Minitab 16, Minitab 17, or Minitab 18 and they will work through several
Minitab exercises throughout the two days of the course. A free 30 day trial version of
Minitab 18 is available on www.minitab.com. The course will also be beneficial to delegates
who are not in a position to bring a laptop with Minitab.
Training Provider: SQT Training
Bookings are closed for this event.