Must Know SPC Tools
“A man is only as good as his tools“
But it’s not enough to simply have all the tools at your disposal,
You need to understand what each of the tools does, when to use each one of them, and how to be effective with the tool you’re using.
Treat this as an introduction to the most common and useful SPC tools.
A more comprehensive look at each one is warranted and presented in other articles on this site.
Though not comprehensive, this article should serve as a way to familiarize yourself with several SPC tools, thus providing the reader with a good jumping off point for them to dive deeper into each one, without loosing sight of the bigger picture.
Before we make changes to any process, we must attempt to understand the behavior that process exhibits under “normal” conditions. This understanding can be achieved most effectively by generating Histograms. They will help us understand the distribution of the process output and estimate the limits within which the process operates, under “normal” conditions.
While histograms are essential for understanding the distribution of a process and estimating limits, they’re not suitable for continuous monitoring, evaluation and control over the process. This is where Control Charts come in to play, as the name suggests, control charts help us answer the question, “Is the process in statistical control?”. An “in control” process is stable and predictable, in contrast, an “out of control” process is not predictable, as it’s under significant influence from special cause, or assignable, variability.
Now that we have estimated control limits and ensured that our process is stable, it doesn’t imply that we can meet the specifications of the product being manufactured, as our process may be incapable of meeting the requirements even when running as intended. This leads us to Process Capability Analysis to compare process performance to specifications.
If our process is incapable of meeting specifications, or needs to be improved for any other reason, we encounter the next problem, where do we begin? What would be the best use of our time, energy and resources? Pareto Analysis provides the answer. It combines frequency with which each nonconformity (from the specification) occurs with the relative cost to scrap or rework. It essentially tells you what the “biggest bang for the buck” improvement you can make is.
The Pareto Analysis identifies which nonconformity to target, now we need to identify which step or what characteristic of the raw materials are causing the nonconformity. This process starts with Flowcharts, which are meant to be an accurate representation of the manufacturing process, and Cause-Effect Diagrams, which serve to discover all possible causes for variation, narrowing it down to the root cause.
Flowcharts and Cause-Effect Diagrams are essential tools but they do not provide us with the “true cause” for the variation/instability, the next tool we are going to discuss is going to help you with exactly that. This tool is unlike the others in that it is more of a guided process than a mathematical or visual tool. Design of Experiments (DOE) is step-by-step process that helps you construct, conduct and analyze experiments in order to figure the “true/root cause” for nonconformity.
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This was a light introduction to some of the tools commonly used in Statistical Process Control (SPC), meant to pique the reader’s curiosity and give them the confidence to foray into the vast and exciting world of SPC. In doing so I hope it gave the reader a general sense of what each of the tools is used for.
In the series of upcoming articles on our website we will delve deeper into each of these one, while introducing new ideas that complement everything discussed here. At the end of this series we hope to be successful in convince the reader of the utility SPC tools in modern manufacturing and its critical role in Industry 4.0 and beyond.
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