The differences between control limits and spec (specification) limits

Matt SavageThe differences between control limits and spec (specification) limits may seem irrelevant or nonexistent to those outside process production, but the gulf between them is in fact huge. In fact, they are two entirely different animals.

Spec limits may be designated by a customer, engineer, etc., indicating the allowable spread of a given measurement. Control limits, on the other hand, emerge from the process. The process data will determine what control limits are and help determine the stability of the process.

If one is tempted to use spec limits as control limits, the advice from process engineers and statisticians as well is simple: Don’t.

For an X-bar chart, for example, such as the one illustrated below, all of the X-bar values are well within the designated spec limits. Things are fine, right?

Not so fast. Remember that an “X-bar” is an average. And as PQ Development Manager Steve Daum points out, if you put one foot in a bucket of ice water and the other into extremely hot water, the average water temperature may be perfectly temperate, indicating a comfortable situation. In fact, the average does not reflect the range of the separate data points, one of which might be 33 degrees Fahrenheit, and the other 180 degrees. Comfortable? Probably not.

A histogram of the same process offers a much clearer picture of the reality of this process (see chart), with some data values well outside the specification limits, indicating an unacceptable result.

Let your data do the talking, when it comes to control limits. Don’t confuse information from the process with requirements for the process.

Focus on quality, customer service helps DANCO Precision thrive

When a precision industry has been around for more than 60 years and continues to thrive, you know it has been doing something right. This can clearly be said of DANCO Precision, Inc., a privately held company in Phoenixville, PA with nearly limitless capabilities in custom stamping for laminations and assemblies. The company uses PQ Systems software products to assure accuracy in gage calibration and quality in its products.

Responding to customer needs for sophisticated laminations and assemblies, DANCO utilizes some 350+ gages to assure accuracy in its calibration systems, depending on GAGEpack to assure that accuracy. “We use it hard,” says Bob Barandon, Director of Quality Assurance and Reliability for the company, noting that the software is “practical and easy to use. We love it.”

This was not always the case. When Barandon came to the company in 1988, gage records were maintained on 3” x 5” cards, he recalls. “We had a skeleton of an inspection department—hard-working and committed employees with little in the way of technologies that are standard now.” With customers whose products range from parts for the Hubble telescope to nuclear ships and gyros/servos, DANCO needed assurance of accuracy in its gages, and GAGEpack has served that need for years.

Continue reading

Not all control limits are created equal

Matt SavageTake a look at the charts below.

Aside from being created with two different programs, can you tell the difference between these two control charts?

CHARTrunner Lean

Microsoft Excel

Give up? Well to be fair, the answer isn’t all that clear. The same data set is repeated in both charts, and both use the same control limits.

So, what’s the big difference?

Continue reading

A little too neat: Nonrandom patterns raise alarms

Barb ClearyA control chart with a regular pattern demands some analysis. Regardless of the pattern that emerges when data is charted using SPC software, it should trigger an alarm and generate efforts to gather more information about the process. Learn what steps you should take to discover why a non-random pattern is emerging.

Continue reading

Avoid making two common mistakes – use the right chart for your data

Steve DaumKnowing the difference between different kinds of charts depends on not only understanding the relative advantages that each offers, but also knowing what information one wants to derive from the data. As we saw in an earlier column, it is critical to know how the data can be analyzed with respect to specific information that is required, and perhaps to anticipate other uses that the data might serve at the same time. Steve Daum discusses the differences between run charts and control charts, and offers perspectives on the benefits of control charts.

Continue reading

Share your charting story, create a charting innovation

Steve DaumYou are looking at a chart. You are going through an analysis and interpretation process. What data is being represented? How important is the data? Does the chart signal any changes? Does the chart show anything that is “bad” or “good?”  Does the chart offer proof of quality?

Ultimately, you want to answer the question: is any action required based on what I see?

Now, think about the workflow leading up to this. How did the chart get created?  How was the data gathered? What part of the process was difficult or error prone? Would it have been possible for you to miss this chart among your other tasks?

At PQ we’ve been pondering questions like these for more than twenty years. We are working hard on our products and services to reduce friction in your quality improvement processes. If you have a charting story to tell, please share it with us; who knows, it may lead to the next great quality improvement solution.

Like control charts? Wait ‘til you see this video…

If you’ve forgotten what control charts are and why they’re important, this three-minute video will remind you how this critical tool can help you demonstrate proof of quality performance, whether you produce a service or a product.

In a heartbeat, you’ll understand the difference between special cause variation and common cause variation—and you’ll learn what to do about it and how data speaks to you about managing your processes.

You may want to show this short, just-released, snappy video to your boss:

The difference between run charts and control charts

Steve DaumA customer recently asked one of our support representatives the following questions: What is the difference between a run chart and a control chart? And when should I use one vs. the other? These are great questions because they allow us to highlight some of the benefits of control charts.

When you create any chart, you are typically trying to answer a question. For example, you might be asking, “Has my process improved?” or, “Has my process gotten worse?”  You might be asking, “How is the process running today compared to yesterday?” Before you decide on using a run chart or a control chart, consider the type of question you want to answer.

Continue reading

An improved improvement chart

Matt SavageIf you have worked with count charts with large denominators, you have probably seen control limits that seem too narrow to be of much value. The p-chart is one of the attributes charts with this flaw.

A p-chart counts two things: 1) the number of non-conforming items (the numerator) and 2) the number of items inspected (the denominator). If you look at the glass half-full rather than half-empty, you might count the number of conforming items (rather than non-conforming). In either case, when the denominator is large, a problem may be present.

Consider the following chart which shows a p-chart from a plastic shopping bag manufacturer.

This chart measures the percent of plastic bags that failed a particular test. The bag manufacturer averages about 20,000 bags inspected in each sample and about 600 failures. As you can tell by looking at this chart, the limits seem too tight to be useful. The control limits are considered to be overly-dispersed.

Continue reading

Tips for designing your quality improvement spreadsheets

Steve DaumAmong our healthcare customers we find substantial use of Microsoft Excel. A recent survey of CHARTrunner customers found that 68% of them use data in Excel to produce their SPC charts and other analysis related to quality improvement. Excel is powerful and flexible and well suited to this job. However, this power can lead to complicated worksheets that are difficult to use and even more difficult to maintain. Once designed and deployed, a spreadsheet template may be in use for several years. Who will be around to debug an error or correct a formula that is discovered a year later?

To improve the situation requires well designed spreadsheets. Today, spreadsheets are so easy to setup and start to use – that we tend to gloss over using a design process to get started. Here are some tips to think about as you design your next quality improvement spreadsheet:

  1. Decide the primary purpose of the spreadsheet
  2. Make the primary purpose easy to accomplish
  3. Use the simplest possible sheet that accomplishes the purpose
  4. Don’t create future work for yourself
  5. Keep the data “pure”
  6. Be consistent among your sheets
  7. Favor traditional arrangements over weird arrangements
  8. Use a “notes” worksheet to document complex sheets

For a more detailed look at these tips see the following article in the PQ Systems knowledge base: