Statistical Process Control (SPC)

In this series of blogs we’re going to take a closer look at Statistical Process Control or SPC – something that you may well have encountered in your Quality Improvement (QI) journey thus far. In this article we are going to drill down into SPC or control charts and how you might go about interpreting them. It is well known that measurement is a key element of a successful QI project, so let’s find out how SPC charts can help.

In this article we’re going to look at how to implement Statistical Process Control (SPC), and how you can use methodology for your Quality Improvement (QI) projects. Described as: ‘a branch of statistics that combines rigorous time series analysis methods with graphical presentation of data,’ SPC is well known as a tool that provides an easy way for people to track the impact of improvement projects, and in this blog we drill down into the detail.

Run Charts and Control Charts are two basic, quality control tools with varying reporting abilities. Run charts are one of the simplest to use but still provide valuable information. Control charts are a more advanced version of a run chart. You may hear this chart referred to as a Shewhart chart. Control charts still plots a single line of data, but also display an upper line for the upper control limit and a lower line for the lower control limit. Like Run charts, control charts can identify different types of variation such as common cause and special cause, however the rules are different.

In this blog, we look at three ways you can create an SPC chart. As we’ve seen in previous articles in this series, creating – and preparing to create – an SPC chart can be a lengthy and involved process, but one that ultimately can bring great reward to your Quality Improvement programme. In this article we explore how to create an SPC chart manually, using templates such as Excel and using QI software.

In this blog we’re going to drill down into how you can use Statistical Process Control (SPC) charts during the lifecycle of your QI project and the benefits this will bring. We’ll be finding out about how to discover issues in your processes using your SPC chart, how to use an SPC chart to prove the results of a PDSA cycle and how SPC charts can measure the long-term results of your improvement initiative.

A common problem with SPC Charts is knowing the right one to pick! To help you make the right choice and ensure you select the best possible chart for your data, we have created an 'SPC Chart Type' infographic guiding you through the decision-making process.

The XbarS chart is used for continuous data, tracks the average (Xbar) and variation (S - standard deviation) within subgroups. It monitors process centre and spread.

A T chart is a type of control chart used to monitor the time between events. It is particularly useful when you want to track how consistent the timing of a process is over time.

The I chart, also known as an Individuals chart, is used when you have continuous data, but you are only measuring one data point at a time, rather than subgroups. This is common when data is expensive or time-consuming to collect.

The U chart is a type of control chart used to monitor the rate of defects or nonconformities per unit when the sample size (number of units inspected) varies. This is helpful when you can't always inspect the same number of items in each sample.

A P chart is used to monitor the proportion of defective items in a sample, where each item is classified as either conforming or nonconforming (pass/fail). The sample size can vary, and that is accounted for in the control limit calculations.

The G chart monitors the number of opportunities until the event occurs. It records the number of opportunities between events.

The C chart is used to monitor the number of defects or nonconformities in a constant sized unit. This chart assumes that each unit has the same opportunity for a defect to occur.

A run chart is a simple yet powerful tool for tracking a process over time. It displays data points in sequential order, allowing you to easily visualize trends, patterns, shifts, or cycles in the process. Unlike control charts, run charts don't have control limits.

Control limits in Statistical Process Control (SPC) charts are described as ‘boundaries of a process that keeps changing over time,’ and ‘standard deviations located above and below the central line of an SPC chart.’ In this blog, we take a deep dive into control limits used within SPC charts. We’ll also take a look at what they are, how they are used and what they can tell us about your Quality Improvement (QI) processes.

We delve deeper into the type of variation that might occur within your Statistical Process Control (SPC) chart and how this works within your Quality Improvement project. We look at identifying the difference between positive or negative variation, as well as normal and unusual variation in data. We also take a closer look at common cause and special cause variation.

Statistical Process Control or SPC as it is commonly known can be daunting and can appear complex at times! We wanted to create a bit of a buzz around SPC and ask some of our Simana users to share what they consider in their experience to be the "Top Benefits of using SPC".

If you are unsure of how an SPC chart breaks down and have heard certain buzz words mentioned when referring to an SPC chart but didn't want to ask what it meant, then take a look at these basic pointers below.