Lead time distribution charts are very useful for making improvements to your system of work. Understanding what to target and when will ensure that you get the best “bang for your buck” in improvements. Making these improvement will help you stablise your system of work and improve predictability which will open the door for the next level of possibilities for your organisation.

Here’s an example of a lead time distribution chart:

To recap what your looking at here (for more info, please refer to https://evogility.com.au/lead-time-distribution-charts/):

  • X-axis: Number of days from when something was committed to, to when it was completed (ie the Lead Time). Note that in the above they are recorded at 5 day increments. So, the value for 10, represents the number of items greater than the last measure (5) and 10.
  • Y-Axis: Count of the number of items that fall within that range. The number at the top of each column represents the total count for that item.

Lets look at how you can use these to make improvements.

Long Tail

You can see there are a number of items coming through at the 70-85 day mark. We need to be genuinely curious about what is going on here. There may be a couple of things going on here:

Different class of service – Perhaps these are items with a low cost of delay that are continually getting pushed back. Perhaps it’s worth capturing this in a different lead time chart so that you understand the lead times for each class of service independently.

Different work item type – Similar to class of service, maybe this is the lead time for a certain work item type – it may be naturally longer than the others. Again, you might want to separate those out into their own chart and create different lead time expectations and control WiP for them.

Refutable demand – Looking at these items, is there a way that they can be identified and not accepted in your system? Is their cost of delay valuable enough or is there some other organisational imperative that is pushing this into your system needlessly. Keeping out undesirable work and not “auto-commiting” to every piece gives you better ability to manage your system.

External dependencies – This is often one of the most common causes for delays – a dependency external to your control blocks these work items. Is there a way you can remove the dependency (perhaps you can upskill one or more of your team to do this work)? If you can’t do this, then is there a better way to identify the dependency early and coordinate with that group to avoid the delays?

Once you get rid of that outside tail element, the end of the tail shifts, and we’re now looking at the 30-50 day range. All of the above are still applicable. With a larger volume of items, you might want to also try:

Blocker Clustering – There might be multiple different causes for blockers that you want to explore and prioritise for removal / reduction. See this article by Klaus Leopold and Troy Magennis to learn more on blocker clustering.

Once you’ve gotten through these things, congratulations, you’ve now “trimmed the tail”, let’s look at what else you can do.

Simple Requests

Look at the left hand side of the chart. The bulk of the requests are in the 10 day or less mark. There may be something you can do here:

Automation – There are lots of these items and they are fast, perhaps they don’t necessarily need a lot of brain power to actually get done. A subset of these you may be able to remove from your request queue through automating the process. Try to categorise these requests and look at what your automation options are for these.

Avoidance – Avoidance of these requests might also be possible. A great example of that is an IT service desk that put together a process for people to automatically, or through their manager, reset their own password. This reduces a great deal of calls to the service desk and frees up time for more important requests.

Work Item Type – As we saw in the XIT case study in our Kanban System Design course, you can often identify a particular work item type and negotiate it out of the system.

All of the options above free up capacity for your team to do more “knowledge work”. That is, the kind of work that is human intensive and valuable to customers. These improvements will help you “trim the tail” and make it more predictable.

If you want to learn more, become a Kanban Management Professional (KMP) by completing these courses:

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Work item ageing charts are particularly useful for solving issues that are currently impacting your teams. Whilst other charts such as Lead Time Distributions and Scatterplots can be useful for coming up with countermeasures for issues that you experience in the past, Work Item Ageing charts are useful for the present issues.

Lets look at an example below:

To break it down:

  • X-Axis – This is the date that the item was started
  • Y-Axis – This is how old the ticket is (how many days since it was committed to)

Each dot represents a work item that is currently in progress.

For the purpose of this post, lets assume that our lead time expectation with customers is 90% within 50 days. We can see one work item approaching 80 days! This is something that should have been actioned earlier, but without a chart like this we may not see those things. This will likely need some customer alerts if that hasn’t already happened and some work to regain our customers trust. Although it falls within our 10%, it has far exceeded the expectation and needs rapid action.

The two items approaching 40 days also need action as our lead time is fast approaching. This case is not as dire as we are still within expectations, but as a leader you will need to offer support to remove blockers, plus you might also want to think about other ways to get this done. For example, you might need to increase their class of service to get the team swarming on these to make the date.

The item that’s at 25 days is not necessarily a problem yet, but may be a problem in the not too distant future. Keep an eye on this, but no need for anything drastic yet.

The Work Item Ageing chart is really useful for helping you see issues that are in your system right now. You may need to create policies to ensure that your lead time and other customer expectations are being met. It can also be useful for ensuring that you have a stable and more predictable system to benefit your customers.

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These are not very widely used, but are one of my favourite charts for helping to manage flow. I find it quite simple as well, which once you finish this article I hope you also find it equally simple and useful.

When managing flow you often want to make sure you team are not taking in more work they can handle, plus you also want to make sure they don’t “starve” for work. Continually looking for balance can often seem difficult, but using this chart you can help achieve the balance your looking for.

Here’s a look at a net flow chart:

Essentially, the green points above the line mean that for that week you’ve gotten more items done that what you’ve started. The red points below the line represent weeks that you’ve started more items than you’ve completed. Those weeks that don’t have a line in them (with a zero) are weeks where you’ve balanced input and output (well done!).

Where you see build ups of several weeks of the one colour, that’s usually a warning sign that you’re flow is out of balance.

You can see in the early part of this chart there is a lot of red. That indicates that WiP is increasing and is often an indicator that you need to start to choke the input to your system or it will be overwhelmed. However, there is often an exception to this where teams are bootstrapping / starting out – you want to start to pull work into the system and it will take some time before the first item flows out.

Alternatively, if you get a number of weeks of all green it means that you’re team are finishing a more work than they started. This is an indicator that you need to get out and start some more work. In the event that you have an upstream, you may have a lead time before that work comes to your system. It’s important to see these signals early and can respond to it.

In first diagram above, you can see in the middle there were a period of time where it was back and forth. Then towards the end you can see balance starting to be achieved. This is an important part of how you use this chart – often with a large board and different work items types / classes of service, it’s hard to see important details of this without the data representation. I hope that you can now you can see the value in this chart and can try and use it.

It’s freely available here if you wanted to download it:

http://bit.ly/SimResources

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One of the really useful charts that I commonly use with Kanban implementations are lead time distribution charts. Given that lead time is one of the key metrics, at least from a customers perspective, understanding the nature of lead times for your Kanban system is a must for ensuring a more fit-for-purpose and customer focused approach. Understanding how to read these charts and take action to achieve that kind of outcome is an essential skill for any kanban practitioner.

One of the key points about lead time is hinted at in the above title – it’s a distribution, not a single number. If you take nothing else away from this article except this point, it will put you in good stead to start to make improvements. Too often, I hear about people talking about “when it will be done” and referring to a single number, like an average. This is inherently disadvantageous, because it denies the real nature of lead times, especially in knowledge work.

No doubt, you’ve seen before a lot of variation in the knowledge work you’re involved with. Items are not all the same size or complexity, plus you have to wait for others which causes delays which are often unpredictable. These kinds of things lead me to prefer to acknowledge the variation in lead times we see, rather than ignore them.

Here’s an example of a lead distribution chart (also known as a histogram):

Here is an explanation of what you’re seeing:

  • X-axis: Number of days from when something was committed to, to when it was completed (ie the Lead Time). Not that in the above they are recorded at 5 day increments. So, the value for 10, represents the number of items greater than the last measure (5) and 10.
  • Y-Axis: Count of the number of items that fall within that range. The number at the top of each column represents the total count for that item.

You can see from the above chart that a significant number of the overall items fall within the 5 & 10 day counts. 43 out of the 78 items (over half) fall within this range. Clearly the majority of work falls within this range, but would you feel confident giving a commitment based on just a 55% hit rate? Of course not, because as we can see, a large number of items are still counted up to 25 days. A little over 80% of items (for the purpose of this, we’ll round up to 85%) take up to 25 days. This is a little better, and you may feel comfortable in giving a commitment based on this, but keep in mind that its up to 2.5 times longer than our earlier sample. Had we given a commitment based on what we thought was more commonly occurring lead time (which is often where our biases may take us) we might be facing considerable customer disappointment and the consequences that go with that.

There’s also a section between 30-50 days, not a great deal of items, but you can see that there is a not insignificant chance that we might land an item this late (about 10% of items). Again, this is 5 times what our first sample told us. In other words, 1 in 10 customers would have to wait 50 days instead of the 10 days if we had promised that. How would you feel as that customer?

Lastly, there are our outliers from 70-85 days. Outliers are a normal part of your system – you shouldn’t dismiss them out of hand. In our case, they’re about 5% of the total, or 1 in 20 requests.

When I look at the actual data behind this chart, the average lead time is approximately 16 days. Perhaps instead of jumping straight to commitments of 10 days, or even 16 days, we might like to take the full distribution into account. Instead, how when discussing time commitments, perhaps acknowledge the distribution with something along the lines of:

“Over half our requests are finished in 10 days or less, the average being 16 days, with 95% of all requests being completed in 50 days.”

This is a better discussion and uncovers the real nature of the work and will lead you to start to make better commitments that are more reliable.

Future posts will cover how to use the Lead Time Distribution Chart for making improvements.

The lead time chart was produced by the freely available FocusedObjective spreadsheets downloadable from: http://bit.ly/SimResources

Cumulative Flow diagrams can contain quite a lot of information for observations. However, they can also be a little daunting for those who are not familiar with them. In this blog post I’ll share with you some tips and information for how to start to use cumulative flow diagrams.

Here’s an example of a CFD:

Each line is a count of the number of tickets moving through that part of the process – stacked on top of one another, hence the name “cumulative”.

  • Light Blue – Done items
  • Yellow – Deploying
  • Grey – Testing
  • Orange – Developing
  • Dark Blue – Analysing

Along the x-axis are the days – it’s quite common for CFDs to use days as the point of measurement.

Along the y-axis is the count of the tickets. For example, on day 0 there were no tickets in the Done area, whereas on day 1 there were now 3.

That’s actually one key part of being able to read these kinds of graphs. The distance in height between the two lines represents the number of tickets. Thus the larger the gap between the lines, the more tickets that are in that part of the process. Therefore, the further apart the lines the greater the Work in Process (WiP).

If you get lines that are continuing to grow apart, the you have a WiP growth problem and this is going to slow down your overall throughput. This is particularly useful if you use the top and bottom lines when viewing WiP growth because these represent the input and output of your overall system. If you’re not getting growth in you system, but lines within it a growing, it means that there’s likely a bottleneck inside the system.

Additionally, where these lines start to converge, it means you have decreasing WiP and may lead to starvation of a part / all of the system. This might not be a problem in the immediate short term, but left unchecked it may lead to an unnecessary spend / costs.

Using these observations, you can start to adjust WiP limits and control the point of input to either prevent extra work coming into the system / parts of the system, or alternatively allowing more work to flow through. Effectively, this is what managing flow is about – trying to match variations in demand and capability to achieve a balance.

Another key observation about CFDs is that the average lead time is denoted by the width of the graph. For example, if you look at the above graph at the dark blue mark on day 1 – this is when item 11 entered the system. If you track horizontally across the chart, you’ll see that this item left the system (the light blue line) on day 7. To determine the system lead time, you can deduct the entry time from the exit time giving you a lead time of 6 days.

Of course, this time will vary as things change in the system. If you look further up the chart to day 7 and the analysis count there and then track across to done, you’ll notice the lead time is down to approximately 4 days. That’s about a 30% reduction in lead time and can often be attributed to actions such as limiting WiP in the system (as Little’s law tells us that decreasing WiP can also lead to decreased lead time) or other improvements to the system.

The third key attribute of this is the angle of the bottom line of the graph. This is known as the delivery rate or throughput of the Kanban system (as an average). The flatter the angle, the less the throughput. The greater the angle (moving towards vertical), the greater the delivery rate / throughput.

You can see these three key attributes in this chart:

You don’t need to always have electronic tools – if you have physical boards you can maintain a physical chart with this data. Alternatively, you can capture this in a spreadsheet. There are also other tools out there such as SwiftKanban, Kanbanize and LeanKit that do these things for you as well.

You can now start to use cumulative flow diagrams to help make more informed decisions about your system of work. You can see bottlenecks that you can action, you can start to limit or increase input to balance flow and you can limit work through the process to help smooth the flow. You can also start to make plans around how to reduce lead time or increase throughput which will have positive impact to your customers and your financial bottom line.

The cumulative flow diagram is an essential tool for managing flow and improving the outcomes of your service delivery. If you aren’t using one, you now have the basic knowledge to get started.

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Lead time scatterplots are an important tool for a team working with Kanban. Sometimes these are referred to as a “Control Chart” which is often the case in manufacturing. However, in knowledge work I think it’s less about the “control” mechanism and more about using it to improve the capability of the term providing the services. Thus I usually just refer to these as “scatterplots” and if you look at the example below you can see why.

There’s quite a bit going on here, so lets look at each part in turn.

X-Axis – This contains a series dates, you can see they progress over about an 8-9 month period moving from left to right chronologically.

Y-Axis – Time in calendar days. This is the amount of time a work item took when it was completed. This is calendar days, so it includes weekends and public holidays.

Orange dots – These represent Standard class of service items. Each time an item is completed, an orange dot is added at the date of completion and the total lead time for the item.

Grey triangles – These represent Fixed date class of service items. Similarly to the orange dots, they represent when an item is completed and the lead time to completion.

Orange dotted horizontal line – This represents the 95th percentile mark for Standard class of service items. That is, 95% of the work items fall under the line and 5% above. This line is optional, plus you can add lines at different percentile’s as per your needs (eg 85%).

Grey dotted horizontal line – This represents the 95th percentile mark for Fixed date class of service items.

You may notice a few things:

  • Lead times are not necessarily uniform. This is often the case in knowledge work – thinking everything is exactly the same is a common misconception
  • Sometimes there are gaps – there may be holiday periods where there is less work going on. You should understand this as being part of the normal cycle / capability of your team.
  • Some items take a large amount of time – Often items that are blocked or have external dependencies cause work items to have their lead time blow out.
  • There are much fewer Fixed date items than standard.
  • Fixed date items have a much lower lead time

Things to look out for

  • Growing lead times – this would tend to indicate an underlying problem in the system that you need to attend to
  • Where batches are completed together – there may be some bottleneck in the process preventing things from getting done, or the items might not be independent – look at how you slice for independence.
  • In progress items – this only shows you the “Done” items, it doesn’t show you the ageing of currently open items. You can use a separate chart for this – it’s important to action problems early before it’s too late!

You don’t need a whole lot of data for this to get started. All you need is the start date and end date of each of your work items. A few years ago I did a lightnight talk at Agile Australia 2017 that talks about this. If you’re looking for a tool to help you do this, you should check out Troy Magennis’ “Throughput and Cycle Time Calculator” excel sheet in his Focused Objectives repository.

It’s good to understand the basics about how these scatterplots work. I’ve touched on some of the ways they can be used, but there’s more detail to it than that and it requires a few other charts, so I’ll save this for a future blog post.

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