Data management means better timing and an understanding of the wider system

Barb ClearyAs World Quality Month celebrations are replaced with attention to holiday celebrations and November’s focus fades into the distant past, facing a new year in the darkness of December may represent an opportunity to pay attention to issues related to developing and managing technology and contemplating the future of a company or organization.

Last month’s issue of Harvard Business Review, with a cover story related to “What really keeps CEOs awake at night,” addressed the timing of innovative technologies in an article authored by Ron Adner and Rahul Kapoor (https://hbr.org/2016/11/right-tech-wrong-time). We all know of technological innovations that have been released too late and missed the revolution (the article cites Blockbuster’s failure to address the shift from rentals to streaming, for example), as well as those that have been ready too soon, falling into a market that does not perceive their value.

To avoid the “right tech, wrong time” scenario, Adner and Kapoor suggest looking more closely at the ecosystems that support technologies. Understanding the competition between the new and the old ecosystems can help to assure more accurate predictions about the timing of transitions, and to render decisions about allocating resources more effective.

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SQCpack 7: Not your father’s SQCpack

Barb ClearySQCpack has been around for a long time. PQ first developed and released the product in the early 1980s, before PCs were widely used, when statistical process control was beginning to make an impact on manufacturing processes. For many years, one upgrade after another appeared in ads in quality magazines.

Most people we speak to have seen or used some version of SQCpack during their quality improvement careers. The program has been the flagship of the PQ Systems products for over 30 years, moving from Apple to DOS to Windows and beyond.

With this history, you might think of a new release as just another upgrade. However, SQCpack 7 is new from the ground up. The rebuild focused on simplifying the work and amplifying the results of using SPC software.

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Customers ask, PQ Systems delivers: Requested features in SQCpack

“I’d really like to see real-time chart alarms and notifications.”
“How about customized out-of-control charts that are easy to create?”
“I wish I could see my chart in real time, even though it’s saved in another application.”
“Do you think you could translate the application into Portuguese?”

When customers ask for features in PQ Systems software solutions, little do they know how thoroughly these ideas are considered by the development team and technical support analysts. Most of the suggestions end up as usable features in the program, as it is updated and continually released to those with maintenance agreements.

A major release of SQCpack took place earlier this year, but developers continue to add features and improve established attributes. Nearly all of these have been derived from customer conversations, many in tech support communication. Of course, trade shows and other opportunities to speak directly with users are responsible for efforts to improve PQ Systems software applications as well.

So what new features and capabilities appear in the program as a result of customer requests? Drum roll, please, as project manager Matt Savage lists some:

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Scatter diagram – how handy is that?

Steve DaumScatter diagrams: a scattered approach? Steve Daum shows how this simple tool establishes support for understanding the correlations (and non-correlations) among factors.

In recent work, I’ve been thinking about the use and application of scatter diagrams. You have probably seen these. Here are some examples:

When you look at a scatter diagram, you are testing a theory. Statisticians call this testing a hypothesis. These scatter diagrams compare two variables: one variable on the horizontal or x-axis and a different variable on the vertical or y-axis. The theory you are testing is that there is no significant correlation between these two variables.

The quick answer to the question Is the theory correct? can be found by looking at the slope of the line. The flatter or more horizontal the line is, the more comfortable you can be that your theory is correct – that is: there is no significant correlation between these two variables. The steeper the slope, either downward or upward, indicates that your hypothesis is not correct. That is there does appear to be correlation between these variables. However, like almost everything with statistics, the quick answer does not tell the whole story.

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Improving quality in the supply chain—by talking about it

Barb ClearyWord of mouth may have the greatest influence when it comes to sharing information—both positive and negative– about products and services, most will agree. We think of a neighbor raving about his new lawn mower, or a co-worker sharing a positive experience with a plumbing service. While consumer products come to mind when we talk about word of mouth, the same process applies when it comes to the supply chain that produces these products.

Large automotive manufacturers such as Ford or GM depend on countless purveyors of parts and services that go into the final product, and count on these suppliers to provide quality products to support the final product quality. Certification to standards such as the ISO 9001 requirements are created to assure that this will happen.

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Quality control for anyone, any time

The newest release of SQCpack from PQ Systems is an easy and scalable application that includes all the tools needed to comply with critical quality standards, reduce variability, and improve profitability. A new user interface opens the door to improvements that streamline process control.

Among the newest features in the SQCpack solution are an improved StatBoard® statistical dashboard, real-time feedback, enhanced data collection tools for measurement devices and CMMs, link to SQL Server , Excel, and Oracle databases, and language support for German, Spanish LA (Latin America), Portuguese, and Mandarin.

“This is the most comprehensive expansion of our well-known SQCpack solution since its inception,” says Matt Savage, product manager. “SQCpack 7 is easy to use and easy to deploy, and provides secure data analysis to provide proof of quality.”

SQCpack can be downloaded as a fully-functional trial version, free for 14 days.

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.