Quality Process Improvement

Blog posts and articles about data analysis and statistics for improving process quality in manufacturing and service.

By Matthew Barsalou, guest blogger A problem must be understood before it can be properly addressed. A thorough understanding of the problem is critical when performing a root cause analysis (RCA) and an RCA is necessary if an organization wants to implement corrective actions that truly address the root cause of the problem. An RCA may also be necessary for process improvement projects; it is... Continue Reading
Did you know that November is World Quality Month? The American Society for Quality is once again heading up this year’s festivities. Throughout the month of November, ASQ will be promoting the use of quality tools in businesses, communities, and institutions all over the world. You can check it out at http://asq.org/world-quality-month/. Here at Minitab, we’re also pretty excited about World... Continue Reading
In Part 5 of our series, we began the analysis of the experiment data by reviewing analysis of covariance and blocking variables, two key concepts in the design and interpretation of your results. The 250-yard marker at the Tussey Mountain Driving Range, one of the locations where we conducted our golf experiment. Some of the golfers drove their balls well beyond this 250-yard maker during a few of... Continue Reading
By Matthew Barsalou, guest blogger Teaching process performance and capability studies is easier when actual process data is available for the student or trainee to practice with. As I have previously discussed at the Minitab Blog, a catapult can be used to generate data for a capability study. My last blog on using a catapult for this purspose was several years ago, so I would like to revisit... Continue Reading
This week is National Healthcare Quality Week, started by the National Association for Healthcare Quality to increase awareness of healthcare quality programs and to highlight the work of healthcare quality professionals and their influence on improved patient care outcomes. In honor of the celebration, I wanted to point you to a few case studies featuring Minitab customers in the healthcare field... Continue Reading
In Part 3 of our series, we decided to test our 4 experimental factors, Club Face Tilt, Ball Characteristics, Club Shaft Flexibility, and Tee Height in a full factorial design because of the many advantages of that data collection plan. In Part 4 we concluded that each golfer should replicate their half fraction of the full factorial 5 times in order to have a high enough power to detect... Continue Reading
I read trade publications that cover everything from banking to biotech, looking for interesting perspectives on data analysis and statistics, especially where it pertains to quality improvement. Recently I read a great blog post from Tony Taylor, an analytical chemist with a background in pharmaceuticals. In it, he discusses the implications of the FDA's updated guidance for industry analytical... Continue Reading
Whatever industry you're in, you're going to need to buy supplies. If you're a printer, you'll need to purchase inks, various types of printing equipment, and paper. If you're in manufacturing, you'll need to obtain parts that you don't make yourself.  But how do you know you're making the right choice when you have multiple suppliers vying to fulfill your orders?  How can you be sure you're... Continue Reading
In 2007, the Crayola crayon company encountered a problem. Labels were coming off of their crayons. Up to that point, Crayola had done little to implement data-driven methodology into the process of manufacturing their crayons. But that was about to change. An elementary data analysis showed that the adhesive didn’t consistently set properly when the labels were dry. Misting crayons as they went... Continue Reading
Rare events inherently occur in all kinds of processes. In hospitals, there are medication errors, infections, patient falls, ventilator-associated pneumonias, and other rare, adverse events that cause prolonged hospital stays and increase healthcare costs.  But rare events happen in many other contexts, too. Software developers may need to track errors in lines of programming code, or a quality... Continue Reading
When we take pictures with a digital camera or smartphone, what the device really does is capture information in the form of binary code. At the most basic level, our precious photos are really just a bunch of 1s and 0s, but if we were to look at them that way, they'd be pretty unexciting. In its raw state, all that information the camera records is worthless. The 1s and 0s need to be converted... Continue Reading
Before I joined Minitab, I worked for many years in Penn State's College of Agricultural Sciences as a writer and editor. I frequently wrote about food science and particularly food safety, as I regularly needed to report on the research being conducted by Penn State's food safety experts, and also edited course materials and bulletins for professionals and consumers about ensuring they had safe... Continue Reading
The line plot is an incredibly agile but frequently overlooked tool in the quest to better understand your processes. In any process, whether it's baking a cake or processing loan forms, many factors have the potential to affect the outcome. Changing the source of raw materials could affect the strength of plywood a factory produces. Similarly, one method of gluing this plywood might be better... Continue Reading
Scientists who use the Hubble Space Telescope to explore the galaxy receive a stream of digitized images in the form binary code. In this state, the information is essentially worthless- these 1s and 0s must first be converted into pictures before the scientists can learn anything from them. The same is true of statistical distributions and parameters that are used to describe sample data. They... Continue Reading
  The NFL recently announced that after scoring a touchdown, teams will be required to kick the extra point from the 15 yard line as opposed to the 2 yard line. This is a pretty big change. And whether you’re trying to improve the quality of your process, or simply trying to make a sporting event more exciting, it’s always good to know what kind of effects your change will have. So I’m going to use... Continue Reading
In previous posts, I discussed the results of a recycling project done by Six Sigma students at Rose-Hulman Institute of Technology last spring. (If you’re playing catch up, you can read Part I and Part II.) The students did an awesome job reducing the amount of recycling that was thrown into the normal trash cans across all of the institution’s academic buildings. At the end of the spring... Continue Reading
By Erwin Gijzen, Guest Blogger In my previous post, we assessed the out-of-spec level for a process with capability analysis and visualized process variability using a control chart. Our goal is to reduce variability, but when a process has a multitude of categorical and continuous variables, identifying root causes can be a huge challenge. Analyzing covariance—using the statistical technique... Continue Reading
by Erwin Gijzen, Guest Blogger People who work in quality improvement know that the root causes of quality issues are hard to find. A typical production process can contain hundreds of potential causes. Additionally, companies often produce products with multiple quality requirements, such as dimensions, surface appearance, and impact resistance. With so many variables, it’s no wonder many companies... Continue Reading
This week I'm at the American Society for Quality's World Conference on Quality and Improvement in Nashville, TN. The ASQ conference is a great opportunity to see how quality professionals are tackling problems in every industry, from beverage distribution to banking services.  Given my statistical bent, I like to see how companies apply tools like ANOVA, regression, and especially... Continue Reading
Before cutting an expensive piece of granite for a countertop, a good carpenter will first confirm he has measured correctly. Acting on faulty measurements could be costly. While no measurement system is perfect, we rely on such systems to quantify data that help us control quality and monitor changes in critical processes. So, how do you know whether the changes you see are valid and not just the... Continue Reading