This is post 6 of 9 in our Little's Law series.
As we discussed in the previous post, a thorough understanding of what it means to violate each of the assumptions of Little's Law (LL) is key to the optimization of your delivery process. So let's take a minute to walk through each of those in a bit more detail.
The first thing to observe about the assumptions is that #1 and #3 are logically equivalent. I'm not sure why Dr. Little calls these out separately because I've never seen a case where one is fulfilled but the other is not. Therefore, I think we can safely treat those two as the same. But more importantly, you'll notice what Little is not saying here with either #1 or #3. He is making no judgment about the actual amount of WIP that is required to be in the system. He says nothing of less WIP being better or more WIP being worse. In fact, Little couldn't care less. All he cares about is that WIP is stable over time. So while having arrivals match departures (and thus unchanging WIP over time) is important, that tells us *nothing* about whether we have too much WIP, too little WIP, or just the right amount of WIP. Assumptions #1 and #3, therefore, while important, can be ruled out as *the* most important.
Assumption #2 is one that is frequently ignored. In your work, how often do you start something but never complete it? My guess is the number of times that has happened to you over the past few months is something greater than zero. Even so, while this assumption is again of crucial importance, it is usually the exception rather than the rule. Unless you find yourself in a context where you are always abandoning more work than you complete (in which case you have much bigger problems than LL), this assumption will also not be the dominant reason why you have a suboptimal workflow.
This leaves us with assumption #4. Allowing items to age arbitrarily is the single greatest factor as to why you are not efficient, effective, or predictable at delivering customer value. Stated a different way, if you plan to adopt the use of flow metrics, the single most important aspect that you should be paying attention to is not letting work items age unnecessarily!
More than limiting WIP, more than visualizing work, more than finding bottlenecks (which is not necessarily a flow thing anyway), the only question to ask of your flow system is, "Are you letting items age needlessly?" Get aging right and most of the rest of predictability takes care of itself. As this is a blog series about Little's Law, getting into the specifics of how to manage item aging is a bit beyond our remit, but thankfully Julia Wester has done an excellent job of giving us an intro to how you might use ActionableAgile Analytics for this goal.
To me, one of the strangest results in all of flow theory is that the most important metric to measure is not really stated in any equation (much less Little's Law). While I always had an intuition that aging was important, I never really understood its relevance. It wasn't until I went back and read the original proofs and subsequent articles by Little and others that I grasped its significance. You'll note that other than the Kanban Guide, all other flow-based frameworks do not even mention work item aging at all. Kinda makes you wonder, doesn't it?
Having now explored the real reasons to understand Little's Law (e.g., pay attention to aging and understand all the assumptions), let's now turn our attention to some ways in which Little's Law should NOT be used.
Explore all entries in this series
About Daniel Vacanti, Guest Writer
Daniel Vacanti is the author of the highly-praised books "When will it be done?" and "Actionable Agile Metrics for Predictability" and the original mind behind the ActionableAgile™️ Analytics Tool. Recently, he co-founded ProKanban.org, an inclusive community where everyone can learn about Professional Kanban, and he co-authored their Kanban Guide.
When he is not playing tennis in the Florida sunshine or whisky tasting in Scotland, Daniel can be found speaking on the international conference circuit, teaching classes, and creating amazing content for people like us.
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