dcsimg
 

Process Improvement

Blog posts and articles about the use of data analysis and statistics to improve processes in business and industry.

Unless you live under a black country rock, you’ve no doubt heard that the world recently lost one of the greatest artists of our time, David Bowie. My memories of the Thin White Duke go all the way back to my formative years. I recall his music echoing through the halls of our house as I crooned along whilst doing the chores. Then as now, Bowie’s creativity and energy inspired me and helped me do... Continue Reading
If you're just getting started in the world of quality improvement, or if you find yourself in a position where you suddenly need to evaluate the quality of incoming or outgoing products from your company, you may have encountered the term "acceptance sampling." It's a statistical method for evaluating the quality of a large batch of materials from a small sample of items, which statistical softwar... Continue Reading

7 Deadly Statistical Sins Even the Experts Make

Do you know how to avoid them?

Get the facts >
‘Statistics’ is a rising star. Everywhere I turn, people are talking about data and the value of being able to analyze and act on it. As someone who’s been writing about that for years, I say it’s about time. Statistics is like a talented actress whose decades of appearing off Broadway have finally paid off. For years, her work has been enriching our lives without us knowing it. Statistics helps... Continue Reading
Having delivered training courses on capability analyses with Minitab, several times, I have noticed that one question you can be absolutely sure will be asked, during the course, is: What is the difference between the Cpk and the Ppk indices? Ppk vs. Cpk indices The terms Cpk and Ppk are often confused, so that when quality or process engineers refer to the Cpk index, they often actually intend to... Continue Reading
According to this article published on Food Tank, over 22 million pounds of food is wasted on college campuses each year. Now that’s a lot of food waste! Students all over the country are noticing excessive food waste at their schools and are starting programs to bring awareness and improve the problem. Naturally, many of these programs have roots in Lean Six Sigma. In one example, a group of... Continue Reading
Control charts are a fantastic tool. These charts plot your process data to identify common cause and special cause variation. By identifying the different causes of variation, you can take action on your process without over-controlling it. Assessing the stability of a process can help you determine whether there is a problem and identify the source of the problem. Is the mean too high, too low,... Continue Reading
Don't be a grumpy cat when something on your capability report doesn't smell right. After pressing that OK button to run your analysis, allow your inner cat to understand how and why certain statistics are being used. To help you along, here are some capability issues that customers have brought up recently. Cp is missing You’ve generated a capability analysis report with the Johnson transformation... Continue Reading
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
People who are ill frequently need medication. But if they miss a dose, or receive the wrong medication—or even get the wrong dose of the right medication—the results can be disastrous.  So medical professionals have a lot at stake in making sure patients get the right medicine, in the right amount, at the right time. But hospitals and other medical facilities are complex systems, and mistakes do... 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
You run a capability analysis and your Cpk is bad. Now what? First, let’s start by defining what “bad” is. In simple terms, the smaller the Cpk, the more defects you have. So the larger your Cpk is, the better. Many practitioners use a Cpk of 1.33 as the gold standard, so we’ll treat that as the gold standard here, too. Suppose we collect some data and run a capability analysis using Minitab Statisti... Continue Reading
I recently guest lectured for an applied regression analysis course at Penn State. Now, before you begin making certain assumptions—because as any statistician will tell you, assumptions are important in regression—you should know that I have no teaching experience whatsoever, and I’m not much older than the students I addressed. I’m just 5 years removed from my undergraduate days at Virginia Tech,... Continue Reading
As we broke for lunch, two participants in the training class began to discuss, debate, and finally fight over a fundamental task in golf—how to drive the ball the farthest off the tee. Both were avid golfers and had spent a great deal of time and money on professional instruction and equipment, so the argument continued through the lunch hour, with neither arguer stopping to eat. Several other... 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