Tag: Scatter Plot

Are You Just an Average CFD User?

“In everyday life, the Flaw of Averages ensures that plans based on average customer demand, average completion time, average interest rate, and other uncertainties are below projection, behind schedule, and beyond budget.” – Sam L. Savage, 2009 – The Flaw of Averages

“Variation is the hard reality, not a set of imperfect measures for a central tendency. Means and medians are the abstractions.”  – Stephen Jay Gould, 1985 – “The Median Isn’t the Message”

“Essentially, all models are wrong, but some are useful” – George E. P. Box, “Empirical Model Building and Response Surfaces”, (1919 – 2013)

“Remember that a model is not the truth. It is a lie to help you get your point across.” – Sam L. Savage, “The Flaw of Averages”, 2009

“You are allowed to lie a little, but you must never mislead.” – Paul Halmos, mathematician, (1916 – 2006)


Last week I read an article about yet another tool showing the ability to produce CFDs (cumulative flow diagrams). Maybe you’re already a user of one of these tools that help you visualize your workflow, and generate them for you automatically (or “auto-magically”) as part of reports they provide. Or, perhaps like me, you still generate them mostly using MS-Excel. Either way, have you wondered just a little about how a CFD works?

As most do, this article displayed a line extending vertically between “stages” on the CFD (or workflow processes, as I often call them) and identified this distance as the WIP (work-in-progress) on a specific date for the respective stage (or stages) of interest. There was also a description of another distance, a line extending horizontally between stages of interest on the CFD and identifying it as the “average lead time” for the requests (workitems) arriving on a specific date. That is, the average time for a request (workitem) to “flow” through (arrive into and depart out of) one or more stages of interest. Lastly, there was a description of a sloped line, as a mean delivery rate of requests (workitems) flowing into or out of a stage (workflow process) depending on viewing the workflow upstream or downstream. Note: for more on the basics on reading a CFD, see this earlier post here.

A Difference Is Not An Average, Right?

It is easy to understand that WIP is simply a “difference” between two counts on the CFD and represents the number of requests (workitems) at a point in time. Similarly, seeing the slope as a simple rise over run calculation of a number of requests per unit of time (a rate over a period of time of interest) is not a complex concept to accept. But, what about the notion of the “average lead time” derived from the CFD? How is it a “difference” between two points in time read from the CFD (ex. calendar dates) can represent an “average” unit of time for a request (workitem) to flow through a stage (workflow process)? A “difference” that represents N numbers summed up and divided by N. Yes, really! But how can this be?

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Does Data Shape Policy Or Does Policy Shape Data? Yes!

“Errors using inadequate data are much less than those using no data at all.” – Charles Babbage, English mathematician, philosopher, inventor, mechanical engineer, invented the first mechanical computer (1791 – 1871)

“I always avoid prophesying before hand, because it is a much better policy to prophesy after the event has already taken place.” – Winston Churchill, British orator, author, and Prime Minister during WWII (1874-1965)

“Policies are many, Principles are few, Policies will change, Principles never do.” – John C. Maxwell, evangelical Christian pastor, speaker, and author of 60+ books primarily about leadership (1947 –   )

“We’re entering a new world in which data may be more important than software.” – Tim O’Reilly, founder of O’Reilly Media, supporter of the free software and open source movements (1954 –  ) 


This question below came up recently on our Agile Denver Kanban SIG (special interest group) LinkedIn discussion list.

“I am working for an organization that has ‘Lab Week’ six times a year. Lab Week encourages innovation by setting aside time for engineers to work on whatever they want. My current conundrum is, what do I do with the work items on my team’s Kanban board, specifically those in To Do or Doing, during Lab Week?

It feels wrong to put them back in the backlog, but keeping them in To Do or Doing will affect lead/cycle time. While this is reality, it is only reality six times a year. In the interest of predictability I think I would want to know what lead/cycle time is without the impact of Lab Week and then factor in Lab Week when we hit a Lab Week.”

I had two immediate responses to this interesting question. First, teams in a number of software development and IT/IS organizations are familiar with the basis for this question, and I’m guessing it comes up in a number of non-IT/IS or non-software development contexts as well. 

Second, there is already an upfront discomfort with the idea of moving workitems back to the “backlog” that have been “pulled” into the ToDo (or Ready, On Deck, etc.) workflow process states. I’m wondering what signal is this discomfort providing to us? I’m guessing the discomfort is even greater with the idea of moving workitems back to the backlog that have made it to some state of Doing in the workflow process. In essence, this would be “resetting the clock” for a number of workitems already considered work in progress for some period of time, and with expectations I’m sure they would be completed within some known SLA.

Just what I like, “real world” questions in particular when they come with some “pain points” clearly identified upfront. As I continued to think about this one more, it felt like a “perfect” opportunity to raise what I feel is a related question and discuss them some together in this post. When discussing metrics as part of measuring and managing flow, the question below is one I raise often with others who are applying the Kanban Method to their workflow processes.

“In your workflow process context: Does Data Shape Your Policy or Does Policy Shape Your Data?”

How might we in the case of a “Lab Week” produce the metrics we want and visualize this information without any “discomfort” of doing post collection adjustments or moving (resetting) workitems back into the backlog? What can we learn from thinking about and responding to this “Lab Week” question that applies beyond the original basis itself in how we develop policies for managing our workflow processes?

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