While 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.
“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.
Charting 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.
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.
Online 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.
Like a recipe designed for four servings that is called upon to serve 4,000, dramatically up-scaling the number of quality metrics being monitored may demand new approaches when deploying SPC. Here are some thoughts and approaches to overcoming barriers to scaling your SPC efforts.
Last weekend’s edition (Jan. 26-27) of The Wall Street Journal featured an article by Bill Gates (“My Solution to the World’s Biggest Problems: Measure them!”), in which he pointed out the importance of measurement systems. “From the fight against polio to fixing education, what’s missing is often good measurement and a commitment to follow the data. We can do better. We have the tools at hand,” he says.
Mr. Gates’ emphasis on measurement and data analysis is a nod to the quality movement – where many of the tools for tracking improvement were developed. SPC charting, SPC software, and other tools for continuous improvement share many of the aims of the Gates Foundation albeit in differing venues. Charting software, that can take the drudgery out of looking at vast amounts of data, makes it more likely that trends and problems are immediately apparent.
PQ Systems has been thinking about ways to make data easy to understand. If you find yourself “drowning in data but starved for information,” SPC software solutions, such as CHARTrunner Lean and SQCpack, provide just the ticket to respond to the challenge.
A 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.
Knowing 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.
You 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.