Although I often preach about embracing uncertainty and sometimes get comments about always being calm… despite that; I worry. As do we all.

But sometimes, in rare moments of clarity, I have the opportunity to stop and reflect over the what I am worried about. It just about always brings me to the realization that I worry in vain.

Let me share three things in particular, that I have worried about lately. That gave me nothing but more worry.

UPDATE I have learned new stuff. There are a better ways. Find the update here

It’s time to wrap this series up. I have one final thing that I want to visualize: queue length. How much stuff is waiting and how long will that take us to complete? And maybe even, “if I add something in the queue now, how long before it’s done?”

1. Lead time
2. Lead time with filters
3. Throughput
4. Where time is spent
5. Single numbers - averages, median and max of lead time

As always my sheet is found here and you can make a copy of it and use it. Please let me know how it’s working out for you and if you end up doing something cooler than me.

Let’s do it - queue length!

I stumbled over a new concept the other day. As it was conceived by Kent Beck, that inspired and thought me a lot in the past, I got interesting.

I have updated this to modern tools here

[UPDATED]

I read Kent’s blog post a bit too fast and missed that this idea was actually proposed by Oddmund Strømmer. Very sorry that I missed that in my writeup, Oddmund. Thanks for correcting me, Raquel.

And after some even more research the origins seems to be traced back to a group of people that took a workshop with Kent Beck. Not only Oddmund Strømmer but also Lars Barlindhaug and Ole Tjensvoll Johannessen. Those Norwegians… always a few steps ahead of me.

[BACK TO THE OLD TEXT]

When I read his blog post I got to this quote:

I hated the idea so I had to try...

UPDATE I have learned new stuff. There are a better ways. Find the update here

This post is all about just aggregating and averaging out to a single number… and then I can’t control myself but start to lay that number out over the individual weeks too.

That, and we will use the Gauge-chart for the first time in my life.

This is the fifth post in my series on some simple kanban board statistics. We have been talking about:

1. Lead time
2. Lead time with filters
3. Throughput
4. Where time is spent

This is the fourth post in my series on some simple kanban board statistics. We have been talking about:

• Lead time
• Lead time with filters
• Throughput

And this time it’s time to see if we can visualize a bit where time is spent. For this first post, we will make some basic classifications of active and not active or not “on the board” and “on the board”.

In the coming posts I want to expand on this and see if we can make a distinction between other states on the board as well, but for that, we need to expand the data in “Raw data”, because that data only contains completed items right now.

Ok - let’s get going. As for all these post I am in this Google Sheet - make a copy if you want to play along

UPDATE I have learned new stuff. There are a better ways. Find the update here

Lead time is awesome to track and try to improve. In fact, it’s something that will guide you to a lot of improvements and should be front-and-centre of your process metrics.

But that says very little about how much that gets done per time unit. Doing one thing in a day, fast and with quality, and then nothing for the rest of the month means that no other things get done.

Let’s start to track another metric; throughput or

the amount of material or items passing through a system or process.

With the data, we have this is pretty easy to get hold of.

UPDATE I have learned new stuff. There are a better ways. Find the update here

This is the second post in my series where I show how you can get make powerful visualizations of process data. As before, my goal here is that you can dump your process data into one tab of my sheet and then the dashboard will make all the other calculations.

In the first post, I talked at some length about other goals of this tool and some of the principles I built these ideas on.

Speaking of those principles; in this post, I will violate one of them a bit, by adding a filter capability to the lead time chart, so that we can see just a part of the data.

UPDATE I have learned new stuff. There are a better ways. Find the update here

I have been coaching agile teams for about 15 years now. One thing that I often help teams that I coach is to tap their process of some valuable data. It turns out that many of the tools that we are using have a lot of data in them that we seldom look at and even more seldom act on.

Most of these tools (like JIRA or Team Foundation server) obviously have ways of looking that this data too, but I’ve found that it’s either really hard to understand the visualizations or that the reports that you can produce simply don’t cut along the right axis.

I’ve now grown tired of recreating these simple reports for every client and wanted to share my, very simple, stats here. This way I can reuse it for...

At </salt>, a boot camp that I’ve been part of setting up, I get to try all kind of different things that I haven’t done before. Mostly around configuring, management and supporting the students computers and our code.

Just last week we had the need for a simple way to do a copy of our GitHub repositories. I did some research and found a simple way that I’ve put together in a script. I wanted to share it here.

When I introduce agile I do that through a nice little quadrant originally from the This is Lean book by Pär Åhlström and Niclas Modig, and visualized by Håkan Forss. I’ve wrote about it here. This post will only focus on the top left triangle - where we focus on maximizing resource utilization.

But I’ve noticed that personal stories sticks better and I have used a story about my dentist to show an example of a setting that focuses heavily on the resource utilization.

I lately was called back to a checkup at the dentist and did some further research. It was a fascinating peek into a world where many people was working hard, smart and diligent to achieve an outcome that was not any good for me as an end customer (aka the wrong thing, in my book).

I wanted to share this story...