Use color coding to improve object recognition and draw focus to items that need attention.
SQCpack 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.
“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:
Scatter 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.
Word 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.
Predicting the future: This article, originally published in QualityDigest.com, addresses the power of charts in providing useful prediction.
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.
Manufacturing has experienced a kind of revival on a global scale. Production demands are on the rise as an empowered population is spoilt for choice.
In an arena of such competition, companies need to leverage any advantage that they may have. Statistical Process Control (SPC) represents this advantage. By reducing production variability and waste in order to drive down manufacturing costs, companies are offering higher quality products on a consistent basis.
HOW HAS SPC BECOME INCREASINGLY RELEVANT?
SPC has for long been a way to deliver better value to customers and thus expand market reach and dominance. Reducing waste by monitoring processes saves time and assures customer satisfaction. In recent years, a prevailing reason to keep manufacturing costs in check is one of survival. Masses are unforgiving and a mistake is costly, both in financial and in social terms.
WHAT IS SPC?
Statistical Process Control represents a way to prevent defective products, not simply to inspect for this waste. By putting tools to collect and analyze data on a real-time basis into those closest to the process itself, companies can determine the possibilities for product flaws before they must be scrapped as waste.
HOW SPC DRIVES DOWN MANUFACTURING COSTS?
Understanding variation in processes is fundamental to product quality. Reducing this variation means that processes become more predictable—and predictability is a key to saving costs. Established statistical methods help to reduce variability and save costs. Identifying variability and identifying its source early in a process reduces scrap, rework, and waste.
About 80 percent of manufacturing costs pertain to the purchase of raw materials. The quality of this raw material is indicated by a constant Cpk , a statistical index that indicates the capability of a process to produce expected outcomes. Higher values of this constant are desirable as they indicate superior quality. If Cpk varies from one production lot to another, the performance of the products from different batches under extenuating conditions will not be consistent.
SPC software solutions help identify inferior quality in raw material batches, so these batches will not make their way into the final product. With anomalies eliminated, potential orders can be saved from cancellation and quality ensured all across the board.
In short, without real-time SPC software solutions, no manufacturing unit can hope to achieve success in national or global markets.
Do you have data that’s an anomaly or special cause that you want to exclude from your analysis? Do you want the ability to temporarily exclude certain data from your data set analysis? Special causes and outlying data can occur in any data collection process, learn how to easily handle these situations.
If you are calculating and charting average weekly temperature in a room, but find that one night the thermostat has been inadvertently set to 90 degrees, how will that data point affect your average? Clearly, the answer is that it would create a false sense of a higher temperature average for the week, and in effect create a misleading report.
There may be times that it is appropriate to leave out irregular data points such as this one, to be sure, without feeling that you’re “cheating” on the numbers. Additionally, there may be special causes that have been addressed and no longer apply to the calculations, and you’d like to remove these. Or perhaps you’d like to exclude certain pieces of data on a temporary basis, to analyze their effect on the calculations. Having the flexibility to remove or exclude anomaly data from your control chart can help you more easily focus on your process and remove any additional noise from the chart or the calculations.
The 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.