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I have found that an XmR chart is the easiest way to display attribute data. Simply convert the numerator/denominator into a ratio and plot the ratio.
- defects per day could be a c chart, but an XmR chart works just as well
- defects/samplesize could be np, p or u chart, but XmR chart works just as well using the ratio
Almost two decades ago, Tom Pyzdek said: X chart provides an excellent approximation to the p chart.
More recently, Donald Wheeler noted that XmR chart limits will be very close to c, np, p or u chart limits if the underlying distribution is correct.
Continue Reading "Use XmR Charts instead of c, np, p and u Charts"
Posted by Jay Arthur in QI Macros, Six Sigma, Statistics.
Customer asked me what seemed like a strange question: What specification limits do I need to get a Cp greater than one? Usually her customer should set specification limits, but her boss wanted to know what they could deliver. Hmmmm!
Then I realized that since QI Macros templates (e.g., XmR chart) calculate the average and sigma estimator, the LSL/USL for Cp = 1.0 would be:
LSL = Average – 3*SigEst USL = Average+ 3*SigEst
For Cp = 1.33, just change the 3 to a 4; Cp = 1.66, change the 3 to a 5. Here’s an XmR chart template with some sample data and calculations to reverse engineer spec limits:
Continue Reading "What USL/LSL Do I Need for Cp>1?"
Posted by Jay Arthur in QI Macros, Six Sigma, Statistics.
A customer called upset about Cpk. He had a runout of 0.010, but was getting a very low Cpk. Turns out he’d made the classic mistake of confusing a hard limit (e.g., zero) with a specification limit. I had him use QI Macros with an Upper Specification limit (USL) and no Lower Specification Limit (LSL). His Cpk immediately jumped to a more expected value of 1.78.
Later in the day another customer asked why Cpk is calculated as the minimum of the upper or lower Cpk? Because you use the one closest to the average. I think that customer may have had the same problem, confusing a hard limit with a specification limit.
Continue Reading "Cpk and Hard vs Specification Limits"
Posted by Jay Arthur in QI Macros, Six Sigma.
Many people avoid Six Sigma because they think it involves a lot of math and statistics. You know, formulas. I don’t think you need any formulas. You don’t need to be a statistician. You just need software that went to college and knows the formulas.
In The Math Gene, Author Keith Devlin explores “why so many people find mathematics impossibly hard.” He says: mathematics is the science of patterns. Isn’t that what we’re trying to do in Six Sigma, separate the wheat from the chaff, separate the signal from the noise and detect the underlying patterns of performance?
Continue Reading "Is Fear of Math Holding You Back?"
Posted by Jay Arthur in QI Macros, Six Sigma.
Nobody wants to read your spreadsheet! No matter how hard you try to make it pretty, create great labels or whatever, the only person who can read your spreadsheet is a CFO or Excel spreadsheet geek. And the Excel geek is going to tell you 10 ways to make it prettier.
The purpose of data is to provide insights for action, not just report past performance.
How Do I Know That Most Excel Users Try to Make Their Spreadsheets Readable By People?
According to Renu Davi, Sr. Program Manager for Excel, Microsoft tracks how people use Excel. The vast majority of the 650 million users use it for lists and reports.
Continue Reading "Nobody Wants to Read Your Spreadsheet!"
Posted by Jay Arthur in Data Mining, QI Macros, Six Sigma.
Over the last 25 years, I’ve gotten to see Six Sigma failures and successes. But in spite of all of the belts trained and investments made, why isn’t product and service quality any better? Why is there so much hassle? Why aren’t more customer experiences hassle-free? I’ve developed a mental list of the most common types of failures. Here’s my fishbone diagram for Six Sigma failures. I’d encourage you to develop your own.
80% of the businesses in the U.S. are service businesses, yet Six Sigma training is extensively focused on the manufacturing factory floor. It takes too long to teach people everything they might need to know to solve all of the problems they might ever encounter.
Continue Reading "Why Six Sigma Fails"
Posted by Jay Arthur in Healthcare, Manufacturing, QI Macros, Service, Six Sigma.
Renu Davi, Sr. Program Manager for Excel, reported at Excelapaloosa that two-thirds of the 650 million users use Excel to make lists. The other third of users do deeper analysis, but I’m wondering how? Microsoft keeps tabs on Excel usage. Here’s some statistics for usage in an Excel workbook:
- Only 4.3% have a function like SUM(A1:B2)
- 7.5% have Pivottables
- 17% use Freeze Panes
- 54% use Merged Cells (instead of Center Across Selection which works much better in many ways)
I wonder why we need any new features if the vast majority of users don’t or won’t use them. Maybe we need to invest in teaching people how to use the Excel features they have.
Continue Reading "Users Barely Using Excel"
Posted by Jay Arthur in QI Macros, Statistics.
I got a call from a QI Macros customer who works at a luxury car dealership. Customers were upset because their cars were spending too long in the shop. The dealership tracked the length of stay of every car, the symptoms and barriers to getting the car done when expected.
I was struck by the similarities between what he was describing and a hospital. Patients come in, get diagnosed, treated, admitted and eventually discharged. This is the same problem as the maintenance shop.
I asked if the maintenance department had information about the type of problem, missing parts, age of the car and so on.
Continue Reading "How is a Hospital Like a Car Dealership?"
Posted by Jay Arthur in Healthcare, Lean, QI Macros, Service, Six Sigma.
The June, 2016 HRB article by Scott Berinato examines how to use charts and diagrams to express ideas and statistics. I agree with Anmol Garg, Tesla data scientist quoted in the article, “You can’t find anything looking at spreadsheets and querying databases. It has to be visual.”
Bernato says: “Convenient is a tempting replacement for good, but it will lead to charts that are merely adequate or, worse, ineffective.” He separates visualizations into four components: idea generation, idea illustration, visual discovery and everyday dataviz. Simple line, bar and pie charts are great for idea generation and illustration, but terrible for visual discovery and dataviz.
Continue Reading "Data Visualizations that Really Work"
Posted by Jay Arthur in QI Macros, Six Sigma, Statistics.
Creating homemade Cp and Cpk templates often results in incorrect values. There are many, many mistakes you might make without realizing. Here are a few examples.
A customer sent me their home grown template for calculating Cp and Cpk and wondered why the QI Macros got such radically different values. It was easy to see from their data that they were using standard deviation, not Sigma estimator (Rbar/d2) to calculate Cp and Cpk. Use Stdev to calculate Pp and Ppk, not Cp and Cpk:
They had run the QI Macros histogram on two columns of data, one measured at 0 degrees and one measured at 90 degrees.
Continue Reading "Cp Cpk Formulas and the Mistakes in Homemade Templates"
Posted by Jay Arthur in Manufacturing, QI Macros, Six Sigma, Statistics.
