Six Sigma is a business methodology focused on improving the quality of goods or services an organization produces by reducing variance in the production process. Six Sigma leaders know that when a production process lacks the stability to create high-quality output consistently, it will continue producing defective products until someone intervenes and improves the process. Reducing variation requires constant monitoring and measurement as systems tend to erode if not kept in check. Over the years, Six Sigma practitioners have developed a set of tools and methods that address control, and problem-solving and improvement to eliminate defects and waste. Here are a few of the most essential.

Six Sigma is a business management methodology that leverages a scientific approach to quality measurement with the aim of reducing variation and defects. The method was pioneered by Motorola and Allied Signal and then made famous by GE who boasted $10 billion in savings during the first years of implementation.

Lean is another methodology, philosophy, tool set, and management system that also uses a scientific problem solving approach, and more. "Lean" is a generic term that was given to the Toyota Production System, or TPS. The two pillars of TPS are usually described as "just in time flow" and "quality at the source."

Lean and Six Sigma are often used together by organizations in multiple industries, bringing methods and mindsets from each approach.

Many frameworks exist for implementing the methodologies. Many of those tools can be useful to organizations whether or not they fully embrace the Lean or Six Sigma approaches. Here are a few that our customers have found most valuable.

DMAIC is said to be the Six Sigma methodology’s roadmap to improvement. It is one of the core tools of the approach, but organizations also use it as a standalone improvement technique. We have clients in almost every industry from healthcare to construction who have achieved quantifiable impact against core business metrics by using this technique. Here are some of the questions we get asked about it most often.

Statistical control charts are a useful tool for managing and improving all sorts of processes. They’ve been used in manufacturing for decades and are increasingly popular in other industries from healthcare to higher education. Control charts give leaders a clear and consistent way of evaluating and talking about process behavior and performance. They help managers make good decisions about which processes are stable and which require attention.

While at first glance, they may look very simple, just a line graph with data points plotted over time, there are some important concepts that you should understand if you are new to the approach.

This post covers the basic, yet critical principles of control charts.

DMAIC (Deh-May-Ick) is one of the most important tools in the continuous improvement toolbox. It is most closely associated with the Six Sigma methodology, but it is also used by those who practice Lean or don’t subscribe to a methodology at all. The reason that DMAIC is so popular is that it is a problem-solving framework that takes teams from discovering root causes to long-term, stable standard work. It is a repeatable process that employees can learn to apply to any number of process problems.  

DMAIC stands for Define, Measure, Analyze, Improve, and Control.  Motorola is credited with bringing it to prominence, although it was essentially an evolution of the improvement methodology used by Toyota.

A deeper dive reveals why it works so well.

Organizations that subscribe to the Lean or Six Sigma business methodology, and others that are devoted to continuous improvement, often use a host of visual management tools to achieve consistency and introduce positive change. Kanban, huddle boards, and value stream maps are all very popular and effective. Process control charts are another valuable visual management tool for recognizing and reacting to process variation.

Here are the details about why they are so useful.

Statistical Process Control

It is probably helpful to begin with a definition of process. A process is quite simply anything that gets done. It could be putting gas in your car, filling out a time sheet, delivering source code to QA, or checking in a patient. Each of these activities results in some output. Sometimes it is a product, but often it is a service or a deliverable to the next process. In addition to the result of the process, data is also generated. Statistical process control is the act of using that data to make the process better. The data might be related to timeliness, cost, quality, or quantity.

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Investing in software to support your Six Sigma initiatives is an effective way to make sure that you get the most out of the methodology. Setting up a structure for improvement makes it simpler for people throughout the organization to participate in, and effectively oversee, continuous improvement efforts.

Organizations that leverage Six Sigma software collect more opportunities for improvement, implement more solutions, and see a higher ROI for Six Sigma. There are a number of solutions on the market that were either developed as Six Sigma management tools or repurposed as such, so we thought it might be helpful to identify some of the most important capabilities that you should look for when evaluating this type of technology.

Donald J. Wheeler, PhD is a world-renown expert in continuous improvement, having worked with W. Edwards Deming and later writing the classic book Understanding Variation. 

Wheeler once wrote and said, "Statistical Process Control is, at its heart, about getting the most from your processes. It is about the continual improvement of processes and outcomes. And it is, first and foremost, a way of thinking... with some tools attached." 

I’d like to thank him for providing the perfect quote for a blog about process control charts because measurement, control, and improvement are exactly what they are designed to enable.

What is a Process Control Chart?

Process control charts (or what Wheeler calls "process behavior charts") are graphs or charts that plot out process data or management data (outputs) in a time-ordered sequence. It's a specialized run chart. They typically include a center line, a 3-sigma upper control limit, and a 3-sigma lower control limit. There might be 1- or 2-sigma limits drawn in, as well. The center line represents the process mean or average (and sometimes the median).

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Corrective and preventive action (CAPA) are improvements to an organization's processes taken to eliminate causes of defects and waste. The approach focuses on the systematic investigation of the root causes of identified problems or identified risks in an attempt to prevent their recurrence (for corrective action) or to prevent occurrence (for preventive action). The success of CAPA implementation is largely dependent on an understanding of KPIs and the ability to use them to identify performance non-conformities.

Like any other continuous improvement approach, CAPA programs are most effective when supported by technology.

CAPA software helps ensure that each identified problem or risk is addressed quickly and completely.

When it comes to continuous improvement techniques, you have many options. There are a variety of approaches and tools designed to support your effort to effect positive change. But it can be difficult to decide which techniques will work best. In the case of PDSA and DMAIC, it can be extra confusing because they are quite similar.