Monday Night Football

I was watching Monday Night Football last night, seeing the Dolphins break my heart as they have so many times over the years, when a thought occurred to me. This new so-called "wildcat" offensive formation is really Lean Football. In a running play the quarterback is actually complete waste. By direct snapping the ball to the running back, you've eliminated a huge non-value-added step from the process. This is a great example of how lean thinking can create the sort of revolutionary changes that can take your business (or sport) to the next level.

So I decided to value stream map it. The current state is the conventional approach. (Click to enlarge.)

In this model you have 3 "operators", and if you consider going down the field as the only value-added activity, then snapping the ball and handing it off are both NVA. So if you try to put some cycle time numbers to these activities (and I'm sure mine are way off) you get about 38% NVA cycle time. This would be great for a manufacturing process, but probably not so good in sports.

Additionally, each process has a yield. I figure snaps and handoffs go right most of the time, and a running play nets an acceptable positive gain about half the time. You can use whatever numbers you want, but if you use mine you get a rolled throughput yield of about 49%.

Now let's look at the future state, the wildcat.

The first thing you notice is that you've reduced headcount by 1. Of course there are still 11 men on the field, but now you have an extra blocker in the game, someone directly supporting the running back's value-added activity. I like to think of blockers as water skippers, making sure things are set up and there are no obstacles to work.

Next you see that the percentage of NVA work is reduced to about 17%, a pretty big improvement in productivity.

And finally you see that by eliminating a step (or combining steps) you can actually improve the RTY. Of course this assumes that long snapping and snapping under center have equal "yields". But it also assumes you'll have no greater chance for running success by using this approach.

You can certainly argue with my numbers, and you can even argue with the overall effectiveness of the wildcat (Miami did lose), and you can site all of the myriad other factors that go into success on the gridiron. But the point is that in any process, be it making furniture, serving customers at the DMV, or even playing football, you can make hugh improvements by finding and eliminating waste.

Am I Losing Weight? (part 4)

I promise this will be the last post in this series. I needed to understand the reason(s) for the big single-day weight swings that were foiling my attempts to slim down. So I decided to peel back one more layer of the onion and look at what days of the week these were occurring on. I plotted weight change by day and included day of the week.

The data show that most of the big swings (up and down) occur on Mondays. I weigh myself first thing in the morning, so each day really represents the results of the previous day. In other words, these big changes are actually happening on Sundays. This isn't completely surprising since my eating and activity patterns on the weekends are not very consistent. (If you're wondering if there's a correlation with the days or distances I run, there isn't. I checked.)

So now, through careful data analysis, I've gone from simple frustration over not achieving my goal to a very specific thing I need to work on. Weekend nutrition habits. I've taken the huge mass of inputs to the process, funneled them down, and arrived at the one big knob I can turn for maximum impact. Six Sigma saves the day again!

Am I Losing Weight? (part 3)

So I'm looking at my recent weight data. (See previous posts to catch up.) And statistically it appears that I am not losing weight at all. But are the data truly random, or are there patterns there that might indicate important information? A good tool for finding patterns is a run chart, so I used one.

Now this I found to be very interesting. I might have hoped to see trending, but there is no indication of it. However, what is clear from the small number of runs about the mean is that there is clustering. If you think about this in context, what it means is that I get a big jump in weight, it stays there awhile, and then there's a rapid decrease, it stays there awhile, and so on. I would not have expected to see this.

This prompted me to look at the average daily change in weight. This turns out to be -0.08 pounds, further evidence that I'm not losing weight. However, when I look at the absolute value of these data, the distribution looks like this.

Here we see that the data are not normal. The average is just under 1 pound, but there are several outliers stretching up to as much as 3 pounds. So what is the reason for these unusually high single day swings (both up and down) that are impacting my weight loss effort? Surely nothing more can be gleaned from mining the data. Or can it? Stay tuned...

Am I Losing Weight? (part 2)

Last week I shared some data regarding my recent efforts to lose weight. Now I'd like to delve a bit deeper into these data and try to devine some meaning from them. Of course no process can begin to be understood without first understanding whether or not it is stable and in control. So falling back on my "everything is a process" credo, I first looked at normality...

And then at SPC...

It seems that I am normal and in control. In my case however, this is not such a good thing. I'd really like to see the data skewed and trending downward. This analysis would seem to suggest that I am not losing weight at all; that the data are completely random.
Now I could stop here and simply admit defeat, go back to the drawing board, and start eating less. But neither my determination to lose weight nor my love for statistical analysis will allow that...

Am I Losing Weight? (part 1)

I recently dedicated myself to losing 5 pounds in a month. I didn't quite make it. In fact I fell way short, and I'm continuing the battle. So now I'm trying to figure out why I failed to achieve my goal. So naturally the first thing I did was go to the data. The chart below shows my weight, as taken at the same time every day, over the last month.

So what can we tell from this? How am I doing? Am I improving? And what, if anything, does the data tell us about why I did not reach my goal?