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# Tag Archives: kanban

## What Should My WIP Limit Be? Super Easy Method to Find Out!

If you’ve built a kanban system, or you’ve tried to put a WIP (work in progress) limit on a phase in your workflow, you’ve probably asked or been asked this question. And very often, then answer is “I don’t know. How about we try * n*.” where n is a guess. Usually an educated guess like:

- 2 x the number of developers
- 1.5 x the number of people on the team
- Number of people involved + 1

And these are all ok places to start if you have no data, but with a little data, we can stop guessing and set our WIP limits with some empirical information and at the same time start building a system that will satisfy one of the assumptions required for us to use Little’s Law properly. There are two things that we need to have in order to use this super easy method:

- Data about average time in state for work items
- CFD (cumulative flow diagram)

Ok, I guess we don’t *need* the CFD if we have the data, but it sure is nice to visualize this information. š We do need to have some data about the way that work passes through our system and we need the data that would be required to create a CFD. For the purposes of this post, lets assume that we are capture the time in state for each work item. Entire time in the system is often called lead time. Time in between any two phases in the system can be cycle time but we’re interested in cycle times for a single state at a time as our objective is to determine the WIP limits for each column in our kanban system.

Let’s use a simple approach to measuring average time in state in days. On our simple kanban system above, we have a Ready State, Development state and a Done state. Each day, we count the # of items that have cross a state boundary and put those numbers on our CFD chart. After several weeks, we have enough data to start calculating a couple new metrics from our CFD.

With even just a couple weeks of data that visualizes how work moves through our system, we can now start measuring Average Arrival Rate (AAR) and Average Departure Rate (ADR) between any two states in our system. AAR and ADR are simply represented as the slope of a line. If we calculate the rise (x-axis) over the run (y-axis) values, we get the slope.

It is the relationship between the two values that is interesting to us and will allow us to more empirically set the WIP Limit values in the system. Based on our understanding of Littleās Law, we are striving for a average rate of divergence between the two of near 0.

A negative divergence suggests the WIP limit is to low and that we are under utilized.

A positive divergence rate suggests the WIP is too high and we are overburdened.

Since ADR (the rate at which we finish work) represents our current capability, the value of ADR should be considered a great candidate for the WIP limit for this state. With the the right WIP limit in place, AAR should match ADR and we will find an average divergence rate of 0. As your team’s capability changes, our divergence will go either positive or negative and will provide an indication of when our WIP limits should change and what they should change to.

And there you have it! When the rate of divergence between AAR and ADR is near zero, we know that our WIP limit is right and that weāre satisfying one of the assumptions required to make Littleās Law work for us!

## ALM Adoption Success Story – Success Factor 2 – Visualize Your Work

Humans love pictures.

We have numerous sayings in our cultures about how powerful visualizations are.

“**A picture is worth a thousand words**“

**“The drawing shows me at one glance what might be spread over ten pages in a book.”**

**“Every picture tells a story.”**

One that the challenges with knowledge work is that for the most part, most of our work is invisible until it’s done. Only Neo can see bits and bytes which leaves the rest of us a little in the dark. If you’re building a building, or digging a hole in the ground, progress is obvious! For invisible work, progress is very difficult to monitor.

So we track work in some software system, but then we hide all of our knowledge in text and numbers! Our love of spreadsheets is epic in proportion. Status reports! MS Project files! All of this data!! For all intents and purposes, our work is still invisible! We often mistake this data for knowledge and understanding and the true insights are left hidden in the numbers.

On our project, we knew it would be very important to understand at a glance where we were in our workflow.Ā We were doing something very novel for this organization and in order to mitigate the risk of heading too far in the wrong direction (which could be any direction at this point), we needed knowledge. We needed to quickly identify roadblocks to progress, which often included self-inflicted delays. We needed to engage with our sponsor in a meaningful manner.

So we started drawing pictures!

The most important “picture” that we have used has been our storyboard. This board is very important to us. It is a muster point for our daily stand-up meetings. It is a place on which to hang “adornments” that help us quickly assess our current state. It is a tool that promotes conversations. And it is always there, silently reminding us to be disciplined with efforts to visualize our work.

Our storyboard looks like pretty much like any other Agile teams storyboard in form, but this one is a unique work of art. Well, unique in that it is exactly what ** weĀ **need it to be at the moment. It represents our workflow. We have our WIP limits on it. And because we are fairly disciplined in maintaining the current state of all our work, it’s always up to date.

We also visualize our tasks and review these daily as well. TeamPulse has a story board as well as a task board for more granular visualizations.

We donāt have WIP limits enabled on the tasks boards, but again, the intent is to see work flowing through the system. And with an explicit policy that tasks should have a cycle time of 1 day, any time an item is discussed at 2 stand-up meetings, the team knows that the work isnāt progressing as expected and some sort of investigation and corrective action may need to take place.

Finally, we use TeamPulse dashboards to visualize our progress over the course of an iteration.

Everyday, we review the storyboards and the tasks boards during our stand-up, and then we review the dashboards to bring the entire conversation back to our progress versus our (the teamās) expectations for the iteration. These dashboards provide that high-level perspective that ties everything back together.

And all of this information is available on the web, to everyone on the project team, including the sponsor. There is no need to interpret numbers. There isnāt a report that needs to be compiled. And the information we want to present is immediately available in a very easy to consume and impactful format because a picture is worth a thousand words!

Going back to the first ALM Adoption Success Factor of Transparency, having something to be transparent with has been a key enabler of success for our team as we strive to deliver this project and continuously improve and get better as we do it.

**Series List**

- Success Factor 1 ā Transparency
- Success Factor 2 – Visualize Your Work
*Coming soon*– Success Factor 3 – Co-location*Coming soon*– Success Factor 4 – Invest In Your Tools*Coming soon*– Success Factor 5 – Retrospectives

## Little’s Law – It’s not about the numbers

I was recently at the Kanban Leadership Retreat in San Diego, hanging out with a bunch of great people from the community and talking all about Kanban for three days.

This is an un-conference format event so we end up talking about whatever the group is interested in talking about, and one of the best sessions that we had (amongst a group of great sessions) was a session on Little’s Law that was hosted by Dan Vacanti (@danvacanti). Dan has been talking about Little’s Law at conferences for the last few months and the group was really interested in hearing this presentation as well as the interactive questions and answer that would happen throughout. It was during the interactive part of the session that “it” finally clicked for me!

Up until now, I didn’t understand why I would need to use Little’s law. I don’t want to use this blog post to describe Little’s Law and besides, Daniel’s presentation does an excellent job of that.

I’m really dumbing this down but in short (Sorry Dan), Little’s Law provides two things of interest for us Kanban practitioners:

1. allows us to derive one of three important Kanban metrics when any two of them are available to use in the calculation

2. allows us to understand/predict the impact of changes on one of those values to the other two

On the first point, for people running their work in kanban systems, normally all of the values are available if you’re managing your work well enough. Approx. Avg. Lead-time, Avg. Work In Progress, Avg. Throughput are relatively easily tracked or counted, and they can all be seen in a Cumulative Flow Diagram.Ā Given I can observe all three in our system; I don’t need Little’s Law to help with that.

Note* – For planning purposes, we can use this relationship to understand what numbers

we might need in the future. Eg. Calculating required WIP based on an anticipated throughput and a proven capability (lead time) to establish resourcing levels

The second point is really interesting to us but is also where we stumble upon the gotcha and the false knowledge that often occurs on teams when they are using Little’s Law. It was in the interactive session that I finally had my epiphany around this second point! It isn’t (usually) about calculating the missing number or predicting changes in the numbers. It is about ensuring that you ** CAN** use Little’s Law at all!! That’s the tricky part and in my opinion, the really powerful thing I learned at KLRUS.

See, in order to use Little’s Law and have it work as you’d expect, there are a number of **really important assumptions** that have to be true before you can use Little’s Law and it is in making these assumptions trueĀ in our kanban systems that weĀ achieve the true benefit!

The assumptions are (for the period of time that is under observation):

- All measurement units are consistent
- Conservation of Flow which assumes
- Average Arrival Rate == Average Departure Rate
- All work that enters the system flows through to completion and exits

- System is “stable”
- The average age of WIP is neither increasing or decreasing
- The total amount of WIP is roughly the same at the beginning and at the end

**All Measurement Units are Consistent**

This one is relatively easy. If you’re measuring in weeks for Throughput, your Lead time should be in weeks. If your WIP is a User Story, your throughput is in User Story. We can’t have WIP as User story and Throughput as Tasks. We can’t have Throughput as User Story/week and Lead Time as User Story/day.

**Average Arrival Rate (AAR) == Average Departure Rate (ADR)**

This one is a bit trickier, but not too bad. One of the assumptions required, as described by Dr. Little, is Conservation of Flow and this metric is one of the ways that we measure this in Kanban. And the tactic we use to manipulate this situation is our WIP limits. If we only accept new work into our system at the same rate that work leaves our system, we are stable enough for Little’s Law to work.

**All Work That Enters the System Leaves the System**

Work that enters our system and doesn’t exit in a predictable fashion will disrupt the Conservation of Flow. AAR and ADR can’t be equal if this happens frequently in the system. We do have to watch out for work being “created” within the system that could mask abnormal termination of work within the system.

**Average Age of WIP is Constant**

Littleās Law also requires a āStable Systemā, which Iām not going to explore deeply in this article. One of the two metrics that we can use from our kanban system though to determine if our system is stable is the average age of WIP in the system. Over the period of time being observed, we want this average to be constant.

**Total WIP at Beginning and End of Time Period are the Same**

The second metric we use to determine system stability is WIP totals at the beginning and end of the time period under observation. If our WIP is trending up or down, we do not have a stable system and predictions that we get from Littleās Law will be suspect.

**So what does this all meanā¦**

So we have a law that we want to use, but in order to use it, we have to fulfill the required assumptions for the law to work. The required assumptions are going to cause us to create specific and explicit policies that will describe how our teams behave. The effects of these policies are varied, but you will see improvements in prioritization and pulling of work, queuing, swarming, workflow/system design, and WIP limits just to name a few in order to effectively strive to satisfy the assumptions.

**In Conclusion**

The power of Little’s Law to Kanban teams is *not** *its ability to predict WIP, Thoughput or Leadtime. The true power lies in its ability to influence team behavior with its underlying assumptions.

When Littleās Law is properly understood by Kanban teams, it will encourage those teams to strive to satisfy the Lawās assumptions which will result in improved predictability and a kanban system that can be “tweaked” to achieve whatever goal is required at the time.

## Kanban Has Changed The Way I Think (or Creating Business Value out of Creative Queue Uses)

Queues are all around us. Policies are everywhere.

Can we use the two together in a simple fashion to create business value?

Note* – Once you get really interested in Kanban, these are the kinds of observations and questions

that just randomly pop into your head, so beware. š

I was sitting on a plane and luckily had a window seat and I was watching the baggage handlers loading the luggage onto the plane. They were counting the bags and starting to load them onto the plane and I looked away for a bit. After a few minutes, I looked back and notice they were almost done and that is when I noticed the priority baggage tag on the last bags being loaded.Ā **This is where kanban took over my brain.**

Could a simple policy that was implemented by baggage handlers and a Last In ā First Out (LIFO) queuing technique manage the priority baggage solution? It seemed to me that this would be a simple tactic to provide a value-add service to customers that they might pay for.

Iām sure it would be a little bit more involved than that (bag check tagging bags, people loading the conveyer belts that take luggage the last mile would need to use a FIFO approach) but basically with a simple policy change (it would have to be an explicit policy) and an understanding of how queues work, the airlines have created business value out of thin air!

Are there any simple policies or queues that you could use to create business value for your organization?