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.

Seeing your goals—with charts

Steve DaumWhile new year’s resolutions may already be long forgotten by many, those who are committed to personal goals continue to try to keep on the path to reach these goals early in the year. Good news: PQ Systems can help!

As we know from commercial applications of charting techniques, it is always far easier to garner information from numbers when they are illustrated visually, in charts or graphs. Even photos can help clarify meanings: A Melbourne, Australia, suburb trained volunteers to measure litter on the streets, giving them photos to support understanding of operational definitions of litter. (One cigarette butt in the gutter was not considered litter; two or more, or those on the sidewalk, were.)

So we have some ideas about supporting your personal goals for 2014 with visual use of data—a specialty of PQ Systems.

Charting data related to weight loss is commonplace, of course. Services such as Livestrong.com or Fitbit help to keep a running chart of weights entered daily, over a period of weeks, months, or years. Even crude hand-drawn charts make the point and demonstrate trends of weight loss or gain.

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Save time by creating your own defaults

Matt Savage“Easy to use right out of the box,” is what SQCpack users say about the software. It can be even easier if you customize default settings to align better with your specific needs.

Among the ways you can customize SQCpack are by changing default chart settings and altering entry preferences. Like the software itself, customizing defaults is easy to do and will save you time in the future.

When you select a default chart icon in SQCpack, the chart that appears represents default values; that is, it reflects common charting patterns that have been selected by the program’s developers.

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Leaner, faster, easier: GAGEpack features improve your life

Whoever invented the original concept for the Search & Replace feature in software programs would be delighted to see how far the concept has come. The upcoming version of GAGEpack, scheduled for a second quarter 2014 release, expands on previously available Search & Replace capabilities to make the management of large gage inventories as simple as possible.

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Selection of GAGEpack reflects company’s commitment to quality

A company with pride in the quality of its products needs to be very sure that its suppliers boast of the same levels of quality in what they provide. For Imagineering Machine, that means selecting GAGEpack from PQ Systems to assure accuracy in gage calibration records.

This family-owned business, with headquarters in St. Louis Park, MN, has been known for its excellence in close tolerance production and prototype machining for nearly 30 years. Now ISO9001 certified, the company has always taken pride in its approach to quality. Its customers remark about the company that “they do not disappoint,” in their consistently high quality standards. A customer working with Imagineering Machine on a part used in the wind power industry nominated the company for “Innovation of the Americas,” and Imagineering Machine was recognized with this award in 2010.

Pursuing this innovation, Imagineering Machine recently added a new Haas ST20SSY Live Tool Lathe, according to the company website. The new machine brings the latest in turning technology to the company, while allowing more time for the CNC Milling department. The company is known for pushing the limits of machines to increase productivity, while tightening tolerances and providing high quality machined parts.

Imagineering Machine’s commitment to quality extends to all manufacturing operations, which are designed to meet customers’ most demanding specifications. Processes from receiving to machining to final inspection are documented, and machining operations maintain tight tolerances. Craig Livingston, who heads the Quality Control Department, oversees maintenance of inspection records and material certifications, with several hundred various types of gages and tools to calibrate, manage, and oversee. Statistical process control is used to assure quality in tools and inspection equipment.

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Taking your charting skills home: KPIs and health maintenance

Steve DaumCharting data is not just for the shop floor any more, as we have long discovered. Measuring outcomes for healthcare, banking, education, and other environments has become commonplace. What about taking your charting skills home? Key Process Indicators can help to evaluate weight gain/loss, exercise patterns, and more, as this piece by Steve Daum demonstrates.

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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.

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Separate your charting and data analysis tools from your enterprise tools

Steve DaumOnline debate rages about whether potatoes and onions should be stored together, with the “no” side saying they both give off gases that accelerate spoilage, and the “yes” followers asserting that it makes no difference. Whether you agree or disagree, you can follow the underlying concept: some things do need to be separated in order to perform at their best. (Hence the practice of assigning twins to separate classrooms, perhaps.)

An important principle in software development is known as separation of concerns. The idea is that different concerns should be handled by different bodies of source code. For example, one body of source code should focus on saving and retrieving data from a database. A different body of code should focus on doing statistical calculations; these two concerns should never be mixed in the same body of source code. When this principle is violated, the source code is sometimes described as having a “code smell” which is not a good thing. Even worse than potatoes that smell like onions.

User requirements for software change all the time. They are dynamic. If you have source code nicely compartmentalized, you will be more nimble at stringing it together to meet some new user requirement.

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The great divide: Creating silos with data analysis systems

Barb ClearyOnce upon a time, there was a manufacturing facility in Ohio that happily utilized Excel data in its quality management system, with some ten years of data organized in a way that was appropriate and useful to the quality manager and his department as they looked for correlations between problem metrics and other metrics that might have contributed to the problem.

In the same plant, a separate automation system was monitoring and recording data on metrics gathered throughout the facility. This system could bring up run charts from hundreds of metrics where data had been automatically recorded, and charts were available whenever anyone needed them.

Sounds fine, right? Unfortunately, this was not a happily-ever-after story, but an example of ways that different systems fail to speak to one another. But wait—there will be an answer to this dilemma.

The quality manager was looking for correlations. But he could look only at the Excel system that was used in his department. Meanwhile, on the other side of the data divide where the production manager and process engineers, in their own silo, used a different system for data analysis, a number of production-system metrics were in fact contributing to the problem metrics, but the correlation could not be identified. The two systems, sadly, could not talk to each other.

The happy accident lay in the quality manager’s discovery of CHARTrunner. Using CHARTrunner Lean, the quality manager removed the charting and data analysis from one of the systems—in this case, the quality department—and used CHARTrunner to create charts that visualized and compared metrics from both systems. So there was a happily-ever-after ending, after all.

In this example, there were only two systems separated by their approaches; in many organizations, there may be five or six different systems to be bridged with a CHARTrunner application.

How well do your systems collaborate and support each other? Check out CHARTrunner for help with bringing them together.