Statistical Process Characterisation & Control (SPC) – Perfect Quality What is Statistical Process Characterisation and Control and how will it help my workplace?

Statistical Process Characterisation & Control (SPC) is a method of monitoring a process during its operation in order to control the quality of the products being produced, as opposed to relying on post-production inspection.

SPC involves gathering information about the product, or the process itself, on a near real-time basis. This enables the operator to take immediate action on the process if required. The approach helps identify unique causes of variation and other non-normal processing conditions, thus bringing the process under statistical control and reducing variation.

Vative runs SPC programs to introduce participants to phases of process characterisation using common statistical techniques and problem-solving methodologies.


The Statistical Process Characterisation & Control (SPC) program covers the following:

  • Developing a Histogram and checking for normality
  • Calculating Standard Deviation, Mean Cp and Cpk to quantify the ability of the process, in order to respond to customer specifications
  • Developing Pareto Diagrams, Cause & Effect Diagrams and Multi-Vari Charts for process optimisation
  • Developing Variable and Attribute Control Charts for process monitoring

Identify the 4 Phases of Process CharacterisationPareto Chart

  • Phase I – Definition
  • Phase II – Analysis
  • Phase III – Optimisation
  • Phase IV – Control

Develop Histograms and Check for Normality

  • A graphic representation of the distribution of data
  • A quick view of the amount of variation in a process

Understand and calculate Standard Deviation

 

  • Cp & Cpk
  • Learn Mean, Sigma and Capability Indices

Pareto diagrams, C&E diagrams, Multi-Vari Analysis

  • 80-20 rule, Fishbone Charts, observation and recording

Key Points & Outcomes:

  • Data collection, Histograms, Multi-Vari Analysis, Pareto Diagrams, Process Capability Study, Scatter Diagrams, Control Charts

Background

The definition of ‘quality’ came from the earliest quality gurus, who were statisticians. Walter Shewhart is often called “the father of Statistical Process Control (SPC)” and inspired many others to utilise the power of data and statistics in quality control.

Before Shewhart joined Bell Telephone in 1918, the quality system involved inspection and the removal of defective parts. When defective parts (output) were found, adjustments would be made to the production process (inputs) to try and reduce the defects. Shewhart applied his statistical knowledge to production and argued against the accepted process – he argued that continually changing the production process in reaction to defects did not improve quality, and in many cases actually made it worse.

Shewhart’s reasoning was that the defects were being caused by variation in the manufacturing process and that this variation can be put into two categories:

  • Assignable [special] cause – variation due to specific special reasons
  • Chance [common] cause – even, predictable variation due to natural probability. Also called ‘noise’.

Common cause variation can be statistically modeled by a Mean (average value) and Standard Deviation (variation). If you continually change the production process (inputs), all you may be doing is moving the average value and not taking into account the natural variation, hence may be making the situation (output) worse.

To introduce Statistical Process Control (SPC), Shewhart invented the control chart, which allowed the statistical variation in a process to be visualised and understood, hence giving a new level of clarity to the understanding of quality.

Stewhart Control Chart

Today, almost 100 years later, control charts are the tool that is used to monitor all types of systems, in manufacturing and in other industries. After setting up a control chart, the monitoring of the process does not require skill or statistics knowledge – shop floor staff can plot data and only raise the alarm when the plot shows an abnormal result. This abnormal result is likely to be a ‘special cause’, which must be investigated and understood.

Six Sigma trained professionals are the ideal people to analyse and understand the variation in a process and use Statistical Process Control (SPC) to consistently deliver a high-quality predictable outcome.

Download our Free PDF to learn more about Statistical Process Control (SPC). Contact us to find out how we can help you implement Statistical Process Control (SPC) to help you achieve your business improvement goals.

Download our PDF to learn more about Statistical Process Control