The Ball Flow Game

I was invited to the Scrum Gathering in Amsterdam this week to give a Deep Dive on Kanban. My Kanban Exploration slides can be downloaded from slideshare. Inspired by an email discussion with Jean Tabaka and Eric Willeke, to introduce the session, and to try and reinforce the concepts of Flow, Value and Capability, I tried a variation of the Ball Point Game that is commonly used in Scrum training.

Here’s a couple of links (Kane Mar) (Declan Whelan) if you’re not familiar with the game. In a nutshell it involves a group working as a team to pass balls between themselves, constrained by some rules. The idea is to pass as many balls in a 2 minute time box. The team has to self organise and inspect and adapt in order to improve its velocity (throughput of balls).

For my variation I wanted to remove the time-box to emphasise flow more, and demonstrate a different way of understanding the capability of a system. In the game, the team are designing a system to meet the purpose of flowing balls quickly between themselves.

The changes I made were to ask the team to pass 20 balls as quickly as possible. I put a unique number (1 – 20) on each ball in case it was useful and also asked the team to time how long it took for each ball to pass through the system.  I took the data that was captured and entered it into a spreadsheet to create a control chart. We ran two rounds of the game twice, with the respective charts below.

Round 1

In Round 1, the team didn’t capture all the data, and some problems were had towards the end, but that the average time for each ball was 13 seconds. The system could also be said to be ‘in control’ as all the data points were with the control limits  which were calculated as AVERAGE +/- (3 * STDEV). The last measured ball was completed at 3 minutes and 35 seconds.

Round 2

In round 2, the team improved their data capture process and overall flow. The average time per ball dropped to 12 seconds and the variability also reduced. The Upper Control Limit dropped from 01:10 to 00:18. The last measured ball was completed at 2 minutes and 22 seconds.

What this demonstrates is that even with variability (which we don’t want to eliminate completely in software product development), by understanding the capability of the system over time, we are able to reliably communicate what might and might not be possible. For example, using the round 2 data, there is a 50% chance we’ll complete a ball in 12 seconds and a 99% chance we’ll complete a ball in 18 seconds.

We could also calculate and chart the throughput of balls completed over a cadence of 30 seconds to similarly understand the capability from that perspective also. For Round 2 those throughputs would have been 3, 4, 4, 5, 4.

There are a few areas I’d change next time I try this.

1. The measurement took a long time and was clearly the significant bottleneck. I made measurement part of the system to add some additional complexity, but in hindsight it was probably too much. Most of the improvements were in measuring the system rather than the performance of the system.
2. I allowed more time than I probably should have for improvement discussions. With the time-boxed version its easier to start the clock for a round and that usually that kicks the team into action. Similarly, when the measurement fell apart we stopped and restarted a couple of times. I wouldn’t do that next time, although by removing measurement from the system, it might be less of a problem.
3. It took time to enter the data into the spreadsheet. I need to find a better way! The spreadsheet can be found here. It’s very simplistic. Please let me know if you use it and improve it!