Tag: Wheeler

It's been a busy few weeks for me with work travel and, blogwise, I'm winding down into a few weeks of "blog vacation" (which...

I really enjoyed the HBO miniseries "Chernobyl" that aired/streamed recently. You can watch it all now through HBO if you have access (or you...

Jamie Flinchbaugh and I got a great response to the first episode of our new "Lean Whiskey" podcast. We've had a great time with...

We have to be careful with text descriptions of business metrics -- this is true when it's numbers in the news or performance measures...

Skip Steward, the Chief Improvement Officer at Baptist Memorial Health Care in Tennessee, was a guest on Episode #314 of the podcast talking about TWI and Toyota Kata in healthcare (he was joined by Brandon Brown). Today, I've asked Skip to come back and chat 1x1, in Episode #320, about his experience with Don Wheeler, learning from W. Edwards Deming, and more. I hope you enjoy his reflections, our discussions about healthcare, and connections to my book Measures of Success (Skip undoubtedly has a book in him too). 

If you're not a baseball fan, I apologize for a second baseball-themed post this week. Baseball has a lot of historical time-series data to work with and analyze. If you're not interested in "Process Behavior Charts" or similar methods, I guess I will apologize for this post again too. Next, I'll be apologizing for apologizing. But, I am at Dr. Wheeler's four-day workshop on "Understanding Statistical Process Control" as I blogged about on Monday. It's great to learn from Dr. Wheeler in person and I'll be sharing reflections on the class in a later post (and I'm posting a few things on LinkedIn along the way). In Chapter 1 of Understanding Variation, Dr. Wheeler points out how charts or graphs are far superior to tables or lists of numbers. He uses a baseball example:

I recently saw this headline: Baseball on pace for lowest batting average since 1972 Just because it's the lowest average in 48 years... it doesn't mean that this year's MLB-wide batting average is low in a way that's statistically meaningful.

For a long time, I've wanted to attend a "Statistical Process Control" (SPC) class taught by Donald J. Wheeler, Ph.D.  I'm finally doing this in September and I'd like to invite you to join me.

As I blogged about yesterday, things went well at the Oscars... or, at least, no errors were made in the announcements. But that thing that didn't go well was the TV ratings. Two Data Points Are Not a Trend The headlines I saw had a lot of two-data-point comparisons. Headlines sometimes gave the percentage decrease in viewers or how many million fewer viewers there were. Many talked about "record low" but if you're tracking a metric "record low" or "all-time high" doesn't mean there's a "special cause." That "record low" could still be noise in the system.

When looking at data, whether it's the number of homicides in San Antonio or some of our workplace metrics, it's tempting to overreact to a "headline" that says a measure went down in 2017 compared to 2016. But do two data points really help us understand what the real trend is? This blog post explores an approach we can use to make better decisions about our data.

  So many of our business metrics are reported in terms of two-data point comparisons. How much did the metric change from last week? Web...

Here's another post on the theme of performance metrics, as I'm talking about next week at Lean Startup Week (see yesterday's post or others...